Hongkong_Disneyland_Reviews.ipynb

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      "source": [
        "# mount your google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "irMa0LQGFWWS",
        "outputId": "dcaa3778-2598-4ed4-ec39-22ab9a767ff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
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        "id": "ONkz-lDxS0R3",
        "outputId": "eaaece23-b76a-46f6-cafc-c194c6e92229"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# import modules\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# plotting\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# need to add nltk imports up here\n",
        "# link: https://www.nltk.org/\n",
        "import nltk\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe = pd.read_csv(\"/content/drive/MyDrive/DS Project/DisneylandReviews.csv\", encoding=\"latin-1\")\n",
        "dataframe.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "2l_gXyK4LBfV",
        "outputId": "45eb5df2-4e1f-46a0-d0f9-1e00aebb1b64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \n",
              "0  If you've ever been to Disneyland anywhere you...  Disneyland_HongKong  \n",
              "1  Its been a while since d last time we visit HK...  Disneyland_HongKong  \n",
              "2  Thanks God it wasn   t too hot or too humid wh...  Disneyland_HongKong  \n",
              "3  HK Disneyland is a great compact park. Unfortu...  Disneyland_HongKong  \n",
              "4  the location is not in the city, took around 1...  Disneyland_HongKong  "
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              "      <td>670772142</td>\n",
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              "      <td>HK Disneyland is a great compact park. Unfortu...</td>\n",
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              "type": "dataframe",
              "variable_name": "dataframe",
              "summary": "{\n  \"name\": \"dataframe\",\n  \"rows\": 42656,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 165709224,\n        \"min\": 1398724,\n        \"max\": 670801367,\n        \"num_unique_values\": 42636,\n        \"samples\": [\n          443940017,\n          289625345,\n          143729284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 112,\n        \"samples\": [\n          \"2016-2\",\n          \"2014-1\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 162,\n        \"samples\": [\n          \"El Salvador\",\n          \"South Sudan\",\n          \"Honduras\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 42632,\n        \"samples\": [\n          \"take a child with you to see their excitement. I had my 4 yr old grandson to see how they enjoyed. there are any number of locations to spend the whole day. time passes to the night until the fireworks ending the show.\",\n          \"If you can get discount tickets do your best to do that. The weekdays are the best to go as they're not as crowded. Get fast passes for the rides that offer it. The workers are really nice. One of the workers on Nemo overheard it was my daughter's 3rd birthday. She asked why she didn't have a birthday button and I said I didn't know about them. When we got off the ride she had one waiting for us and put my daughter's name on it. It was really special.\",\n          \"As always the great joy for the kids makes up for the ridiculously high costs of food and drink! Still a winner for the kids   our 3 year old grandson and our 25 year old daughter and her husband. Fast pass is a must if you don't want to queue for hours. But rides deliver and staff were I have no idea how! The food and drinks are atrociously expensive and the food inside is dreadful. Dinner at Pirates of the Caribbean was 2.5 hours of misery, warm food and dreadful service. How do they get it so wrong. Eat in the village just outside the park so much better! \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Disneyland_HongKong\",\n          \"Disneyland_California\",\n          \"Disneyland_Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "code",
      "source": [
        "print(dataframe[\"Review_Text\"][0])\n",
        "print(dataframe[\"Review_Text\"][1])\n",
        "print(dataframe[\"Review_Text\"][2])"
      ],
      "metadata": {
        "id": "Fzou7xbBLO6r",
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          "base_uri": "https://localhost:8080/"
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        "outputId": "ebdafdc6-10f1-48aa-e11a-7b0ca83178a1"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "If you've ever been to Disneyland anywhere you'll find Disneyland Hong Kong very similar in the layout when you walk into main street! It has a very familiar feel. One of the rides  its a Small World  is absolutely fabulous and worth doing. The day we visited was fairly hot and relatively busy but the queues moved fairly well. \n",
            "Its been a while since d last time we visit HK Disneyland .. Yet, this time we only stay in Tomorrowland .. AKA Marvel land!Now they have Iron Man Experience n d Newly open Ant Man n d Wasp!!Ironman .. Great feature n so Exciting, especially d whole scenery of HK (HK central area to Kowloon)!Antman .. Changed by previous Buzz lightyear! More or less d same, but I'm expecting to have something most!!However, my boys like it!!Space Mountain .. Turns into Star Wars!! This 1 is Great!!!For cast members (staffs) .. Felt bit MINUS point from before!!! Just dun feel like its a Disney brand!! Seems more local like Ocean Park or even worst!!They got no SMILING face, but just wanna u to enter n attraction n leave!!Hello this is supposed to be Happiest Place on Earth brand!! But, just really Dont feel it!!Bakery in Main Street now have more attractive delicacies n Disney theme sweets .. These are Good Points!!Last, they also have Starbucks now inside the theme park!!\n",
            "Thanks God it wasn   t too hot or too humid when I was visiting the park   otherwise it would be a big issue (there is not a lot of shade).I have arrived around 10:30am and left at 6pm. Unfortunately I didn   t last until evening parade, but 8.5 hours was too much for me.There is plenty to do and everyone will find something interesting for themselves to enjoy.It wasn   t extremely busy and the longest time I had to queue for certain attractions was 45 minutes (which is really not that bad).Although I had an amazing time, I felt a bit underwhelmed with choice of rides and attractions. The park itself is quite small (I was really expecting something grand   even the main castle which was closed by the way was quite small).The food options are good, few coffee shops (including Starbucks) and plenty of gift shops. There was no issue with toilets as they are everywhere.All together it was a great day out and I really enjoyed it.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe['Branch'].value_counts()"
      ],
      "metadata": {
        "id": "lDcsOASHMiP7",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e2991d1-a2d8-456d-8dda-96a827d8d21f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Branch\n",
              "Disneyland_California    19406\n",
              "Disneyland_Paris         13630\n",
              "Disneyland_HongKong       9620\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong= dataframe[dataframe['Branch']==\"Disneyland_HongKong\"]\n",
        "df_HongKong.head()"
      ],
      "metadata": {
        "id": "H6O7hp6SNQSK",
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          "height": 206
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      "execution_count": null,
      "outputs": [
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              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \n",
              "0  If you've ever been to Disneyland anywhere you...  Disneyland_HongKong  \n",
              "1  Its been a while since d last time we visit HK...  Disneyland_HongKong  \n",
              "2  Thanks God it wasn   t too hot or too humid wh...  Disneyland_HongKong  \n",
              "3  HK Disneyland is a great compact park. Unfortu...  Disneyland_HongKong  \n",
              "4  the location is not in the city, took around 1...  Disneyland_HongKong  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_HongKong",
              "summary": "{\n  \"name\": \"df_HongKong\",\n  \"rows\": 9620,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 165576748,\n        \"min\": 3530053,\n        \"max\": 670772142,\n        \"num_unique_values\": 9607,\n        \"samples\": [\n          497315135,\n          329990097,\n          125428439\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 108,\n        \"samples\": [\n          \"2013-1\",\n          \"2018-8\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 117,\n        \"samples\": [\n          \"Barbados\",\n          \"Singapore\",\n          \"Northern Mariana Islands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9606,\n        \"samples\": [\n          \"We went to HK Disneyland on the weekend when we would expect really high crowds, but the crowd level was manageable. We arrived there at 1 PM after having lunch in Tsim Sha Sui.At 3 PM, a typhoon warning was issued, it's potentially a storm coming right towards Hong Kong Disneyland, most of outdoor attractions are suddenly closed. We managed to stay there because the weather situation was bright and good enough. 1 hour after, they reopened all rides again and visitors could continue having fun! Woohoo!Some good things we experienced:1. 3 times riding Big Grizzly Mountain due to very short queue line (only 5 mins standing in line to enjoy every ride)2. Incredible fireworks to end our dayLastly, this would be an objective review. If you are a true Disney fan and have been to other Disney parks, please be open minded and realize that you are going to HK Disneyland to have fun and experience the magic of Disney in new place country. This theme park might not be as good big interesting as others, so you gotta visit other Disneyland to experience ultimate treasures and rides!HK Trip period: 18 Sept   22 Sept 2014\",\n          \"It is a place to visit if you r in Hong Kong. Communication to and from is very good as the metro tube MRT goes to Disneyland Activities galore...rides....shows...souvenir shops ...eateries are in abundance inside the park. To use your time efficiently select from the various maps and guides the rides and activities you want to do. It is a whole day program actually... The icing on the cake is the parade at the end of the day....don't miss it.\",\n          \"Was impressed with the cleanliness of the entire compound as it was very dirty when I was there 4 years ago. Kudos to the cleaning team!Very long queues for the rides as well as photo taking with the characters.The app wasn't very user friendly as I wasn't able to find the schedule of shows, etc. Overall kids love the place.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_HongKong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong[\"Rating\"].value_counts()"
      ],
      "metadata": {
        "id": "FDBaIrFSVFBR",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03429dc4-006a-49fb-a1b1-d5f133e370ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rating\n",
              "5    4517\n",
              "4    3230\n",
              "3    1365\n",
              "2     336\n",
              "1     172\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "ax = sns.countplot(x=\"Rating\", data=df_HongKong,\n",
        "                   order = df_HongKong['Rating'].value_counts().index)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation = 90)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BFCcFv_EOoQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "cf49d427-88cf-4130-c3fa-7489b99525ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-f23d8d1bf1ab>:4: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(ax.get_xticklabels(), rotation = 90)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5402BVx4V6hz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# HongKong Disneyland Analysis"
      ],
      "metadata": {
        "id": "fHnyrFLwN6ZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# one line of code! we made a new column and populated it\n",
        "df_HongKong['New_Rating'] = np.where(df_HongKong['Rating'] < 3, 0, 1)\n",
        "\n",
        "df_HongKong.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "e25d776f-dd00-46e4-858d-7c725a516a13",
        "id": "kmVN1ChoN6ZP"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-109-a039e4d16658>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_HongKong['New_Rating'] = np.where(df_HongKong['Rating'] < 3, 0, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \\\n",
              "0  If you've ever been to Disneyland anywhere you...  Disneyland_HongKong   \n",
              "1  Its been a while since d last time we visit HK...  Disneyland_HongKong   \n",
              "2  Thanks God it wasn   t too hot or too humid wh...  Disneyland_HongKong   \n",
              "3  HK Disneyland is a great compact park. Unfortu...  Disneyland_HongKong   \n",
              "4  the location is not in the city, took around 1...  Disneyland_HongKong   \n",
              "\n",
              "   New_Rating  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34121ce2-7773-4938-a482-d67ac90cd88d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review_ID</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Year_Month</th>\n",
              "      <th>Reviewer_Location</th>\n",
              "      <th>Review_Text</th>\n",
              "      <th>Branch</th>\n",
              "      <th>New_Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>670772142</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>Australia</td>\n",
              "      <td>If you've ever been to Disneyland anywhere you...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>670682799</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-5</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>Its been a while since d last time we visit HK...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>670623270</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>Thanks God it wasn   t too hot or too humid wh...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>670607911</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>Australia</td>\n",
              "      <td>HK Disneyland is a great compact park. Unfortu...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>670607296</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>United Kingdom</td>\n",
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              "      <td>Disneyland_HongKong</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_HongKong",
              "summary": "{\n  \"name\": \"df_HongKong\",\n  \"rows\": 9620,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 165576748,\n        \"min\": 3530053,\n        \"max\": 670772142,\n        \"num_unique_values\": 9607,\n        \"samples\": [\n          497315135,\n          329990097,\n          125428439\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 108,\n        \"samples\": [\n          \"2013-1\",\n          \"2018-8\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 117,\n        \"samples\": [\n          \"Barbados\",\n          \"Singapore\",\n          \"Northern Mariana Islands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9606,\n        \"samples\": [\n          \"We went to HK Disneyland on the weekend when we would expect really high crowds, but the crowd level was manageable. We arrived there at 1 PM after having lunch in Tsim Sha Sui.At 3 PM, a typhoon warning was issued, it's potentially a storm coming right towards Hong Kong Disneyland, most of outdoor attractions are suddenly closed. We managed to stay there because the weather situation was bright and good enough. 1 hour after, they reopened all rides again and visitors could continue having fun! Woohoo!Some good things we experienced:1. 3 times riding Big Grizzly Mountain due to very short queue line (only 5 mins standing in line to enjoy every ride)2. Incredible fireworks to end our dayLastly, this would be an objective review. If you are a true Disney fan and have been to other Disney parks, please be open minded and realize that you are going to HK Disneyland to have fun and experience the magic of Disney in new place country. This theme park might not be as good big interesting as others, so you gotta visit other Disneyland to experience ultimate treasures and rides!HK Trip period: 18 Sept   22 Sept 2014\",\n          \"It is a place to visit if you r in Hong Kong. Communication to and from is very good as the metro tube MRT goes to Disneyland Activities galore...rides....shows...souvenir shops ...eateries are in abundance inside the park. To use your time efficiently select from the various maps and guides the rides and activities you want to do. It is a whole day program actually... The icing on the cake is the parade at the end of the day....don't miss it.\",\n          \"Was impressed with the cleanliness of the entire compound as it was very dirty when I was there 4 years ago. Kudos to the cleaning team!Very long queues for the rides as well as photo taking with the characters.The app wasn't very user friendly as I wasn't able to find the schedule of shows, etc. Overall kids love the place.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_HongKong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploring the text data"
      ],
      "metadata": {
        "id": "J94rxaT6N6ZP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###High Rating Reviews Analysis for California DisneyLand"
      ],
      "metadata": {
        "id": "DfzW9tvHN6ZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong_high = df_HongKong[df_HongKong['New_Rating']==1]\n",
        "df_HongKong_high.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a94442a9-e4cc-4739-a2ff-60f24668f637",
        "id": "jdcjOz_yN6ZQ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 9112 entries, 0 to 9618\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   Review_ID          9112 non-null   int64 \n",
            " 1   Rating             9112 non-null   int64 \n",
            " 2   Year_Month         9112 non-null   object\n",
            " 3   Reviewer_Location  9112 non-null   object\n",
            " 4   Review_Text        9112 non-null   object\n",
            " 5   Branch             9112 non-null   object\n",
            " 6   New_Rating         9112 non-null   int64 \n",
            "dtypes: int64(3), object(4)\n",
            "memory usage: 569.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong_high.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b3a18985-a1c7-470a-9ba5-673a246b2f1b",
        "id": "EXwOoxR9N6ZQ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \\\n",
              "0  If you've ever been to Disneyland anywhere you...  Disneyland_HongKong   \n",
              "1  Its been a while since d last time we visit HK...  Disneyland_HongKong   \n",
              "2  Thanks God it wasn   t too hot or too humid wh...  Disneyland_HongKong   \n",
              "3  HK Disneyland is a great compact park. Unfortu...  Disneyland_HongKong   \n",
              "4  the location is not in the city, took around 1...  Disneyland_HongKong   \n",
              "\n",
              "   New_Rating  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  "
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "            'suggestCharts', [key], {});\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_HongKong_high",
              "summary": "{\n  \"name\": \"df_HongKong_high\",\n  \"rows\": 9112,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164747829,\n        \"min\": 3924467,\n        \"max\": 670772142,\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          319699691,\n          419313434,\n          263415598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 107,\n        \"samples\": [\n          \"2013-2\",\n          \"2018-8\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 111,\n        \"samples\": [\n          \"Hungary\",\n          \"Hong Kong\",\n          \"Singapore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          \"Parades, special stage performance and fireworks were good. Pls bring caps as limited shade area. No own food allowed as they check ur bags. Not necessary to purchase meal coupon as all were around hkd100. Better restaurants within list of meal coupons were all closed when we visited on tue and wed  pissed off. They should indicate it on website! Food at clopin nicer than Tahitian. Suggest to get 2 day pass in order to play all rides in park. More suitable for family with children.teenagers or adults travelling alone might find it boring n limited exciting rides\",\n          \"We visited Disneyland HK with our three kids   4,6, and 7. We did 90% of the rides attractions in one day! Yes it was tiring for the kids  rented a stroller for our youngest son but the older two walked the entire time. We were there from when it opened to about 5pm (nobody had the energy to stay any longer). Honestly my kids at this age could careless about the parade. The lines were not bad at all, the longest wait was probably 10 mins. DO get fast passes for Winnie the Pooh and Hyperspace mountain. You'd need to pre register for the Jedi training in advance (by hyperspace mountain and check the show times). Yes the rudeness of the mainlanders is not a myth....we've encountered a few pushes here and there to attempt to cut in front of us. Over all it is def doable to do everything in one day if you're an adult or coming with older children. Another note  my 4 yr old was tall enough to ride the hyperspace mountain but he DID NOT like the ride at all. Started crying in the middle of it (too dark fast). Would advise kids 5 and under to not ride this even if they are tall enough. \",\n          \"A trip to Disney is fun anywhere in the world, but I especially love the HK park. Yes it's the smallest in the world, but that makes it manageable to do in one day. It's easy to get on almost all rides in one day and still fit in plenty of time for shopping. It has my favorite version of Its a Small World in the world and love all the other Disney character references throughout. Eat some character dim sum while you're there and enjoy the fireworks show   also one of my favorites!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_HongKong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_HongKong_high.index)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ff576e9-ff54-4440-f9f1-8556fded5c39",
        "id": "Iz0KfleEN6ZQ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index([   0,    1,    2,    3,    4,    5,    6,    7,    9,   10,\n",
            "       ...\n",
            "       9605, 9606, 9607, 9609, 9611, 9612, 9613, 9614, 9617, 9618],\n",
            "      dtype='int64', length=9112)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong_high = df_HongKong_high.reset_index(drop=True)\n"
      ],
      "metadata": {
        "id": "KuUDKeFkN6ZR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_HongKong_high['Review_Text'][0])\n",
        "print(df_HongKong_high['Review_Text'][1])\n",
        "print(df_HongKong_high['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dd0f32f-274f-477b-be7e-ffc16f914817",
        "id": "O3qzf-56N6ZR"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We've been to Disneyland Hongkong and Tokyo, so far this one is the best. We're looking forward to visit the biggest one in Orlando. 1 day is not enough, it is recommended to stay in Disney Hotel   Resort so you can enjoy the fast track.. save huge amount of time.. if you're not staying there, plan and strategize your visit by getting all the fast track passes from kiosk nearby the attraction; then come back when it's time for your ride. The projection and fireworks show are out of this world!!\n",
            "I went to Disneyland Paris in April 2018 on Easter weekend, I know it says June 2018 but I can't choose a date before then, and I loved it, me and my mum went and as I have autism we managed to get a disability pass for both parks. Disney are excellent with disability access and cater to all types of disabilities, both visible (wheelchair users, etc.) and invisible (autism, etc.), we managed to get on a lot of rides because with the pass you don't queue in the normal queue but the entrance for disabilities. I can only fault one thing when I went we met Spider man and had photos taken but you have to pay for the photos and they are very expensive, even to just pay for one photo. The food wasn't spectacular but it was edible and nice, there is a variety of food outlets so there was plenty of choice. I would loved to go again in Halloween or Christmas but if not I would just love to go again.\n",
            "What a fantastic place, the queues were decent as this is the best time of year to go apparently, we managed to see almost everything, I was a bit disappointed the Haunted Mansion wasn   t open but it can   t be helped! The fireworks were amazing and the parade was so entertaining!A definite must for all Disney lovers!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lowercase"
      ],
      "metadata": {
        "id": "ONvRdDd8N6ZR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong_high['Review_Text'] = df_HongKong_high['Review_Text'].str.lower()\n",
        "df_HongKong_high.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0dac39be-76d0-4514-a8f7-3b82ef3fa3d6",
        "id": "1p5RhOPkN6ZR"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \\\n",
              "0  if you've ever been to disneyland anywhere you...  Disneyland_HongKong   \n",
              "1  its been a while since d last time we visit hk...  Disneyland_HongKong   \n",
              "2  thanks god it wasn   t too hot or too humid wh...  Disneyland_HongKong   \n",
              "3  hk disneyland is a great compact park. unfortu...  Disneyland_HongKong   \n",
              "4  the location is not in the city, took around 1...  Disneyland_HongKong   \n",
              "\n",
              "   New_Rating  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  "
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d32b22ff-66ee-4ee3-bb13-4ad946672846');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_HongKong_high",
              "summary": "{\n  \"name\": \"df_HongKong_high\",\n  \"rows\": 9112,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164747829,\n        \"min\": 3924467,\n        \"max\": 670772142,\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          319699691,\n          419313434,\n          263415598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 107,\n        \"samples\": [\n          \"2013-2\",\n          \"2018-8\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 111,\n        \"samples\": [\n          \"Hungary\",\n          \"Hong Kong\",\n          \"Singapore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          \"parades, special stage performance and fireworks were good. pls bring caps as limited shade area. no own food allowed as they check ur bags. not necessary to purchase meal coupon as all were around hkd100. better restaurants within list of meal coupons were all closed when we visited on tue and wed  pissed off. they should indicate it on website! food at clopin nicer than tahitian. suggest to get 2 day pass in order to play all rides in park. more suitable for family with children.teenagers or adults travelling alone might find it boring n limited exciting rides\",\n          \"we visited disneyland hk with our three kids   4,6, and 7. we did 90% of the rides attractions in one day! yes it was tiring for the kids  rented a stroller for our youngest son but the older two walked the entire time. we were there from when it opened to about 5pm (nobody had the energy to stay any longer). honestly my kids at this age could careless about the parade. the lines were not bad at all, the longest wait was probably 10 mins. do get fast passes for winnie the pooh and hyperspace mountain. you'd need to pre register for the jedi training in advance (by hyperspace mountain and check the show times). yes the rudeness of the mainlanders is not a myth....we've encountered a few pushes here and there to attempt to cut in front of us. over all it is def doable to do everything in one day if you're an adult or coming with older children. another note  my 4 yr old was tall enough to ride the hyperspace mountain but he did not like the ride at all. started crying in the middle of it (too dark fast). would advise kids 5 and under to not ride this even if they are tall enough. \",\n          \"a trip to disney is fun anywhere in the world, but i especially love the hk park. yes it's the smallest in the world, but that makes it manageable to do in one day. it's easy to get on almost all rides in one day and still fit in plenty of time for shopping. it has my favorite version of its a small world in the world and love all the other disney character references throughout. eat some character dim sum while you're there and enjoy the fireworks show   also one of my favorites!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_HongKong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_HongKong_high['Review_Text'][0])\n",
        "print(df_HongKong_high['Review_Text'][1])\n",
        "print(df_HongKong_high['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1664f1e7-4986-4d38-9334-933268d80615",
        "id": "i17L78oZN6ZS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "if you've ever been to disneyland anywhere you'll find disneyland hong kong very similar in the layout when you walk into main street! it has a very familiar feel. one of the rides  its a small world  is absolutely fabulous and worth doing. the day we visited was fairly hot and relatively busy but the queues moved fairly well. \n",
            "its been a while since d last time we visit hk disneyland .. yet, this time we only stay in tomorrowland .. aka marvel land!now they have iron man experience n d newly open ant man n d wasp!!ironman .. great feature n so exciting, especially d whole scenery of hk (hk central area to kowloon)!antman .. changed by previous buzz lightyear! more or less d same, but i'm expecting to have something most!!however, my boys like it!!space mountain .. turns into star wars!! this 1 is great!!!for cast members (staffs) .. felt bit minus point from before!!! just dun feel like its a disney brand!! seems more local like ocean park or even worst!!they got no smiling face, but just wanna u to enter n attraction n leave!!hello this is supposed to be happiest place on earth brand!! but, just really dont feel it!!bakery in main street now have more attractive delicacies n disney theme sweets .. these are good points!!last, they also have starbucks now inside the theme park!!\n",
            "thanks god it wasn   t too hot or too humid when i was visiting the park   otherwise it would be a big issue (there is not a lot of shade).i have arrived around 10:30am and left at 6pm. unfortunately i didn   t last until evening parade, but 8.5 hours was too much for me.there is plenty to do and everyone will find something interesting for themselves to enjoy.it wasn   t extremely busy and the longest time i had to queue for certain attractions was 45 minutes (which is really not that bad).although i had an amazing time, i felt a bit underwhelmed with choice of rides and attractions. the park itself is quite small (i was really expecting something grand   even the main castle which was closed by the way was quite small).the food options are good, few coffee shops (including starbucks) and plenty of gift shops. there was no issue with toilets as they are everywhere.all together it was a great day out and i really enjoyed it.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##Strip funky characters (!,~-@$% etc)"
      ],
      "metadata": {
        "id": "ZSSrpw64N6ZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong_high['Review_Text'] = df_HongKong_high['Review_Text'].str.replace('[^A-z ]','').str.replace(' +',' ').str.strip()\n",
        "df_HongKong_high.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d0235a7f-3a5e-434d-f3c9-d49c41bf20a3",
        "id": "jGKw4akzN6ZS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \\\n",
              "0  if you've ever been to disneyland anywhere you...  Disneyland_HongKong   \n",
              "1  its been a while since d last time we visit hk...  Disneyland_HongKong   \n",
              "2  thanks god it wasn   t too hot or too humid wh...  Disneyland_HongKong   \n",
              "3  hk disneyland is a great compact park. unfortu...  Disneyland_HongKong   \n",
              "4  the location is not in the city, took around 1...  Disneyland_HongKong   \n",
              "\n",
              "   New_Rating  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  "
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Review_ID</th>\n",
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              "      <td>United Kingdom</td>\n",
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              "      display:flex;\n",
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              "      buttonEl.style.display =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_HongKong_high",
              "summary": "{\n  \"name\": \"df_HongKong_high\",\n  \"rows\": 9112,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164747829,\n        \"min\": 3924467,\n        \"max\": 670772142,\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          319699691,\n          419313434,\n          263415598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 107,\n        \"samples\": [\n          \"2013-2\",\n          \"2018-8\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 111,\n        \"samples\": [\n          \"Hungary\",\n          \"Hong Kong\",\n          \"Singapore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          \"parades, special stage performance and fireworks were good. pls bring caps as limited shade area. no own food allowed as they check ur bags. not necessary to purchase meal coupon as all were around hkd100. better restaurants within list of meal coupons were all closed when we visited on tue and wed  pissed off. they should indicate it on website! food at clopin nicer than tahitian. suggest to get 2 day pass in order to play all rides in park. more suitable for family with children.teenagers or adults travelling alone might find it boring n limited exciting rides\",\n          \"we visited disneyland hk with our three kids   4,6, and 7. we did 90% of the rides attractions in one day! yes it was tiring for the kids  rented a stroller for our youngest son but the older two walked the entire time. we were there from when it opened to about 5pm (nobody had the energy to stay any longer). honestly my kids at this age could careless about the parade. the lines were not bad at all, the longest wait was probably 10 mins. do get fast passes for winnie the pooh and hyperspace mountain. you'd need to pre register for the jedi training in advance (by hyperspace mountain and check the show times). yes the rudeness of the mainlanders is not a myth....we've encountered a few pushes here and there to attempt to cut in front of us. over all it is def doable to do everything in one day if you're an adult or coming with older children. another note  my 4 yr old was tall enough to ride the hyperspace mountain but he did not like the ride at all. started crying in the middle of it (too dark fast). would advise kids 5 and under to not ride this even if they are tall enough.\",\n          \"a trip to disney is fun anywhere in the world, but i especially love the hk park. yes it's the smallest in the world, but that makes it manageable to do in one day. it's easy to get on almost all rides in one day and still fit in plenty of time for shopping. it has my favorite version of its a small world in the world and love all the other disney character references throughout. eat some character dim sum while you're there and enjoy the fireworks show   also one of my favorites!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_HongKong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_HongKong_high['Review_Text'][0])\n",
        "print(df_HongKong_high['Review_Text'][1])\n",
        "print(df_HongKong_high['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc6f2560-b336-4447-be7f-a72dd3c12ef7",
        "id": "JJgIfxHfN6ZS"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "if you've ever been to disneyland anywhere you'll find disneyland hong kong very similar in the layout when you walk into main street! it has a very familiar feel. one of the rides  its a small world  is absolutely fabulous and worth doing. the day we visited was fairly hot and relatively busy but the queues moved fairly well.\n",
            "its been a while since d last time we visit hk disneyland .. yet, this time we only stay in tomorrowland .. aka marvel land!now they have iron man experience n d newly open ant man n d wasp!!ironman .. great feature n so exciting, especially d whole scenery of hk (hk central area to kowloon)!antman .. changed by previous buzz lightyear! more or less d same, but i'm expecting to have something most!!however, my boys like it!!space mountain .. turns into star wars!! this 1 is great!!!for cast members (staffs) .. felt bit minus point from before!!! just dun feel like its a disney brand!! seems more local like ocean park or even worst!!they got no smiling face, but just wanna u to enter n attraction n leave!!hello this is supposed to be happiest place on earth brand!! but, just really dont feel it!!bakery in main street now have more attractive delicacies n disney theme sweets .. these are good points!!last, they also have starbucks now inside the theme park!!\n",
            "thanks god it wasn   t too hot or too humid when i was visiting the park   otherwise it would be a big issue (there is not a lot of shade).i have arrived around 10:30am and left at 6pm. unfortunately i didn   t last until evening parade, but 8.5 hours was too much for me.there is plenty to do and everyone will find something interesting for themselves to enjoy.it wasn   t extremely busy and the longest time i had to queue for certain attractions was 45 minutes (which is really not that bad).although i had an amazing time, i felt a bit underwhelmed with choice of rides and attractions. the park itself is quite small (i was really expecting something grand   even the main castle which was closed by the way was quite small).the food options are good, few coffee shops (including starbucks) and plenty of gift shops. there was no issue with toilets as they are everywhere.all together it was a great day out and i really enjoyed it.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Stop words"
      ],
      "metadata": {
        "id": "6hTbo1lhN6ZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "\n",
        "nltk.download('stopwords') # this makes sure these are downloaded if you haven't already!\n",
        "print(stopwords.words('english'))\n",
        "\n",
        "stop = stopwords.words('english') # see why it's important to turn everything lower case? all of your stopwords are!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c0f03a2-28f6-4850-dae5-bd73ccd84275",
        "id": "KEHEVrS4N6ZT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong_high['Review_Text'] = df_HongKong_high['Review_Text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
        "df_HongKong_high.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "41ff4578-bc2f-458d-8c10-672aaecb3e2e",
        "id": "PEsuZ1YZN6ZT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \\\n",
              "0  ever disneyland anywhere find disneyland hong ...  Disneyland_HongKong   \n",
              "1  since last time visit hk disneyland .. yet, ti...  Disneyland_HongKong   \n",
              "2  thanks god hot humid visiting park otherwise w...  Disneyland_HongKong   \n",
              "3  hk disneyland great compact park. unfortunatel...  Disneyland_HongKong   \n",
              "4  location city, took around 1 hour kowlon, kids...  Disneyland_HongKong   \n",
              "\n",
              "   New_Rating  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  "
            ],
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f20ecb2d-2efe-4dc4-938b-1ed824a1d168\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f20ecb2d-2efe-4dc4-938b-1ed824a1d168')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f20ecb2d-2efe-4dc4-938b-1ed824a1d168 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_HongKong_high",
              "summary": "{\n  \"name\": \"df_HongKong_high\",\n  \"rows\": 9112,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164747829,\n        \"min\": 3924467,\n        \"max\": 670772142,\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          319699691,\n          419313434,\n          263415598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 107,\n        \"samples\": [\n          \"2013-2\",\n          \"2018-8\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 111,\n        \"samples\": [\n          \"Hungary\",\n          \"Hong Kong\",\n          \"Singapore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9098,\n        \"samples\": [\n          \"parades, special stage performance fireworks good. pls bring caps limited shade area. food allowed check ur bags. necessary purchase meal coupon around hkd100. better restaurants within list meal coupons closed visited tue wed pissed off. indicate website! food clopin nicer tahitian. suggest get 2 day pass order play rides park. suitable family children.teenagers adults travelling alone might find boring n limited exciting rides\",\n          \"visited disneyland hk three kids 4,6, 7. 90% rides attractions one day! yes tiring kids rented stroller youngest son older two walked entire time. opened 5pm (nobody energy stay longer). honestly kids age could careless parade. lines bad all, longest wait probably 10 mins. get fast passes winnie pooh hyperspace mountain. need pre register jedi training advance (by hyperspace mountain check show times). yes rudeness mainlanders myth....we've encountered pushes attempt cut front us. def doable everything one day adult coming older children. another note 4 yr old tall enough ride hyperspace mountain like ride all. started crying middle (too dark fast). would advise kids 5 ride even tall enough.\",\n          \"trip disney fun anywhere world, especially love hk park. yes smallest world, makes manageable one day. easy get almost rides one day still fit plenty time shopping. favorite version small world world love disney character references throughout. eat character dim sum enjoy fireworks show also one favorites!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_HongKong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_HongKong_high['Review_Text'][0])\n",
        "print(df_HongKong_high['Review_Text'][1])\n",
        "print(df_HongKong_high['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8357793b-cd74-4fbd-de92-56ee145f2220",
        "id": "3R40aic_N6ZU"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ever disneyland anywhere find disneyland hong kong similar layout walk main street! familiar feel. one rides small world absolutely fabulous worth doing. day visited fairly hot relatively busy queues moved fairly well.\n",
            "since last time visit hk disneyland .. yet, time stay tomorrowland .. aka marvel land!now iron man experience n newly open ant man n wasp!!ironman .. great feature n exciting, especially whole scenery hk (hk central area kowloon)!antman .. changed previous buzz lightyear! less same, i'm expecting something most!!however, boys like it!!space mountain .. turns star wars!! 1 great!!!for cast members (staffs) .. felt bit minus point before!!! dun feel like disney brand!! seems local like ocean park even worst!!they got smiling face, wanna u enter n attraction n leave!!hello supposed happiest place earth brand!! but, really dont feel it!!bakery main street attractive delicacies n disney theme sweets .. good points!!last, also starbucks inside theme park!!\n",
            "thanks god hot humid visiting park otherwise would big issue (there lot shade).i arrived around 10:30am left 6pm. unfortunately last evening parade, 8.5 hours much me.there plenty everyone find something interesting enjoy.it extremely busy longest time queue certain attractions 45 minutes (which really bad).although amazing time, felt bit underwhelmed choice rides attractions. park quite small (i really expecting something grand even main castle closed way quite small).the food options good, coffee shops (including starbucks) plenty gift shops. issue toilets everywhere.all together great day really enjoyed it.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Text EDA on the California high rating Data\n"
      ],
      "metadata": {
        "id": "T9OGU9_gN6ZU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Most common words"
      ],
      "metadata": {
        "id": "QH4iLSwMN6ZV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "myTable = df_HongKong_high['Review_Text'].str.split(expand=True).stack().value_counts()\n",
        "myTable[0:20] # too cool!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0b85d3d-d66e-42ec-c9a0-ae844224d9e5",
        "id": "7AKZfoCRN6ZV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disney        6118\n",
              "disneyland    5713\n",
              "park          5202\n",
              "rides         4818\n",
              "day           4663\n",
              "time          3316\n",
              "one           3198\n",
              "hong          2933\n",
              "get           2910\n",
              "kids          2643\n",
              "go            2634\n",
              "great         2526\n",
              "food          2493\n",
              "place         2433\n",
              "good          2274\n",
              "kong          2246\n",
              "visit         2241\n",
              "really        2224\n",
              "would         2147\n",
              "went          2114\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df_HongKong_high['Review_Text'].str.split(expand=True).stack().value_counts()\n",
        "x = pd.DataFrame(x)\n",
        "# reset the index\n",
        "x.reset_index(inplace=True)\n",
        "# rename the columns\n",
        "x.rename(columns={x.columns[0]:'word', x.columns[1]:'frequency'}, inplace=True)\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "93ac9410-3b02-47d8-a2e9-c00605360d6c",
        "id": "P54DWEZVN6ZV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         word  frequency\n",
              "0      disney       6118\n",
              "1  disneyland       5713\n",
              "2        park       5202\n",
              "3       rides       4818\n",
              "4         day       4663"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "      <td>disney</td>\n",
              "      <td>6118</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>disneyland</td>\n",
              "      <td>5713</td>\n",
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              "      <th>2</th>\n",
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              "      <td>5202</td>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8ceaed50-b5a7-4d02-a2be-a4e2b1c8a855 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8ceaed50-b5a7-4d02-a2be-a4e2b1c8a855');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-567d128c-17e1-4242-bfb0-60aac4e59a7e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-567d128c-17e1-4242-bfb0-60aac4e59a7e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-567d128c-17e1-4242-bfb0-60aac4e59a7e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 38726,\n  \"fields\": [\n    {\n      \"column\": \"word\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 38726,\n        \"samples\": [\n          \"bend\",\n          \"across...\",\n          \"apart\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103,\n        \"min\": 1,\n        \"max\": 6118,\n        \"num_unique_values\": 513,\n        \"samples\": [\n          218,\n          17,\n          74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = x[0:20] # wanna play? change the 20 to 10 or 30 and see what happens...\n",
        "            # you need to run the previous cell first\n",
        "x.plot(x='word', y='frequency')\n",
        "plt.xticks(np.arange(len(x)), x['word'], rotation=90)\n",
        "plt.suptitle('Top Words in DisneyLand HongKong high rated Review')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "6c11045f-60e1-45d3-9903-63cce7474c40",
        "id": "YMlMr_10N6ZW"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Word Cloud"
      ],
      "metadata": {
        "id": "gg1EkKxIN6ZW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud\n",
        "\n",
        "# Generate a word cloud image\n",
        "wordcloud = WordCloud().generate(' '.join(df_HongKong_high['Review_Text']))\n",
        "\n",
        "# # Display the generated image:\n",
        "# # the matplotlib way:\n",
        "# import matplotlib.pyplot as plt\n",
        "# plt.imshow(wordcloud, interpolation='bilinear')\n",
        "# plt.axis(\"off\")\n",
        "\n",
        "# lower max_font_size\n",
        "wordcloud = WordCloud(max_font_size=40).generate(' '.join(df_HongKong_high['Review_Text']))\n",
        "plt.figure()\n",
        "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "02b59874-7cbb-41c5-e907-a81041da69e6",
        "id": "L0CtMXRON6ZW"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Tokenizer"
      ],
      "metadata": {
        "id": "Xb5VyQ1lN6ZW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_HongKong_high['Review_Text'] = df_HongKong_high.apply(lambda row: nltk.word_tokenize(row['Review_Text']), axis=1)\n",
        "df_HongKong_high.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3580a464-78c8-4f8f-ac45-0a5e9d143fa3",
        "id": "K9GiLQPxN6ZX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month     Reviewer_Location  \\\n",
              "0  670772142       4     2019-4             Australia   \n",
              "1  670682799       4     2019-5           Philippines   \n",
              "2  670623270       4     2019-4  United Arab Emirates   \n",
              "3  670607911       4     2019-4             Australia   \n",
              "4  670607296       4     2019-4        United Kingdom   \n",
              "\n",
              "                                         Review_Text               Branch  \\\n",
              "0  [ever, disneyland, anywhere, find, disneyland,...  Disneyland_HongKong   \n",
              "1  [since, last, time, visit, hk, disneyland, ..,...  Disneyland_HongKong   \n",
              "2  [thanks, god, hot, humid, visiting, park, othe...  Disneyland_HongKong   \n",
              "3  [hk, disneyland, great, compact, park, ., unfo...  Disneyland_HongKong   \n",
              "4  [location, city, ,, took, around, 1, hour, kow...  Disneyland_HongKong   \n",
              "\n",
              "   New_Rating  \n",
              "0           1  \n",
              "1           1  \n",
              "2           1  \n",
              "3           1  \n",
              "4           1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d6c2b3cf-057a-4703-8805-7dab7b9c9f87\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review_ID</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Year_Month</th>\n",
              "      <th>Reviewer_Location</th>\n",
              "      <th>Review_Text</th>\n",
              "      <th>Branch</th>\n",
              "      <th>New_Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>670772142</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>Australia</td>\n",
              "      <td>[ever, disneyland, anywhere, find, disneyland,...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>670682799</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-5</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>[since, last, time, visit, hk, disneyland, ..,...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>670623270</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>[thanks, god, hot, humid, visiting, park, othe...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>670607911</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>Australia</td>\n",
              "      <td>[hk, disneyland, great, compact, park, ., unfo...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>670607296</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>[location, city, ,, took, around, 1, hour, kow...</td>\n",
              "      <td>Disneyland_HongKong</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d6c2b3cf-057a-4703-8805-7dab7b9c9f87')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d6c2b3cf-057a-4703-8805-7dab7b9c9f87 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d6c2b3cf-057a-4703-8805-7dab7b9c9f87');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6aa639cc-5b3f-4fe1-99e4-5ab87d64ac10\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6aa639cc-5b3f-4fe1-99e4-5ab87d64ac10')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6aa639cc-5b3f-4fe1-99e4-5ab87d64ac10 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_HongKong_high",
              "summary": "{\n  \"name\": \"df_HongKong_high\",\n  \"rows\": 9112,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164747829,\n        \"min\": 3924467,\n        \"max\": 670772142,\n        \"num_unique_values\": 9099,\n        \"samples\": [\n          319699691,\n          419313434,\n          263415598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 107,\n        \"samples\": [\n          \"2013-2\",\n          \"2018-8\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 111,\n        \"samples\": [\n          \"Hungary\",\n          \"Hong Kong\",\n          \"Singapore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_HongKong\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_HongKong_high['Review_Text'][0])\n",
        "print(df_HongKong_high['Review_Text'][1])\n",
        "print(df_HongKong_high['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d766b32-0466-4a6f-b893-0828d9daed74",
        "id": "HqgMsifYN6ZX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ever', 'disneyland', 'anywhere', 'find', 'disneyland', 'hong', 'kong', 'similar', 'layout', 'walk', 'main', 'street', '!', 'familiar', 'feel', '.', 'one', 'rides', 'small', 'world', 'absolutely', 'fabulous', 'worth', 'doing', '.', 'day', 'visited', 'fairly', 'hot', 'relatively', 'busy', 'queues', 'moved', 'fairly', 'well', '.']\n",
            "['since', 'last', 'time', 'visit', 'hk', 'disneyland', '..', 'yet', ',', 'time', 'stay', 'tomorrowland', '..', 'aka', 'marvel', 'land', '!', 'now', 'iron', 'man', 'experience', 'n', 'newly', 'open', 'ant', 'man', 'n', 'wasp', '!', '!', 'ironman', '..', 'great', 'feature', 'n', 'exciting', ',', 'especially', 'whole', 'scenery', 'hk', '(', 'hk', 'central', 'area', 'kowloon', ')', '!', 'antman', '..', 'changed', 'previous', 'buzz', 'lightyear', '!', 'less', 'same', ',', 'i', \"'m\", 'expecting', 'something', 'most', '!', '!', 'however', ',', 'boys', 'like', 'it', '!', '!', 'space', 'mountain', '..', 'turns', 'star', 'wars', '!', '!', '1', 'great', '!', '!', '!', 'for', 'cast', 'members', '(', 'staffs', ')', '..', 'felt', 'bit', 'minus', 'point', 'before', '!', '!', '!', 'dun', 'feel', 'like', 'disney', 'brand', '!', '!', 'seems', 'local', 'like', 'ocean', 'park', 'even', 'worst', '!', '!', 'they', 'got', 'smiling', 'face', ',', 'wan', 'na', 'u', 'enter', 'n', 'attraction', 'n', 'leave', '!', '!', 'hello', 'supposed', 'happiest', 'place', 'earth', 'brand', '!', '!', 'but', ',', 'really', 'dont', 'feel', 'it', '!', '!', 'bakery', 'main', 'street', 'attractive', 'delicacies', 'n', 'disney', 'theme', 'sweets', '..', 'good', 'points', '!', '!', 'last', ',', 'also', 'starbucks', 'inside', 'theme', 'park', '!', '!']\n",
            "['thanks', 'god', 'hot', 'humid', 'visiting', 'park', 'otherwise', 'would', 'big', 'issue', '(', 'there', 'lot', 'shade', ')', '.i', 'arrived', 'around', '10:30am', 'left', '6pm', '.', 'unfortunately', 'last', 'evening', 'parade', ',', '8.5', 'hours', 'much', 'me.there', 'plenty', 'everyone', 'find', 'something', 'interesting', 'enjoy.it', 'extremely', 'busy', 'longest', 'time', 'queue', 'certain', 'attractions', '45', 'minutes', '(', 'which', 'really', 'bad', ')', '.although', 'amazing', 'time', ',', 'felt', 'bit', 'underwhelmed', 'choice', 'rides', 'attractions', '.', 'park', 'quite', 'small', '(', 'i', 'really', 'expecting', 'something', 'grand', 'even', 'main', 'castle', 'closed', 'way', 'quite', 'small', ')', '.the', 'food', 'options', 'good', ',', 'coffee', 'shops', '(', 'including', 'starbucks', ')', 'plenty', 'gift', 'shops', '.', 'issue', 'toilets', 'everywhere.all', 'together', 'great', 'day', 'really', 'enjoyed', 'it', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lexicon Normalization"
      ],
      "metadata": {
        "id": "8sfFVJUlN6ZX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Stemming"
      ],
      "metadata": {
        "id": "T3YjCX3jN6ZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "\n",
        "# this is the function we will use\n",
        "ps = PorterStemmer()\n",
        "\n",
        "filtered_sent = df_HongKong_high['Review_Text'][1]\n",
        "\n",
        "stemmed_words=[]\n",
        "for w in filtered_sent:\n",
        "    stemmed_words.append(ps.stem(w))\n",
        "\n",
        "print(\"Filtered Sentence:\",filtered_sent)\n",
        "print(\"Stemmed Sentence:\",stemmed_words)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ced564e-3a32-4871-a9fa-ddd06fd97e31",
        "id": "gp2cDheSN6ZX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtered Sentence: ['since', 'last', 'time', 'visit', 'hk', 'disneyland', '..', 'yet', ',', 'time', 'stay', 'tomorrowland', '..', 'aka', 'marvel', 'land', '!', 'now', 'iron', 'man', 'experience', 'n', 'newly', 'open', 'ant', 'man', 'n', 'wasp', '!', '!', 'ironman', '..', 'great', 'feature', 'n', 'exciting', ',', 'especially', 'whole', 'scenery', 'hk', '(', 'hk', 'central', 'area', 'kowloon', ')', '!', 'antman', '..', 'changed', 'previous', 'buzz', 'lightyear', '!', 'less', 'same', ',', 'i', \"'m\", 'expecting', 'something', 'most', '!', '!', 'however', ',', 'boys', 'like', 'it', '!', '!', 'space', 'mountain', '..', 'turns', 'star', 'wars', '!', '!', '1', 'great', '!', '!', '!', 'for', 'cast', 'members', '(', 'staffs', ')', '..', 'felt', 'bit', 'minus', 'point', 'before', '!', '!', '!', 'dun', 'feel', 'like', 'disney', 'brand', '!', '!', 'seems', 'local', 'like', 'ocean', 'park', 'even', 'worst', '!', '!', 'they', 'got', 'smiling', 'face', ',', 'wan', 'na', 'u', 'enter', 'n', 'attraction', 'n', 'leave', '!', '!', 'hello', 'supposed', 'happiest', 'place', 'earth', 'brand', '!', '!', 'but', ',', 'really', 'dont', 'feel', 'it', '!', '!', 'bakery', 'main', 'street', 'attractive', 'delicacies', 'n', 'disney', 'theme', 'sweets', '..', 'good', 'points', '!', '!', 'last', ',', 'also', 'starbucks', 'inside', 'theme', 'park', '!', '!']\n",
            "Stemmed Sentence: ['sinc', 'last', 'time', 'visit', 'hk', 'disneyland', '..', 'yet', ',', 'time', 'stay', 'tomorrowland', '..', 'aka', 'marvel', 'land', '!', 'now', 'iron', 'man', 'experi', 'n', 'newli', 'open', 'ant', 'man', 'n', 'wasp', '!', '!', 'ironman', '..', 'great', 'featur', 'n', 'excit', ',', 'especi', 'whole', 'sceneri', 'hk', '(', 'hk', 'central', 'area', 'kowloon', ')', '!', 'antman', '..', 'chang', 'previou', 'buzz', 'lightyear', '!', 'less', 'same', ',', 'i', \"'m\", 'expect', 'someth', 'most', '!', '!', 'howev', ',', 'boy', 'like', 'it', '!', '!', 'space', 'mountain', '..', 'turn', 'star', 'war', '!', '!', '1', 'great', '!', '!', '!', 'for', 'cast', 'member', '(', 'staff', ')', '..', 'felt', 'bit', 'minu', 'point', 'befor', '!', '!', '!', 'dun', 'feel', 'like', 'disney', 'brand', '!', '!', 'seem', 'local', 'like', 'ocean', 'park', 'even', 'worst', '!', '!', 'they', 'got', 'smile', 'face', ',', 'wan', 'na', 'u', 'enter', 'n', 'attract', 'n', 'leav', '!', '!', 'hello', 'suppos', 'happiest', 'place', 'earth', 'brand', '!', '!', 'but', ',', 'realli', 'dont', 'feel', 'it', '!', '!', 'bakeri', 'main', 'street', 'attract', 'delicaci', 'n', 'disney', 'theme', 'sweet', '..', 'good', 'point', '!', '!', 'last', ',', 'also', 'starbuck', 'insid', 'theme', 'park', '!', '!']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_HongKong_high['Stemmed'] = df_HongKong_high['Review_Text'].apply(lambda x: [ps.stem(y) for y in x]) # Stem every word."
      ],
      "metadata": {
        "id": "serBtzt-N6ZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check your work!\n",
        "print(df_HongKong_high['Review_Text'][0]) # reported\n",
        "print(df_HongKong_high['Stemmed'][0]) # report\n",
        "\n",
        "print(df_HongKong_high['Review_Text'][10]) # note the difference between inches, reported, covered\n",
        "print(df_HongKong_high['Stemmed'][10]) # inch, report, cover"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3df53640-6167-415b-a592-27130bfd06c3",
        "id": "6dzakceGN6ZY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ever', 'disneyland', 'anywhere', 'find', 'disneyland', 'hong', 'kong', 'similar', 'layout', 'walk', 'main', 'street', '!', 'familiar', 'feel', '.', 'one', 'rides', 'small', 'world', 'absolutely', 'fabulous', 'worth', 'doing', '.', 'day', 'visited', 'fairly', 'hot', 'relatively', 'busy', 'queues', 'moved', 'fairly', 'well', '.']\n",
            "['ever', 'disneyland', 'anywher', 'find', 'disneyland', 'hong', 'kong', 'similar', 'layout', 'walk', 'main', 'street', '!', 'familiar', 'feel', '.', 'one', 'ride', 'small', 'world', 'absolut', 'fabul', 'worth', 'do', '.', 'day', 'visit', 'fairli', 'hot', 'rel', 'busi', 'queue', 'move', 'fairli', 'well', '.']\n",
            "['spent', 'day', 'grown', 'kids', 'admit', 'great', 'time', '.', 'seems', 'kids', 'never', 'tire', 'disney', '.', 'many', 'rides', 'states', 'disney', 'parks', 'great', 'ones', 'less', '.', 'haunted', 'house', ',', 'catch', 'lion', 'king', 'show', ',', 'space', 'mountain', ',', 'iron', 'man', 'experience', 'list', 'few', '.', 'lots', 'shops', 'spend', 'money', 'course', '.', 'kids', 'love', 'day', '!']\n",
            "['spent', 'day', 'grown', 'kid', 'admit', 'great', 'time', '.', 'seem', 'kid', 'never', 'tire', 'disney', '.', 'mani', 'ride', 'state', 'disney', 'park', 'great', 'one', 'less', '.', 'haunt', 'hous', ',', 'catch', 'lion', 'king', 'show', ',', 'space', 'mountain', ',', 'iron', 'man', 'experi', 'list', 'few', '.', 'lot', 'shop', 'spend', 'money', 'cours', '.', 'kid', 'love', 'day', '!']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tDPE6xVYN6ZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Low Rating Reviews Analysis for California DisneyLand"
      ],
      "metadata": {
        "id": "BY_lz-P-N6ZY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_Paris_low = df_Paris[df_Paris['New_Rating']==0]\n",
        "df_Paris_low.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09ea76ae-3b38-4a49-9c44-da144519c47f",
        "id": "MstYr7IBN6ZY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1872 entries, 29034 to 42647\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   Review_ID          1872 non-null   int64 \n",
            " 1   Rating             1872 non-null   int64 \n",
            " 2   Year_Month         1872 non-null   object\n",
            " 3   Reviewer_Location  1872 non-null   object\n",
            " 4   Review_Text        1872 non-null   object\n",
            " 5   Branch             1872 non-null   object\n",
            " 6   New_Rating         1872 non-null   int64 \n",
            "dtypes: int64(3), object(4)\n",
            "memory usage: 117.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_Paris_low.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4fa5e19b-a02e-4ee7-ae6f-7b83fd1882e8",
        "id": "821kdE7aN6ZY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Review_ID  Rating Year_Month Reviewer_Location  \\\n",
              "29034  670216406       2     2019-4            Greece   \n",
              "29043  669337300       1     2019-4            Greece   \n",
              "29047  669121201       2     2019-4       Switzerland   \n",
              "29053  668207242       1     2019-4    United Kingdom   \n",
              "29058  668123744       2     2019-4    United Kingdom   \n",
              "\n",
              "                                             Review_Text            Branch  \\\n",
              "29034  We booked tickets on line for Disneyland, this...  Disneyland_Paris   \n",
              "29043  Why they selling fastpass tickets without have...  Disneyland_Paris   \n",
              "29047  We went to Disneyland Paris during the Easter ...  Disneyland_Paris   \n",
              "29053  Its a shame this park carry the name Disney......  Disneyland_Paris   \n",
              "29058  I know Disneyland is a money machine but hadn ...  Disneyland_Paris   \n",
              "\n",
              "       New_Rating  \n",
              "29034           0  \n",
              "29043           0  \n",
              "29047           0  \n",
              "29053           0  \n",
              "29058           0  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_Paris_low",
              "summary": "{\n  \"name\": \"df_Paris_low\",\n  \"rows\": 1872,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 180684140,\n        \"min\": 2216235,\n        \"max\": 670216406,\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          114819291,\n          188760405,\n          150184207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 105,\n        \"samples\": [\n          \"2017-1\",\n          \"2013-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Canada\",\n          \"Greece\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          \"If you're in France, this is the absolute last thing you should be wanting to do. Save your money and go to real castles, even if you have children.When I visited, a very large part of the park was closed (including rides) for 'renovations for your future enjoyment', which of course was not advertised when purchasing our tickets. Nor was there any signage in the park about closures, which meant that you would walk down numerous streets only to come to dead ends and have to backtrack constantly. It was extremely frustrating.The rides were minimal   only 3 'big thrill' rides and not that many that I saw for children, either, there were absolutely no carnival games where you could play to win prizes, not a single Disney character in sight, not enough restaurants for the mass of people (even in non peak eating times). As another reviewer so accurately described it, this was simply a giant Disney themed shopping mall. The shops didn't even have much variety between each other, either   they all carried essentially the same merchandise with only a slight variation and the consumer panic in every shop was sickening.Our assessment of the entire experience was very poor. We expected more from Disney. If you absolutely must come here, don't bother unless your children are six years old or younger and you're interested in losing a ton of money on naff Disney merchandise and being nagged all day by your children to buy things for them, because that's all your entertainment options.\",\n          \"Disney is supposed to make your dreams come true. Sadly the magic disappeared on day one when we arrived. The accommodation was fine, beds nice and comfy, a good size and clean. No kettle or drink making facilities in the room. We had to hire a kettle    25, which came with nothing !! Oh of course you have to buy a tea package to go with it.    9, what does that have in it? Four paper cups, PAPER CUPS ! Four tea bags, UHT milk in pots.Sugar and four biscuits. No thanks . Bought cups from store and got t bags from restaurants. Needed a wheelchair, no mobility scooters! Okay, you need to walk to the park for 20 mins which is quicker or catch the bus. Done ! In park you have hire scrappy wheelchair which didn't open properly or steer well.    125:00 deposit and    45 a day as well as the evening before use and morning of return even when we were not in the park.    90 to have it sitting in the corridor. The hotel would not return it for us. Bad organisation skills came to mind.....No facilites for a quick meal for the kids. Restaurant     27 per head all you eat buffett. No good for kids and diabetics on portion control. Bar snacks   all we offer is a slice of pizza and a pot of pasta heated in the microwave. So five portions and drinks   54 : (Breakfast buffet. Nothing for diabetics at all. Sugar cereals, fruit in syrup, white bread, cheeses, croissants......oh dear!On site queues everywhere. Expensive food and poor quality. Broken rides, rides closed and kids cold and disappointed. We had to queue to see Mickey, Whinnie the pooh and no other characters around. Very saddened by this. The best bits........ rides that were open, the parade was frequent and really exciting, Christmas lights everywhere made it look so magical, fireworks were heavenly but on too late so my children missed them. The best thing of all was the Character breakfast, it made their trip. Lots of food and lots of characters to visit them.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_Paris_low.index)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7625ff4d-a058-4153-a008-b8e570343430",
        "id": "cOb67NHXN6ZZ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index([29034, 29043, 29047, 29053, 29058, 29064, 29065, 29067, 29074, 29078,\n",
            "       ...\n",
            "       42629, 42630, 42633, 42635, 42636, 42639, 42640, 42641, 42643, 42647],\n",
            "      dtype='int64', length=1872)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_Paris_low = df_Paris_low.reset_index(drop=True)\n"
      ],
      "metadata": {
        "id": "TGmqvGjcN6ZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_Paris_low['Review_Text'][0])\n",
        "print(df_Paris_low['Review_Text'][1])\n",
        "print(df_Paris_low['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec485ba8-2d4b-475e-ab30-11b89525f8d8",
        "id": "xSrEX6ioN6ZZ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We booked tickets on line for Disneyland, this is something I recommend, the lines were already long when we got there, so booking your tickets in advance does help.I was advised to buy a fast passes, but i decided not to, because it was not clear to me on the website if this also counted as an entrance ticket, meaning; a total price of 90 euro with entrancevinstead or 74 or 68 or an extra 90 euro's, depending on age of child.But even if we would have, the lines were long there as well, and also, what kind of system is this? People that do not have that much to spend will be held back, because people with money obviously can skip the lines...absolutely ridiculous.Getting something to eat was horrible, again lines everywhere...just to eat someting right...And don't get me started on the staff, trying to figure out the amount of 3 icecreams with a small casio computer, and not being able to, or very rude staff. Only 2 ladies were friendly, have to be honest. All in all, very disappointing, long waiting lines, not being able to sit somewhere like a human being, and bad service.Our kids were so disappointed that they wanted to leave.We even heard people saying: 'Never again'.Absolutely not worth the money.\n",
            "Why they selling fastpass tickets without have buy before the entrance tickets???  Any answer ???  Shame on you!!!   We came here from Greece until the entrance!!!  Avoid this place!!!                                                                                                                \n",
            "We went to Disneyland Paris during the Easter break with family members who had come over from the States. As we had all experience Disneyland in Anaheim and Disneyworld in Orlando, perhaps our expectations were just too high. First off, the moving sidewalks from the parking lot to the park entrance were broken. Not a great first impression. Furthermore, there was a lot of gardening work ongoing when we arrived. Isn't Disney supposed to be magic, with everything looking in top shape and perfect? The security line moved, but was the first bottleneck. The second major bottleneck was to actually enter the park. As we had bought tickets online, we thought we would be able to enter the park fairly easily. However, there was pretty much mass chaos at the ticket check point, with many people jumping the queue and others just standing around. We eventually made it through and made our way to Fantasyland.We managed to ride a few rides, but when it came time to get on the Teacups, they had closed the ride. Not for the day, but for about 45 minutes to disperse the line. This caused many people just to loiter around the entrance, waiting for the ride to open again. Not great. We did manage to eat lunch  early  enough that we didn't have to wait a long time. However, the food that we got was not what we ordered. None of us had the wherewithal to go back and try to fix the order. so we just ate what we got. When it came time for ice cream, it took about 45 minutes just to get to the front of the line, and then another 10 minutes to process the order. This was because the staff at the counter were poorly trained, and only one person knew how to use the register, and others seemed like trainees. There were plenty of other foodcarts around the park that were empty. Not enough staff at the park to operate these. There were also plenty of rides (like Autopia and Dumbo) that were closed for renovation. Disneyland Paris does not carry the iconic felt mouse ears. We definitely won't be going back, and I can't recommend Disneyland Paris to anyone who is looking for a true Disney experience.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lowercase"
      ],
      "metadata": {
        "id": "aKyYvj_PN6ZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_Paris_low['Review_Text'] = df_Paris_low['Review_Text'].str.lower()\n",
        "df_Paris_low.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3251249f-cd43-484e-9b12-ade36e9f8bc9",
        "id": "u9msOSdGN6ZZ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month Reviewer_Location  \\\n",
              "0  670216406       2     2019-4            Greece   \n",
              "1  669337300       1     2019-4            Greece   \n",
              "2  669121201       2     2019-4       Switzerland   \n",
              "3  668207242       1     2019-4    United Kingdom   \n",
              "4  668123744       2     2019-4    United Kingdom   \n",
              "\n",
              "                                         Review_Text            Branch  \\\n",
              "0  we booked tickets on line for disneyland, this...  Disneyland_Paris   \n",
              "1  why they selling fastpass tickets without have...  Disneyland_Paris   \n",
              "2  we went to disneyland paris during the easter ...  Disneyland_Paris   \n",
              "3  its a shame this park carry the name disney......  Disneyland_Paris   \n",
              "4  i know disneyland is a money machine but hadn ...  Disneyland_Paris   \n",
              "\n",
              "   New_Rating  \n",
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              "2           0  \n",
              "3           0  \n",
              "4           0  "
            ],
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              "type": "dataframe",
              "variable_name": "df_Paris_low",
              "summary": "{\n  \"name\": \"df_Paris_low\",\n  \"rows\": 1872,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 180684140,\n        \"min\": 2216235,\n        \"max\": 670216406,\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          114819291,\n          188760405,\n          150184207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 105,\n        \"samples\": [\n          \"2017-1\",\n          \"2013-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Canada\",\n          \"Greece\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          \"if you're in france, this is the absolute last thing you should be wanting to do. save your money and go to real castles, even if you have children.when i visited, a very large part of the park was closed (including rides) for 'renovations for your future enjoyment', which of course was not advertised when purchasing our tickets. nor was there any signage in the park about closures, which meant that you would walk down numerous streets only to come to dead ends and have to backtrack constantly. it was extremely frustrating.the rides were minimal   only 3 'big thrill' rides and not that many that i saw for children, either, there were absolutely no carnival games where you could play to win prizes, not a single disney character in sight, not enough restaurants for the mass of people (even in non peak eating times). as another reviewer so accurately described it, this was simply a giant disney themed shopping mall. the shops didn't even have much variety between each other, either   they all carried essentially the same merchandise with only a slight variation and the consumer panic in every shop was sickening.our assessment of the entire experience was very poor. we expected more from disney. if you absolutely must come here, don't bother unless your children are six years old or younger and you're interested in losing a ton of money on naff disney merchandise and being nagged all day by your children to buy things for them, because that's all your entertainment options.\",\n          \"disney is supposed to make your dreams come true. sadly the magic disappeared on day one when we arrived. the accommodation was fine, beds nice and comfy, a good size and clean. no kettle or drink making facilities in the room. we had to hire a kettle    25, which came with nothing !! oh of course you have to buy a tea package to go with it.    9, what does that have in it? four paper cups, paper cups ! four tea bags, uht milk in pots.sugar and four biscuits. no thanks . bought cups from store and got t bags from restaurants. needed a wheelchair, no mobility scooters! okay, you need to walk to the park for 20 mins which is quicker or catch the bus. done ! in park you have hire scrappy wheelchair which didn't open properly or steer well.    125:00 deposit and    45 a day as well as the evening before use and morning of return even when we were not in the park.    90 to have it sitting in the corridor. the hotel would not return it for us. bad organisation skills came to mind.....no facilites for a quick meal for the kids. restaurant     27 per head all you eat buffett. no good for kids and diabetics on portion control. bar snacks   all we offer is a slice of pizza and a pot of pasta heated in the microwave. so five portions and drinks   54 : (breakfast buffet. nothing for diabetics at all. sugar cereals, fruit in syrup, white bread, cheeses, croissants......oh dear!on site queues everywhere. expensive food and poor quality. broken rides, rides closed and kids cold and disappointed. we had to queue to see mickey, whinnie the pooh and no other characters around. very saddened by this. the best bits........ rides that were open, the parade was frequent and really exciting, christmas lights everywhere made it look so magical, fireworks were heavenly but on too late so my children missed them. the best thing of all was the character breakfast, it made their trip. lots of food and lots of characters to visit them.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_Paris_low['Review_Text'][0])\n",
        "print(df_Paris_low['Review_Text'][1])\n",
        "print(df_Paris_low['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a48c7de9-c74b-4d59-92f3-91ef376122eb",
        "id": "P0ao_F_QN6Za"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "we booked tickets on line for disneyland, this is something i recommend, the lines were already long when we got there, so booking your tickets in advance does help.i was advised to buy a fast passes, but i decided not to, because it was not clear to me on the website if this also counted as an entrance ticket, meaning; a total price of 90 euro with entrancevinstead or 74 or 68 or an extra 90 euro's, depending on age of child.but even if we would have, the lines were long there as well, and also, what kind of system is this? people that do not have that much to spend will be held back, because people with money obviously can skip the lines...absolutely ridiculous.getting something to eat was horrible, again lines everywhere...just to eat someting right...and don't get me started on the staff, trying to figure out the amount of 3 icecreams with a small casio computer, and not being able to, or very rude staff. only 2 ladies were friendly, have to be honest. all in all, very disappointing, long waiting lines, not being able to sit somewhere like a human being, and bad service.our kids were so disappointed that they wanted to leave.we even heard people saying: 'never again'.absolutely not worth the money.\n",
            "why they selling fastpass tickets without have buy before the entrance tickets???  any answer ???  shame on you!!!   we came here from greece until the entrance!!!  avoid this place!!!                                                                                                                \n",
            "we went to disneyland paris during the easter break with family members who had come over from the states. as we had all experience disneyland in anaheim and disneyworld in orlando, perhaps our expectations were just too high. first off, the moving sidewalks from the parking lot to the park entrance were broken. not a great first impression. furthermore, there was a lot of gardening work ongoing when we arrived. isn't disney supposed to be magic, with everything looking in top shape and perfect? the security line moved, but was the first bottleneck. the second major bottleneck was to actually enter the park. as we had bought tickets online, we thought we would be able to enter the park fairly easily. however, there was pretty much mass chaos at the ticket check point, with many people jumping the queue and others just standing around. we eventually made it through and made our way to fantasyland.we managed to ride a few rides, but when it came time to get on the teacups, they had closed the ride. not for the day, but for about 45 minutes to disperse the line. this caused many people just to loiter around the entrance, waiting for the ride to open again. not great. we did manage to eat lunch  early  enough that we didn't have to wait a long time. however, the food that we got was not what we ordered. none of us had the wherewithal to go back and try to fix the order. so we just ate what we got. when it came time for ice cream, it took about 45 minutes just to get to the front of the line, and then another 10 minutes to process the order. this was because the staff at the counter were poorly trained, and only one person knew how to use the register, and others seemed like trainees. there were plenty of other foodcarts around the park that were empty. not enough staff at the park to operate these. there were also plenty of rides (like autopia and dumbo) that were closed for renovation. disneyland paris does not carry the iconic felt mouse ears. we definitely won't be going back, and i can't recommend disneyland paris to anyone who is looking for a true disney experience.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##Strip funky characters (!,~-@$% etc)"
      ],
      "metadata": {
        "id": "SWJEHqtVN6Za"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_Paris_low['Review_Text'] = df_Paris_low['Review_Text'].str.replace('[^A-z ]','').str.replace(' +',' ').str.strip()\n",
        "df_Paris_low.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0b5a148e-634f-49a9-b54f-a6d219fd15c5",
        "id": "8jYH_w3yN6Za"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month Reviewer_Location  \\\n",
              "0  670216406       2     2019-4            Greece   \n",
              "1  669337300       1     2019-4            Greece   \n",
              "2  669121201       2     2019-4       Switzerland   \n",
              "3  668207242       1     2019-4    United Kingdom   \n",
              "4  668123744       2     2019-4    United Kingdom   \n",
              "\n",
              "                                         Review_Text            Branch  \\\n",
              "0  we booked tickets on line for disneyland, this...  Disneyland_Paris   \n",
              "1  why they selling fastpass tickets without have...  Disneyland_Paris   \n",
              "2  we went to disneyland paris during the easter ...  Disneyland_Paris   \n",
              "3  its a shame this park carry the name disney......  Disneyland_Paris   \n",
              "4  i know disneyland is a money machine but hadn ...  Disneyland_Paris   \n",
              "\n",
              "   New_Rating  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  "
            ],
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              "        vertical-align: middle;\n",
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              "      background-color: #3B4455;\n",
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              "        document.querySelector('#df-165957d7-a25e-4f19-9c79-63484f830ae3 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "    })();\n",
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              "</div>\n",
              "\n",
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              "type": "dataframe",
              "variable_name": "df_Paris_low",
              "summary": "{\n  \"name\": \"df_Paris_low\",\n  \"rows\": 1872,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 180684140,\n        \"min\": 2216235,\n        \"max\": 670216406,\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          114819291,\n          188760405,\n          150184207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 105,\n        \"samples\": [\n          \"2017-1\",\n          \"2013-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Canada\",\n          \"Greece\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          \"if you're in france, this is the absolute last thing you should be wanting to do. save your money and go to real castles, even if you have children.when i visited, a very large part of the park was closed (including rides) for 'renovations for your future enjoyment', which of course was not advertised when purchasing our tickets. nor was there any signage in the park about closures, which meant that you would walk down numerous streets only to come to dead ends and have to backtrack constantly. it was extremely frustrating.the rides were minimal   only 3 'big thrill' rides and not that many that i saw for children, either, there were absolutely no carnival games where you could play to win prizes, not a single disney character in sight, not enough restaurants for the mass of people (even in non peak eating times). as another reviewer so accurately described it, this was simply a giant disney themed shopping mall. the shops didn't even have much variety between each other, either   they all carried essentially the same merchandise with only a slight variation and the consumer panic in every shop was sickening.our assessment of the entire experience was very poor. we expected more from disney. if you absolutely must come here, don't bother unless your children are six years old or younger and you're interested in losing a ton of money on naff disney merchandise and being nagged all day by your children to buy things for them, because that's all your entertainment options.\",\n          \"disney is supposed to make your dreams come true. sadly the magic disappeared on day one when we arrived. the accommodation was fine, beds nice and comfy, a good size and clean. no kettle or drink making facilities in the room. we had to hire a kettle    25, which came with nothing !! oh of course you have to buy a tea package to go with it.    9, what does that have in it? four paper cups, paper cups ! four tea bags, uht milk in pots.sugar and four biscuits. no thanks . bought cups from store and got t bags from restaurants. needed a wheelchair, no mobility scooters! okay, you need to walk to the park for 20 mins which is quicker or catch the bus. done ! in park you have hire scrappy wheelchair which didn't open properly or steer well.    125:00 deposit and    45 a day as well as the evening before use and morning of return even when we were not in the park.    90 to have it sitting in the corridor. the hotel would not return it for us. bad organisation skills came to mind.....no facilites for a quick meal for the kids. restaurant     27 per head all you eat buffett. no good for kids and diabetics on portion control. bar snacks   all we offer is a slice of pizza and a pot of pasta heated in the microwave. so five portions and drinks   54 : (breakfast buffet. nothing for diabetics at all. sugar cereals, fruit in syrup, white bread, cheeses, croissants......oh dear!on site queues everywhere. expensive food and poor quality. broken rides, rides closed and kids cold and disappointed. we had to queue to see mickey, whinnie the pooh and no other characters around. very saddened by this. the best bits........ rides that were open, the parade was frequent and really exciting, christmas lights everywhere made it look so magical, fireworks were heavenly but on too late so my children missed them. the best thing of all was the character breakfast, it made their trip. lots of food and lots of characters to visit them.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_Paris_low['Review_Text'][0])\n",
        "print(df_Paris_low['Review_Text'][1])\n",
        "print(df_Paris_low['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6057443f-02b2-4e9b-8391-e97a46af010f",
        "id": "wHHLVsKxN6Zb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "we booked tickets on line for disneyland, this is something i recommend, the lines were already long when we got there, so booking your tickets in advance does help.i was advised to buy a fast passes, but i decided not to, because it was not clear to me on the website if this also counted as an entrance ticket, meaning; a total price of 90 euro with entrancevinstead or 74 or 68 or an extra 90 euro's, depending on age of child.but even if we would have, the lines were long there as well, and also, what kind of system is this? people that do not have that much to spend will be held back, because people with money obviously can skip the lines...absolutely ridiculous.getting something to eat was horrible, again lines everywhere...just to eat someting right...and don't get me started on the staff, trying to figure out the amount of 3 icecreams with a small casio computer, and not being able to, or very rude staff. only 2 ladies were friendly, have to be honest. all in all, very disappointing, long waiting lines, not being able to sit somewhere like a human being, and bad service.our kids were so disappointed that they wanted to leave.we even heard people saying: 'never again'.absolutely not worth the money.\n",
            "why they selling fastpass tickets without have buy before the entrance tickets???  any answer ???  shame on you!!!   we came here from greece until the entrance!!!  avoid this place!!!\n",
            "we went to disneyland paris during the easter break with family members who had come over from the states. as we had all experience disneyland in anaheim and disneyworld in orlando, perhaps our expectations were just too high. first off, the moving sidewalks from the parking lot to the park entrance were broken. not a great first impression. furthermore, there was a lot of gardening work ongoing when we arrived. isn't disney supposed to be magic, with everything looking in top shape and perfect? the security line moved, but was the first bottleneck. the second major bottleneck was to actually enter the park. as we had bought tickets online, we thought we would be able to enter the park fairly easily. however, there was pretty much mass chaos at the ticket check point, with many people jumping the queue and others just standing around. we eventually made it through and made our way to fantasyland.we managed to ride a few rides, but when it came time to get on the teacups, they had closed the ride. not for the day, but for about 45 minutes to disperse the line. this caused many people just to loiter around the entrance, waiting for the ride to open again. not great. we did manage to eat lunch  early  enough that we didn't have to wait a long time. however, the food that we got was not what we ordered. none of us had the wherewithal to go back and try to fix the order. so we just ate what we got. when it came time for ice cream, it took about 45 minutes just to get to the front of the line, and then another 10 minutes to process the order. this was because the staff at the counter were poorly trained, and only one person knew how to use the register, and others seemed like trainees. there were plenty of other foodcarts around the park that were empty. not enough staff at the park to operate these. there were also plenty of rides (like autopia and dumbo) that were closed for renovation. disneyland paris does not carry the iconic felt mouse ears. we definitely won't be going back, and i can't recommend disneyland paris to anyone who is looking for a true disney experience.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Stop words"
      ],
      "metadata": {
        "id": "t1-tfdOMN6Zb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "\n",
        "nltk.download('stopwords') # this makes sure these are downloaded if you haven't already!\n",
        "print(stopwords.words('english'))\n",
        "\n",
        "stop = stopwords.words('english') # see why it's important to turn everything lower case? all of your stopwords are!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "534dcb3d-3174-42dd-de05-2e8cbd2c7821",
        "id": "t2IhC3-1N6Zb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_Paris_low['Review_Text'] = df_Paris_low['Review_Text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
        "df_Paris_low.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7abcfcc8-c4d8-4578-99df-ce3403197dea",
        "id": "97yBpseeN6Zc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month Reviewer_Location  \\\n",
              "0  670216406       2     2019-4            Greece   \n",
              "1  669337300       1     2019-4            Greece   \n",
              "2  669121201       2     2019-4       Switzerland   \n",
              "3  668207242       1     2019-4    United Kingdom   \n",
              "4  668123744       2     2019-4    United Kingdom   \n",
              "\n",
              "                                         Review_Text            Branch  \\\n",
              "0  booked tickets line disneyland, something reco...  Disneyland_Paris   \n",
              "1  selling fastpass tickets without buy entrance ...  Disneyland_Paris   \n",
              "2  went disneyland paris easter break family memb...  Disneyland_Paris   \n",
              "3  shame park carry name disney... build place ye...  Disneyland_Paris   \n",
              "4  know disneyland money machine realised much se...  Disneyland_Paris   \n",
              "\n",
              "   New_Rating  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  "
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Review_ID</th>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e082f75f-d5a7-4927-a219-9506fe575545 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-e082f75f-d5a7-4927-a219-9506fe575545');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-fill-color: #FFFFFF;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "    30% {\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-76a12699-6eda-423c-bca7-af9d803465cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_Paris_low",
              "summary": "{\n  \"name\": \"df_Paris_low\",\n  \"rows\": 1872,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 180684140,\n        \"min\": 2216235,\n        \"max\": 670216406,\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          114819291,\n          188760405,\n          150184207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 105,\n        \"samples\": [\n          \"2017-1\",\n          \"2013-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Canada\",\n          \"Greece\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          \"france, absolute last thing wanting do. save money go real castles, even children.when visited, large part park closed (including rides) 'renovations future enjoyment', course advertised purchasing tickets. signage park closures, meant would walk numerous streets come dead ends backtrack constantly. extremely frustrating.the rides minimal 3 'big thrill' rides many saw children, either, absolutely carnival games could play win prizes, single disney character sight, enough restaurants mass people (even non peak eating times). another reviewer accurately described it, simply giant disney themed shopping mall. shops even much variety other, either carried essentially merchandise slight variation consumer panic every shop sickening.our assessment entire experience poor. expected disney. absolutely must come here, bother unless children six years old younger interested losing ton money naff disney merchandise nagged day children buy things them, that's entertainment options.\",\n          \"disney supposed make dreams come true. sadly magic disappeared day one arrived. accommodation fine, beds nice comfy, good size clean. kettle drink making facilities room. hire kettle 25, came nothing !! oh course buy tea package go it. 9, it? four paper cups, paper cups ! four tea bags, uht milk pots.sugar four biscuits. thanks . bought cups store got bags restaurants. needed wheelchair, mobility scooters! okay, need walk park 20 mins quicker catch bus. done ! park hire scrappy wheelchair open properly steer well. 125:00 deposit 45 day well evening use morning return even park. 90 sitting corridor. hotel would return us. bad organisation skills came mind.....no facilites quick meal kids. restaurant 27 per head eat buffett. good kids diabetics portion control. bar snacks offer slice pizza pot pasta heated microwave. five portions drinks 54 : (breakfast buffet. nothing diabetics all. sugar cereals, fruit syrup, white bread, cheeses, croissants......oh dear!on site queues everywhere. expensive food poor quality. broken rides, rides closed kids cold disappointed. queue see mickey, whinnie pooh characters around. saddened this. best bits........ rides open, parade frequent really exciting, christmas lights everywhere made look magical, fireworks heavenly late children missed them. best thing character breakfast, made trip. lots food lots characters visit them.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_Paris_low['Review_Text'][0])\n",
        "print(df_Paris_low['Review_Text'][1])\n",
        "print(df_Paris_low['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "012ba86f-118b-40fb-ac68-b7faeeac1021",
        "id": "uxMHJl3EN6Zc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "booked tickets line disneyland, something recommend, lines already long got there, booking tickets advance help.i advised buy fast passes, decided to, clear website also counted entrance ticket, meaning; total price 90 euro entrancevinstead 74 68 extra 90 euro's, depending age child.but even would have, lines long well, also, kind system this? people much spend held back, people money obviously skip lines...absolutely ridiculous.getting something eat horrible, lines everywhere...just eat someting right...and get started staff, trying figure amount 3 icecreams small casio computer, able to, rude staff. 2 ladies friendly, honest. all, disappointing, long waiting lines, able sit somewhere like human being, bad service.our kids disappointed wanted leave.we even heard people saying: 'never again'.absolutely worth money.\n",
            "selling fastpass tickets without buy entrance tickets??? answer ??? shame you!!! came greece entrance!!! avoid place!!!\n",
            "went disneyland paris easter break family members come states. experience disneyland anaheim disneyworld orlando, perhaps expectations high. first off, moving sidewalks parking lot park entrance broken. great first impression. furthermore, lot gardening work ongoing arrived. disney supposed magic, everything looking top shape perfect? security line moved, first bottleneck. second major bottleneck actually enter park. bought tickets online, thought would able enter park fairly easily. however, pretty much mass chaos ticket check point, many people jumping queue others standing around. eventually made made way fantasyland.we managed ride rides, came time get teacups, closed ride. day, 45 minutes disperse line. caused many people loiter around entrance, waiting ride open again. great. manage eat lunch early enough wait long time. however, food got ordered. none us wherewithal go back try fix order. ate got. came time ice cream, took 45 minutes get front line, another 10 minutes process order. staff counter poorly trained, one person knew use register, others seemed like trainees. plenty foodcarts around park empty. enough staff park operate these. also plenty rides (like autopia dumbo) closed renovation. disneyland paris carry iconic felt mouse ears. definitely going back, can't recommend disneyland paris anyone looking true disney experience.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Text EDA on the California low rating Data\n",
        "\n"
      ],
      "metadata": {
        "id": "-h2yPxAaN6Zc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Most common words"
      ],
      "metadata": {
        "id": "e6oie0uSN6Zc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "myTable = df_Paris_low['Review_Text'].str.split(expand=True).stack().value_counts()\n",
        "myTable[0:20] # too cool!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1da94677-0799-4ec2-a20f-be4ec9307971",
        "id": "G5BqnK1pN6Zc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disney        2680\n",
              "park          2215\n",
              "rides         1900\n",
              "get           1550\n",
              "one           1389\n",
              "would         1314\n",
              "go            1239\n",
              "disneyland    1214\n",
              "time          1166\n",
              "day           1034\n",
              "people        1031\n",
              "staff         1015\n",
              "food           978\n",
              "paris          952\n",
              "ride           892\n",
              "even           860\n",
              "like           822\n",
              "2              806\n",
              "queue          769\n",
              "us             753\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df_Paris_low['Review_Text'].str.split(expand=True).stack().value_counts()\n",
        "x = pd.DataFrame(x)\n",
        "# reset the index\n",
        "x.reset_index(inplace=True)\n",
        "# rename the columns\n",
        "x.rename(columns={x.columns[0]:'word', x.columns[1]:'frequency'}, inplace=True)\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ca689f5b-82c8-4b7a-c583-2c4841cad6a6",
        "id": "1ywxgY1LN6Zd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     word  frequency\n",
              "0  disney       2680\n",
              "1    park       2215\n",
              "2   rides       1900\n",
              "3     get       1550\n",
              "4     one       1389"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-698dbf0a-367a-41e6-8689-f248dd0d38bd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "  </thead>\n",
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              "      <th>1</th>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-698dbf0a-367a-41e6-8689-f248dd0d38bd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-698dbf0a-367a-41e6-8689-f248dd0d38bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d85a2c26-a63e-4a0e-8c65-3247abe52498 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 26887,\n  \"fields\": [\n    {\n      \"column\": \"word\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 26887,\n        \"samples\": [\n          \"stupid.7)\",\n          \"worsened\",\n          \"queues!!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47,\n        \"min\": 1,\n        \"max\": 2680,\n        \"num_unique_values\": 308,\n        \"samples\": [\n          20,\n          1034,\n          385\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = x[0:20] # wanna play? change the 20 to 10 or 30 and see what happens...\n",
        "            # you need to run the previous cell first\n",
        "x.plot(x='word', y='frequency')\n",
        "plt.xticks(np.arange(len(x)), x['word'], rotation=90)\n",
        "plt.suptitle('Top Words in DisneyLand Paris low rating Review')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "bed44b7d-601c-41aa-838e-f319b06a45e6",
        "id": "vTpBY_-3N6Zd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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t7dGqVSu8+eabGDp0aKHXqjh13rlzB1euXHnhF218fLzy/0OHDsXEiRNx//59uLq64rfffkNOTg6GDBlS4ud56tQpfPbZZwgODi50Wn1ycjLMzc1L9Dzu37+Pli1bFmpX1PvhZX7//XeYmZlBX18fzs7Ohd5fmzZtwvLly3Hr1i3k5OQo17u7uxfqq6h1zyvJ6/kqBZ8JDw8PlfUODg6wsLBQ+ez7+voqA09QUBCaNWuGZs2awcrKCkFBQbC3t8fly5cxcODAYm37Rc917969+OKLLxASEqIyDkfdM7qK+54oauD18z+fVyl4TyQkJGDVqlUIDw9XCWYFgW/YsGEv7CM5ORmWlpbKMTLbt29Hp06dAOQfPmrcuDFq1679wsffuXMHQogif18DUDmrzdfXF3PnzkVGRgaCgoLg6OiIJk2aoFGjRggKCkKXLl1w8uRJ9O/fv0Q/h8qIAaYKe3YcjFQqhZWVFby8vAAAjRs3hpGREU6ePInw8HCV9iUlk8mgo1P6nX39+/eHr68vdu3ahX/++QdLly7F4sWL8ccff6B79+4l7u9FZ0WI5wZJl8a1a9egq6v70i+/qVOnomfPnti9ezcOHjyITz75BAsXLsTRo0fh7e1dojoVCgW6dOmCWbNmFdn22V+qAwYMUI4j+N///ofNmzejWbNmJQ4JYWFh6NSpE7y8vLBixQq4uLhAKpXi77//xsqVKwsNvC3Ln/fz2rZtqzzj5HmbN2/G8OHD0bt3b8ycORN2dnbKMVzPD+gEVPc+vExxX8/iKk44aNOmDX788Ufcu3cPQUFB8PX1hUQiQZs2bRAUFAQnJycoFAqVvRwvU9RzDQoKwltvvYW2bdti9erVcHR0hL6+PjZs2ICtW7eW+Hk9S1vviZ49e6JBgwYYNGgQLly4AB0dHeX7denSpSp7e55VsGdVJpOhd+/e2LVrF1avXo24uDicOnUKCxYseGkNCoUCEokE+/fvL/K5F/QP5L+2OTk5CA4OVr62QH6wCQoKwq1bt5CQkFDs17YyY4ApZ1xdXXH48GGkpKSo/BV/69Yt5f3PKvjr4Vm3b9+GkZHRK3d/N2nSRBlSZDIZfHx8lL889fT00Lx5c5w6dQrh4eGws7NTfhkW1BAaGqo8y6NAaGhooRpf9DwVCgXCwsJUvkBDQ0OLbO/o6IgJEyZgwoQJiI+PR5MmTfDll1+WKsCUlcjISBw/fhw+Pj4v3ANToGbNmpgxYwZmzJiBO3fuoHHjxli+fDk2b95com3WrFkTqamp6Ny58yvbWllZwd/fH1u2bMGgQYNw6tQpfPXVVyXaHpA/8DcrKwt//vmnyl/Sz5/NUxKurq5Fvpdf9H4ojZ07d6JGjRr4448/VEJCUZM2lpQmXs+Cz8SdO3dU5oGKi4tDUlKSyueq4Mvr0KFDOHfunHLOkbZt22LNmjVwcnKCsbGxyuHlkvr9999hYGCAgwcPqszjsmHDhkJty2LWWVdXV9y9e7fQ+qLWFZeJiQk+++wzjBgxAjt27MCAAQOUe+nMzMyK9Tl69913sWnTJhw5cgQ3b96EEOKlh4+A/PeHEALu7u4v3VMDAC1atIBUKkVQUBCCgoIwc+ZMAPmv7Y8//ogjR44ob1d1HANTzvTo0QN5eXn49ttvVdavXLkSEomk0Bd2cHCwyvHoqKgo7NmzB127dn3l/At6enpo2bIlTp06hVOnTinHvxQoOBZ75swZ5aElAGjWrBns7Oywdu1ald3K+/fvx82bN4s8k+h5Bc/j2dM+ART6Qs3Lyys0psLOzg5OTk4q29a2xMREvPfee8jLy8NHH330wnbp6enIzMxUWVezZk2YmpqW6vn0798fwcHBOHjwYKH7kpKSkJubq7JuyJAhuHHjBmbOnAldXV2Vs1uKq+B99exfy8nJyUV+sRVXjx49cObMGeUZHwCQkJCgMmZHXUXVffbsWeW4gtLQ5OtZMHfQ85+BFStWAIDK58rd3R3VqlXDypUrkZOTo/x8+vr6IiwsDDt37kSrVq3UmotEV1cXEolEZSxdREREkTPuGhsba3x2Wz8/PwQHB6ucIZaYmKj2e2LQoEFwdnbG4sWLAQBNmzZFzZo1sWzZMuUh4GclJCSo3O7cuTOsrKywfft2bN++HS1atHjl4cZ33nkHurq6mDdvXqG9TEIIPH78WHnbwMAAzZs3x6+//orIyEiVPTAZGRlYtWoVatasCUdHx1I9/8qEe2DKmZ49e6JDhw746KOPEBERgUaNGuGff/7Bnj17MHXq1ELH9OvXrw8/Pz9MnjwZMpkMq1evBgDMmzevWNtr06aN8i/nZ0MKkB9gFi5cqGxXQF9fH4sXL8aIESPQrl07vPfee4iLi1Oefjlt2rRXbrdx48Z47733sHr1aiQnJ+ONN97AkSNHCv11lZKSAmdnZ/Tt2xeNGjWCiYkJDh8+jHPnzmH58uXFeo6advv2bWzevBlCCMjlcly+fBm//fYbUlNTsWLFCnTr1u2lj+3UqRP69++PunXrQk9PD7t27UJcXFypwsTMmTPx559/4s0338Tw4cPRtGlTpKWl4erVq9i5cyciIiJUDqn4+/vD2toav/32G7p37w47O7si+z1y5EihL2YA6N27N7p27QqpVIqePXvi/fffR2pqKn788UfY2dkhJiamxM8BAGbNmoVffvkF3bp1w5QpU2BsbIwffvgBrq6uuHLlSqn6fN6bb76JP/74A2+//Tb8/f0RHh6OtWvXom7dukV+cRWHJl/PRo0aYdiwYfjhhx+QlJSEdu3a4d9//8WmTZvQu3dvdOjQQaW9r68vtm3bhgYNGihPCW7SpAmMjY1x+/btYo9/eRF/f3/l+3ngwIGIj4/Hd999Bw8Pj0KvSdOmTXH48GGsWLECTk5OcHd3L3JMU0nMmjULmzdvRpcuXTBp0iQYGxvjp59+QvXq1ZGYmFjqvT76+vqYMmUKZs6ciQMHDqBbt2746aef0L17d9SrVw8jRoxAtWrV8PDhQwQGBsLMzAx//fWXyuPfeecdbNu2DWlpacW67EbNmjXxxRdfYM6cOYiIiEDv3r1hamqK8PBw7Nq1C2PHjsUHH3ygbO/r64tFixbB3NwcDRo0AJD/h5unpydCQ0OV8+xUedo49Yn+8/xp1EIIkZKSIqZNmyacnJyEvr6+qFWrlli6dGmh0yHx9JTezZs3i1q1agmZTCa8vb1FYGBgsbd/8OBBAUDo6ekpTyss8PjxYyGRSAqd3lpg+/btwtvbW8hkMmFlZSUGDRokHjx4oNJm2LBhwtjYuMhtZ2RkiMmTJwtra2thbGwsevbsKaKiolROo87KyhIzZ84UjRo1EqampsLY2Fg0atRIeQrpy7zoFE5/f/9Cbdu1ayfatWv3yj7x9NR1PD311MLCQnh7e4spU6YUeRrq86fdPnr0SAQEBAgvLy9hbGwszM3NRcuWLcWOHTtUHleSOlNSUsScOXOEh4eHkEqlwsbGRrzxxhti2bJlIjs7u1AfEyZMEADE1q1bX1jvi5ZffvlFCCHEn3/+KRo2bCgMDAyEm5ubWLx4sVi/fr1aP+8rV66Idu3aCQMDA1GtWjXx+eefi3Xr1pXolNmEhIQXtlEoFGLBggXC1dVV+VnZu3evGDZsmHB1dS30Myjq1OjSvp4vq/lZOTk5Yt68ecLd3V3o6+sLFxcXMWfOHJGZmVno8d99950AIMaPH6+yvnPnzgKAOHLkyCtrEOLlUwOsW7dO+bvFy8tLbNiwoci6b926Jdq2bSsMDQ0FAOUp1ep+Bi9duiR8fX2FTCYTzs7OYuHChWLVqlUCgIiNjX3p83rZeyI5OVmYm5urbO/SpUvinXfeEdbW1kImkwlXV1fRv3//In+Ohw4dEgCERCIRUVFRL9z2837//XfRpk0bYWxsLIyNjYWXl5cICAgQoaGhKu327dsnAIju3burrB89erQAINatW/fS515VSIQog1FT9FpIJBIEBAQUOtxE9DLTpk3DunXrEBsbW2Gua0VUYOrUqfj++++Rmpqq9csUkHZxDAxRFZKZmYnNmzejT58+DC9U7mVkZKjcfvz4MX755Re0adOG4YU4BoaoKoiPj8fhw4exc+dOPH78GFOmTNF2SUSv5OPjg/bt26NOnTqIi4vDunXrIJfL8cknn2i7NCoHGGCIqoAbN25g0KBBsLOzw6pVq1443wVRedKjRw/s3LkTP/zwAyQSCZo0aYJ169bxFGICAHAMDBEREVU4HANDREREFQ4DDBEREVU4DDBERERU4TDAEBERUYXDAENEREQVDgMMERERVTgMMERERFThMMAQERFRhcMAQ0RERBUOAwwRERFVOAwwREREVOEwwBAREVGFwwBDREREFQ4DDBEREVU4DDBERERU4TDAEBERUYXDAENEREQVDgMMERERVTgMMERERFThMMAQERFRhcMAQ0RERBUOAwwRERFVOAwwREREVOEwwBAREVGFwwBDREREFQ4DDBEREVU4DDBERERU4TDAEBERUYWjp+0CyopCoUB0dDRMTU0hkUi0XQ4REREVgxACKSkpcHJygo7OS/aziBJYsGCBaNasmTAxMRG2traiV69e4tatW8r7w8PDBYAilx07dijbFXX/r7/+qrKtwMBA4e3tLaRSqahZs6bYsGFDSUoVUVFRL6yFCxcuXLhw4VK+l6ioqJd+z0ueBopi6datGwYMGIDmzZsjNzcX//vf/3Dt2jXcuHEDxsbGyMvLQ0JCgspjfvjhByxduhQxMTEwMTEBAEgkEmzYsAHdunVTtrOwsICBgQEAIDw8HPXr18e4ceMwevRoHDlyBFOnTsW+ffvg5+dXrFqTk5NhYWGBqKgomJmZFfcpEhERkRbJ5XK4uLggKSkJ5ubmL2xXogDzvISEBNjZ2eH48eNo27ZtkW28vb3RpEkTrFu37r+NSiTYtWsXevfuXeRjZs+ejX379uHatWvKdQMGDEBSUhIOHDhQrNrkcjnMzc2RnJzMAENERFRBFPf7W61BvMnJyQAAKyurIu+/cOECQkJCMGrUqEL3BQQEwMbGBi1atMD69evxbI4KDg5G586dVdr7+fkhODj4hbVkZWVBLperLERERFQ5lXoQr0KhwNSpU9G6dWvUr1+/yDbr1q1DnTp18MYbb6isnz9/Pjp27AgjIyP8888/mDBhAlJTUzF58mQAQGxsLOzt7VUeY29vD7lcjoyMDBgaGhba1sKFCzFv3rzSPh0iIiKqQEodYAICAnDt2jWcPHmyyPszMjKwdetWfPLJJ4Xue3adt7c30tLSsHTpUmWAKY05c+Zg+vTpytsFx9CIiIio8ilVgJk4cSL27t2LEydOwNnZucg2O3fuRHp6OoYOHfrK/lq2bInPP/8cWVlZkMlkcHBwQFxcnEqbuLg4mJmZFbn3BQBkMhlkMlnJnwwREZVbQgjk5uYiLy9P26WQhujq6kJPT0/tKU5KFGCEEJg0aRJ27dqFY8eOwd3d/YVt161bh7feegu2trav7DckJASWlpbKAOLj44O///5bpc2hQ4fg4+NTknKJiKgCy87ORkxMDNLT07VdCmmYkZERHB0dIZVKS91HiQJMQEAAtm7dij179sDU1BSxsbEAAHNzc5U9I3fv3sWJEycKhRAA+OuvvxAXF4dWrVrBwMAAhw4dwoIFC/DBBx8o24wbNw7ffvstZs2ahZEjR+Lo0aPYsWMH9u3bV9rnSUREFYhCoUB4eDh0dXXh5OQEqVTKSUkrASEEsrOzkZCQgPDwcNSqVevlk9W9RIlOo37Rm2fDhg0YPny48vb//vc/bN68GREREYUKO3DgAObMmYO7d+9CCAEPDw+MHz8eY8aMUWl77NgxTJs2DTdu3ICzszM++eQTlW28Ck+jJiKquDIzMxEeHg5XV1cYGRlpuxzSsPT0dNy/fx/u7u7KOeAKFPf7W615YMozBhgiooqrIMAU9QVHFd/LXt/XMg8MERERkTYwwBAREWmQEAJjx46FlZUVJBIJQkJCtF1SpVRpr0ZNRESkDQcOHMDGjRtx7Ngx1KhRAzY2NtouqVJigCEiItKgsLAwODo6FpqFvkB2drZapw9TPh5CKqHfLzzAqI3ncPVBsrZLISKicmb48OGYNGkSIiMjIZFI4Obmhvbt22PixImYOnUqbGxs4OfnBwC4du0aunfvDhMTE9jb22PIkCF49OiRsq+0tDQMHToUJiYmcHR0xPLly9G+fXtMnTpV2UYikWD37t0qNVhYWGDjxo3K21FRUejfvz8sLCxgZWWFXr16ISIiQqXm3r17Y9myZXB0dIS1tTUCAgKQk5OjbJOVlYXZs2fDxcUFMpkMHh4eWLdunfJs4mXLlqnUEBISAolEgrt376r/Q30BBpgSOnwzDkduxePQzbhXNyYiIo0RQiA9O/e1LyU5Wffrr7/G/Pnz4ezsjJiYGJw7dw4AsGnTJkilUpw6dQpr165FUlISOnbsCG9vb5w/fx4HDhxAXFwc+vfvr+xr5syZOH78OPbs2YN//vkHx44dw8WLF0v0M8vJyYGfnx9MTU0RFBSEU6dOwcTEBN26dUN2drayXWBgIMLCwhAYGIhNmzZh48aNKiFo6NCh+PXXX7Fq1SrcvHkT33//PUxMTCCRSDBy5Ehs2LBBZbsbNmxA27Zt4eHhUaJ6S4KHkEqog5cd9l+LReCteEzvUlvb5RARVRkZOXmo++nB177dG/P9YCQt3telubk5TE1NoaurCwcHB+X6WrVqYcmSJcrbX3zxBby9vbFgwQLluvXr18PFxQW3b9+Gk5MT1q1bh82bN6NTp04A8kPQiy7f8yLbt2+HQqHATz/9pJzLbcOGDbCwsMCxY8fQtWtXAIClpSW+/fZb6OrqwsvLC/7+/jhy5AjGjBmD27dvY8eOHTh06BA6d+4MAKhRo4ZyG8OHD8enn36Kf//9Fy1atEBOTg62bt1aaK+MpjHAlFB7z/xLI1x9mIx4eSbszDg/ARERvVzTpk1Vbl++fBmBgYEwMTEp1DYsLAwZGRnIzs5Gy5YtleutrKzg6elZou1evnwZd+/ehampqcr6zMxMhIWFKW/Xq1cPurq6ytuOjo64evUqgPzDQbq6umjXrl2R23BycoK/vz/Wr1+PFi1a4K+//kJWVhb69etXolpLigGmhOxMDdDQ2RxXHiTj2O0E9G/GK14TEb0Ohvq6uDHfTyvbVZexsbHK7dTUVPTs2ROLFy8u1NbR0bHYY0ckEkmhQ1zPjl1JTU1F06ZNsWXLlkKPffZahfr6+oX6VSgUAPDCiyg/a/To0RgyZAhWrlyJDRs24N133y3zGZQZYEqhg6cdrjxIRuCteAYYIqLXRCKRFPtQTnnXpEkT/P7773Bzc4OeXuHnVLNmTejr6+Ps2bOoXr06AODJkye4ffu2yp4QW1tbxMTEKG/fuXNH5eKXTZo0wfbt22FnZ1fqWekbNGgAhUKB48ePKw8hPa9Hjx4wNjbGmjVrcODAAZw4caJU2yoJDuIthY5edgCAoDuPkJ2r0HI1RERU0QQEBCAxMRHvvfcezp07h7CwMBw8eBAjRoxAXl4eTExMMGrUKMycORNHjx7FtWvXMHz48ELXF+zYsSO+/fZbXLp0CefPn8e4ceNU9qYMGjQINjY26NWrF4KCghAeHo5jx45h8uTJePDgQbFqdXNzw7BhwzBy5Ejs3r1b2ceOHTuUbXR1dTF8+HDMmTMHtWrVgo+Pj2Z+UC/BAFMKDaqZw8ZEitSsXJyPSNR2OUREVME4OTnh1KlTyMvLQ9euXdGgQQNMnToVFhYWypCydOlS+Pr6omfPnujcuTPatGlTaCzN8uXL4eLiAl9fXwwcOBAffPCByqEbIyMjnDhxAtWrV8c777yDOnXqYNSoUcjMzCzRHpk1a9agb9++mDBhAry8vDBmzBikpaWptBk1ahSys7MxYsQINX4yxceLOZbSjB2X8fvFBxjdxh0fv1lX4/0TEVVlvJhj0dq3b4/GjRvjq6++0nYphQQFBaFTp06IioqCvb39S9vyYo5aVHAY6WhovJYrISIi0p6srCw8ePAAc+fORb9+/V4ZXjSFAaaUfGvbQE9HgnsJabj/OO3VDyAiIqqEfv31V7i6uiIpKUllrpuyxgBTSmYG+mjmZgkAOHqLe2GIiKjsHTt2rNwdPho+fDjy8vJw4cIFVKtW7bVtlwFGDcrDSAwwRERErxUDjBoKAszZe4lIy8rVcjVERERVBwOMGmramsDFyhDZeQqcuvvo1Q8gIqISqaQnylZ5mnhdGWDUIJFI0NEzfy9MIM9GIiLSmILJ2J6dVZYqj4LX9flLGJRE5ZiTWYs6eNlhU/B9BN5KgBBCebVPIiIqPV1dXVhYWCA+Pv+PQyMjI/5+rQSEEEhPT0d8fDwsLCxULiBZUgwwampVwxqG+rqIlWfiZkwK6jppftI8IqKqyMHBAQCUIYYqDwsLC+XrW1oMMGoy0NdFaw9rHL4Zj8DQeAYYIiINkUgkcHR0hJ2dncoVlqli09fXV2vPSwEGGA3o4GWHwzfjcfRWPAI6eGi7HCKiSkVXV1cjX3hUuXAQrwZ0eDqQ91LkEzxJy9ZyNURERJUfA4wGOFkYwsvBFAoBHL+doO1yiIiIKj0GGA3pwFl5iYiIXhsGGA0pmJX3+O0E5OYptFwNERFR5cYAoyHeLhYwN9RHckYOLkUlabscIiKiSo0BRkP0dHXQrrYtAB5GIiIiKmsMMBpUcBgpkAGGiIioTDHAaFC72rbQkQC3YlPwMClD2+UQERFVWgwwGmRpLIV3dUsA3AtDRERUlkoUYBYuXIjmzZvD1NQUdnZ26N27N0JDQ1XatG/fHhKJRGUZN26cSpvIyEj4+/vDyMgIdnZ2mDlzJnJzc1XaHDt2DE2aNIFMJoOHhwc2btxYumf4mhUcRjrGq1MTERGVmRIFmOPHjyMgIABnzpzBoUOHkJOTg65duyItLU2l3ZgxYxATE6NclixZorwvLy8P/v7+yM7OxunTp7Fp0yZs3LgRn376qbJNeHg4/P390aFDB4SEhGDq1KkYPXo0Dh48qObTLXsFs/KeuvsYmTl5Wq6GiIiocpIIIURpH5yQkAA7OzscP34cbdu2BZC/B6Zx48b46quvinzM/v378eabbyI6Ohr29vYAgLVr12L27NlISEiAVCrF7NmzsW/fPly7dk35uAEDBiApKQkHDhwoVm1yuRzm5uZITk6Gmdnru8CiEAJvLDqKmORMbBzRHO2fBhoiIiJ6teJ+f6s1BiY5ORkAYGVlpbJ+y5YtsLGxQf369TFnzhykp6cr7wsODkaDBg2U4QUA/Pz8IJfLcf36dWWbzp07q/Tp5+eH4ODgF9aSlZUFuVyusmiDRCJRhhaOgyEiIiobpQ4wCoUCU6dORevWrVG/fn3l+oEDB2Lz5s0IDAzEnDlz8Msvv2Dw4MHK+2NjY1XCCwDl7djY2Je2kcvlyMgo+uyehQsXwtzcXLm4uLiU9qmprWAczNHQeKixg4uIiIheQK+0DwwICMC1a9dw8uRJlfVjx45V/r9BgwZwdHREp06dEBYWhpo1a5a+0leYM2cOpk+frrwtl8u1FmJae1hDqquDqMQMhCWkwsPOVCt1EBERVVal2gMzceJE7N27F4GBgXB2dn5p25YtWwIA7t69CwBwcHBAXFycSpuC2w4ODi9tY2ZmBkNDwyK3I5PJYGZmprJoi5FUDy1r5B9W46y8REREmleiACOEwMSJE7Fr1y4cPXoU7u7ur3xMSEgIAMDR0REA4OPjg6tXryI+/r8v9kOHDsHMzAx169ZVtjly5IhKP4cOHYKPj09JytWqjrw6NRERUZkpUYAJCAjA5s2bsXXrVpiamiI2NhaxsbHKcSlhYWH4/PPPceHCBURERODPP//E0KFD0bZtWzRs2BAA0LVrV9StWxdDhgzB5cuXcfDgQXz88ccICAiATCYDAIwbNw737t3DrFmzcOvWLaxevRo7duzAtGnTNPz0y05BgDkf8QTyzBwtV0NERFS5lCjArFmzBsnJyWjfvj0cHR2Vy/bt2wEAUqkUhw8fRteuXeHl5YUZM2agT58++Ouvv5R96OrqYu/evdDV1YWPjw8GDx6MoUOHYv78+co27u7u2LdvHw4dOoRGjRph+fLl+Omnn+Dn56ehp132XK2NUcPWGLkKgaDbj7RdDhERUaWi1jww5Zm25oF51hd7b+Cnk+Ho08QZy/s30koNREREFclrmQeGXq7gMNLx2/FQKCplTiQiItIKBpgy1MzNCiYyPTxKzcaVh8naLoeIiKjSYIApQ1I9HfjWsgHAWXmJiIg0iQGmjHV4ehgpkFenJiIi0hgGmDLW3tMWAHDlQTLiUzK1XA0REVHlwABTxuxMDdDQ2RwAcCw0QcvVEBERVQ4MMK9BB16dmoiISKMYYF6DgtOpg+48QnauQsvVEBERVXwMMK9Bg2rmsDGRIjUrF+cjErVdDhERUYXHAPMa6OhI0K42L+5IRESkKQwwr4ny6tQ8nZqIiEhtDDCviW9tG+jpSHAvIQ33H6dpuxwiIqIKjQHmNTEz0EczN0sAPIxERESkLgaY10h5GIkBhoiISC0MMK9RQYA5ey8RaVm5Wq6GiIio4mKAeY1q2prAxcoQ2XkKnA57rO1yiIiIKiwGmNdIIpGgoycPIxEREamLAeY1K7g69bHQeAghtFwNERFRxcQA85q1qmENQ31dxCRn4mZMirbLISIiqpAYYF4zA31dtPawBgAEclI7IiKiUmGA0YIOPJ2aiIhILQwwWtDh6UDeS5FP8CQtW8vVEBERVTwMMFrgZGEILwdTKARw/HaCtsshIiKqcBhgtISHkYiIiEqPAUZLCmblPX47Abl5Ci1XQ0REVLEwwGiJt4sFzA31kZyRg0tRSdouh4iIqEJhgNESPV0dtKttC4CHkYiIiEqKAUaLCg4jBTLAEBERlQgDjBa1q20LHQlwKzYF0UkZ2i6HiIiowmCA0SJLYym8q1sC4Ky8REREJcEAo2U8jERERFRyDDBaVjAr76m7j5GZk6flaoiIiCqGEgWYhQsXonnz5jA1NYWdnR169+6N0NBQ5f2JiYmYNGkSPD09YWhoiOrVq2Py5MlITk5W6UcikRRatm3bptLm2LFjaNKkCWQyGTw8PLBx48bSP8tyrI6jKRzNDZCRk4cz9x5ruxwiIqIKoUQB5vjx4wgICMCZM2dw6NAh5OTkoGvXrkhLSwMAREdHIzo6GsuWLcO1a9ewceNGHDhwAKNGjSrU14YNGxATE6NcevfurbwvPDwc/v7+6NChA0JCQjB16lSMHj0aBw8eVO/ZlkMSiQTtPXkYiYiIqCQkQghR2gcnJCTAzs4Ox48fR9u2bYts89tvv2Hw4MFIS0uDnp5e/kYlEuzatUsltDxr9uzZ2LdvH65du6ZcN2DAACQlJeHAgQPFqk0ul8Pc3BzJyckwMzMr2RN7zQ7diMOYn8/DxcoQJ2Z2gEQi0XZJREREWlHc72+1xsAUHBqysrJ6aRszMzNleCkQEBAAGxsbtGjRAuvXr8ezOSo4OBidO3dWae/n54fg4OAXbicrKwtyuVxlqShae1hDqqeDqMQMhCWkarscIiKicq/UAUahUGDq1Klo3bo16tevX2SbR48e4fPPP8fYsWNV1s+fPx87duzAoUOH0KdPH0yYMAHffPON8v7Y2FjY29urPMbe3h5yuRwZGUXPl7Jw4UKYm5srFxcXl9I+tdfOSKqHVjWsAXBWXiIiouLQe3WTogUEBODatWs4efJkkffL5XL4+/ujbt26mDt3rsp9n3zyifL/3t7eSEtLw9KlSzF58uTSloM5c+Zg+vTpKtuvSCGmg6ctTtxOwNFb8Rjbtqa2yyEiIirXSrUHZuLEidi7dy8CAwPh7Oxc6P6UlBR069YNpqam2LVrF/T19V/aX8uWLfHgwQNkZWUBABwcHBAXF6fSJi4uDmZmZjA0NCyyD5lMBjMzM5WlIimYD+Z8xBPIM3O0XA0REVH5VqIAI4TAxIkTsWvXLhw9ehTu7u6F2sjlcnTt2hVSqRR//vknDAwMXtlvSEgILC0tIZPJAAA+Pj44cuSISptDhw7Bx8enJOVWKK7Wxqhha4xchUDQ7UfaLoeIiKhcK9EhpICAAGzduhV79uyBqakpYmNjAQDm5uYwNDRUhpf09HRs3rxZZTCtra0tdHV18ddffyEuLg6tWrWCgYEBDh06hAULFuCDDz5QbmfcuHH49ttvMWvWLIwcORJHjx7Fjh07sG/fPg0+9fKno6cd7iWEIzA0Hv4NHbVdDhERUblVotOoX3R674YNGzB8+HAcO3YMHTp0KLJNeHg43NzccODAAcyZMwd3796FEAIeHh4YP348xowZAx2d/3YIHTt2DNOmTcONGzfg7OyMTz75BMOHDy/2E6tIp1EXOH33EQb+dBY2JlL8+7/O0NHh6dRERFS1FPf7W615YMqzihhgsnMVaPr5IaRk5WLL6JZo7WGj7ZKIiIheq9cyDwxpllRPB729qwEANp6O0G4xRERE5RgDTDkz7A03AMDhm3GIfJyu3WKIiIjKKQaYcsbDzgRta9tCCGBTcIS2yyEiIiqXGGDKoRGt3QAAO85FIS0rV7vFEBERlUMMMOVQu1q2qGFjjJSsXPx+8YG2yyEiIip3GGDKIR0diXIszMbTEVAoKuWJYkRERKXGAFNO9WnqDFOZHu4lpOHEnQRtl0NERFSuMMCUUyYyPfRrln8xyg2nIrRbDBERUTnDAFOODX/DDRIJcPx2AsISUrVdDhERUbnBAFOOVbc2QicvewDAJk5sR0REpMQAU84VnFK988IDyDNztFsMERFROcEAU869UdManvamSM/Ow45zUdouh4iIqFxggCnnJBIJhj/dC7MpOAJ5PKWaiIiIAaYi6N24GiyM9BGVmIGjt+K1XQ4REZHWMcBUAIZSXQxoXh0AsOFUuJarISIi0j4GmApiiI8rdHUkOB32GLdi5douh4iISKsYYCqIahaG8KvHU6qJiIgABpgKZURrdwDAHxcf4klatparISIi0h4GmAqkmasl6jmZIStXgW08pZqIiKowBpgKRCKRKPfC/BIcgdw8hZYrIiIi0g4GmAqmZyNH2JhIEZ2ciYPX47RdDhERkVYwwFQwMj1dDGyRf0r1xtM8pZqIiKomBpgKaHArV+jpSHAu4gmuPUzWdjlERESvHQNMBWRnZgD/ho4AgPWc2I6IiKogBpgKqmAw797LMUhIydJyNURERK8XA0wF1djFAt7VLZCdp8DWs5HaLoeIiOi1YoCpwIa/4QYA2Hz2PrJzeUo1ERFVHQwwFViPBo6wN5MhISULf1+N0XY5RERErw0DTAWmr6uDIa1cAeRfpVoIoeWKiIiIXg8GmAruvRbVIdXTweUHybgUlaTtcoiIiF4LBpgKztpEhl6NnAAAG05FaLcYIiKi14QBphIY3toNALD/agxikzO1WwwREdFrwABTCdRzMkcLdyvkKgQ2n7mv7XKIiIjKXIkCzMKFC9G8eXOYmprCzs4OvXv3RmhoqEqbzMxMBAQEwNraGiYmJujTpw/i4lQvOhgZGQl/f38YGRnBzs4OM2fORG5urkqbY8eOoUmTJpDJZPDw8MDGjRtL9wyriJFP98Js/TcSmTl52i2GiIiojJUowBw/fhwBAQE4c+YMDh06hJycHHTt2hVpaWnKNtOmTcNff/2F3377DcePH0d0dDTeeecd5f15eXnw9/dHdnY2Tp8+jU2bNmHjxo349NNPlW3Cw8Ph7++PDh06ICQkBFOnTsXo0aNx8OBBDTzlyqlzHXtUszBEYlo2/rwcre1yiIiIypREqHHubUJCAuzs7HD8+HG0bdsWycnJsLW1xdatW9G3b18AwK1bt1CnTh0EBwejVatW2L9/P958801ER0fD3t4eALB27VrMnj0bCQkJkEqlmD17Nvbt24dr164ptzVgwAAkJSXhwIEDxapNLpfD3NwcycnJMDMzK+1TrFC+Px6GhftvoY6jGf6e3AYSiUTbJREREZVIcb+/1RoDk5ycfyVkKysrAMCFCxeQk5ODzp07K9t4eXmhevXqCA4OBgAEBwejQYMGyvACAH5+fpDL5bh+/bqyzbN9FLQp6KMoWVlZkMvlKktVM6B5dRjq6+JmjBxnwxO1XQ4REVGZKXWAUSgUmDp1Klq3bo369esDAGJjYyGVSmFhYaHS1t7eHrGxsco2z4aXgvsL7ntZG7lcjoyMjCLrWbhwIczNzZWLi4tLaZ9ahWVupI+3m1QDAGzkKdVERFSJlTrABAQE4Nq1a9i2bZsm6ym1OXPmIDk5WblERUVpuyStGPH0+kj/3IhFVGK6doshIiIqI6UKMBMnTsTevXsRGBgIZ2dn5XoHBwdkZ2cjKSlJpX1cXBwcHByUbZ4/K6ng9qvamJmZwdDQsMiaZDIZzMzMVJaqqJa9Kdp42EAhwFOqiYio0ipRgBFCYOLEidi1axeOHj0Kd3d3lfubNm0KfX19HDlyRLkuNDQUkZGR8PHxAQD4+Pjg6tWriI+PV7Y5dOgQzMzMULduXWWbZ/soaFPQB73ciKenVP/6byTSs3Nf3piIiKgCKlGACQgIwObNm7F161aYmpoiNjYWsbGxynEp5ubmGDVqFKZPn47AwEBcuHABI0aMgI+PD1q1agUA6Nq1K+rWrYshQ4bg8uXLOHjwID7++GMEBARAJpMBAMaNG4d79+5h1qxZuHXrFlavXo0dO3Zg2rRpGn76lVMHTzu4WhtBnpmLPy4+1HY5REREmidKAECRy4YNG5RtMjIyxIQJE4SlpaUwMjISb7/9toiJiVHpJyIiQnTv3l0YGhoKGxsbMWPGDJGTk6PSJjAwUDRu3FhIpVJRo0YNlW0UR3JysgAgkpOTS/S4ymJd0D3hOnuv6LT8mFAoFNouh4iIqFiK+/2t1jww5VlVnAfmWSmZOfBZeBSpWbn4ZVQL+Nay1XZJREREr/Ra5oGh8svUQB99m+YPsOYp1UREVNkwwFRiw95wg0QCHA2NR8SjtFc/gIiIqIJggKnE3G2M0cHTDkIAG09HaLscIiIijWGAqeSGP53YbueFB0jJzNFuMURERBrCAFPJ+daygYedCVKzcrHzwgNtl0NERKQRDDCVnEQiUe6F2XQ6AgpFpTzpjIiIqhgGmCrgnSbVYGagh4jH6Th2O/7VDyAiIirnGGCqACOpHga0qA4A2MBTqomIqBJggKkihrRyhY4ECLrzCHfiUrRdDhERkVoYYKoIFysjdKlrD4CnVBMRUcXHAFOFjGidf/XwXZceIjWLV6kmIqKKiwGmCmnpboUatsZIz87D3svR2i6HiIio1BhgqhCJRIJ3m7kAALafj9JyNURERKXHAFPFvNPEGXo6ElyKTEJoLAfzEhFRxcQAU8XYmsrQuU7+YN7t57gXhoiIKiYGmCro3eb5h5F2XXqArNw8LVdDRERUcgwwVVDb2rZwMDPAk/QcHLoRp+1yiIiISowBpgrS1ZGgXzNnADyMREREFRMDTBXV/+nZSCfvPsKDJ+laroaIiKhkGGCqKBcrI7T2sIYQwG/nH2i7HCIiohJhgKnC3m2ef4HH385HIU8htFwNERFR8THAVGFd69rD3FAf0cmZOHn3kbbLISIiKjYGmCrMQF8Xb3tXAwBsPxep5WqIiIiKjwGmiiuYE+bQjTg8Ts3ScjVERETFwwBTxdVxNEMjZ3Pk5AnsuvRQ2+UQEREVCwMMof/TvTDbzkVBCA7mJSKi8o8BhvBWIycY6uvibnwqLkYmabscIiKiV2KAIZga6MO/oSMADuYlIqKKgQGGAPw3mHfvlRikZuVquRoiIqKXY4AhAEAzV0vUsDVGenYe9l6O1nY5REREL8UAQwAAiUSCd59eH2n7eV7gkYiIyjcGGFJ6p4kz9HQkuBSZhNDYFG2XQ0RE9EIlDjAnTpxAz5494eTkBIlEgt27d6vcL5FIilyWLl2qbOPm5lbo/kWLFqn0c+XKFfj6+sLAwAAuLi5YsmRJ6Z4hFZutqQyd6tgBALaf414YIiIqv0ocYNLS0tCoUSN89913Rd4fExOjsqxfvx4SiQR9+vRRaTd//nyVdpMmTVLeJ5fL0bVrV7i6uuLChQtYunQp5s6dix9++KGk5VIJDXh6gcddlx4gKzdPy9UQEREVTa+kD+jevTu6d+/+wvsdHBxUbu/ZswcdOnRAjRo1VNabmpoWaltgy5YtyM7Oxvr16yGVSlGvXj2EhIRgxYoVGDt2bElLphJoW9sWDmYGiJVn4tCNOLzZ0EnbJRERERVSpmNg4uLisG/fPowaNarQfYsWLYK1tTW8vb2xdOlS5Ob+d+pucHAw2rZtC6lUqlzn5+eH0NBQPHnypCxLrvJ0dSTo18wZAA8jERFR+VXiPTAlsWnTJpiamuKdd95RWT958mQ0adIEVlZWOH36NObMmYOYmBisWLECABAbGwt3d3eVx9jb2yvvs7S0LLStrKwsZGX9dzFCuVyu6adTZfRr6oJvjt7FybuP8OBJOpwtjbRdEhERkYoy3QOzfv16DBo0CAYGBirrp0+fjvbt26Nhw4YYN24cli9fjm+++UYlgJTUwoULYW5urlxcXFzULb/Kqm5thNYe1hAC+O38A22XQ0REVEiZBZigoCCEhoZi9OjRr2zbsmVL5ObmIiIiAkD+OJq4uDiVNgW3XzRuZs6cOUhOTlYuUVE8/KGO/k/nhPntfBTyFLzAIxERlS9lFmDWrVuHpk2bolGjRq9sGxISAh0dHdjZ5Z/C6+PjgxMnTiAnJ0fZ5tChQ/D09Czy8BEAyGQymJmZqSxUen71HGBuqI/o5EycvPtI2+UQERGpKHGASU1NRUhICEJCQgAA4eHhCAkJQWTkfxcBlMvl+O2334rc+xIcHIyvvvoKly9fxr1797BlyxZMmzYNgwcPVoaTgQMHQiqVYtSoUbh+/Tq2b9+Or7/+GtOnTy/l06SSMtDXxdve1QDwAo9ERFT+lHgQ7/nz59GhQwfl7YJQMWzYMGzcuBEAsG3bNggh8N577xV6vEwmw7Zt2zB37lxkZWXB3d0d06ZNUwkn5ubm+OeffxAQEICmTZvCxsYGn376KU+hfs3ebe6CjacjcOhGHB6nZsHaRKbtkoiIiAAAEiFEpRzgIJfLYW5ujuTkZB5OUkOvb0/i8oNkfOxfB6N9a7z6AURERGoo7vc3r4VEL9W/ef5g3m3nolBJsy4REVVADDD0Um81coKhvi7uxqfiYmSStsshIiICwABDr2BqoI8eDRwBcDAvERGVHwww9EoDWuQfRtp7JQapWbmvaE1ERFT2GGDolZq5WqKGrTHSs/Ow93K0tsshIiJigKFXk0gkePfpzLzbz3OGYyIi0j4GGCqWd5o4Q09HgkuRSbgdl6LtcoiIqIpjgKFisTWVoVOd/Es9bD/HvTBERKRdDDBUbO8+nRPmj4sPkJWbp+VqiIioKmOAoWJrW8sWDmYGeJKeg0M34l79ACIiojLCAEPFpqerg37NnAHwMBIREWkXAwyVSL+m+YeRTt59hAdP0rVcDRERVVUMMFQi1a2N0NrDGkIAv51/oO1yiIioimKAoRLr/3ROmN/ORyFPwQs8EhHR68cAQyXmV88B5ob6iE7OxMm7j7RdDhERVUEMMFRiBvq6eNu7GgBe4JGIiLSDAYZKpWBOmEM34vA4NUvL1RARUVXDAEOlUsfRDA2dzZGTJ7Dr0kNtl0NERFUMAwyVWsFemG3noiAEB/MSEdHrwwBDpdazkRMM9HVwNz4VFyOTtF0OERFVIQwwVGpmBvrwb+AEgIN5iYjo9WKAIbUUHEbaeyUGqVm5Wq6GiIiqCgYYUktzN0vUsDFGenYe9l6O1nY5RERURTDAkFokEolyL8z287zAIxERvR4MMKS2d5o4Q09HgkuRSbgdl6LtcoiIqApggCG12ZrK0KmOHQBg+znuhSEiorLHAEMaUXAY6Y+LD5CVm6flaoiIqLJjgCGNaFvLFg5mBniSnoN/rsdpuxwiIqrkGGBII/R0ddCvmTMA4Mt9NxGbnKnlioiIqDJjgCGNGdO2BmrZmSBWnonRP59DejbnhSEiorLBAEMaY2agj3XDmsPKWIprD+WYvv0yFApeI4mIiDSPAYY0qrq1EX4Y0hRSXR0cuB6Lpf+EarskIiKqhBhgSOOauVlhcd8GAIA1x8LwGye4IyIiDStxgDlx4gR69uwJJycnSCQS7N69W+X+4cOHQyKRqCzdunVTaZOYmIhBgwbBzMwMFhYWGDVqFFJTU1XaXLlyBb6+vjAwMICLiwuWLFlS8mdHWvO2tzMmdfQAAPxv11WcufdYyxUREVFlUuIAk5aWhkaNGuG77757YZtu3bohJiZGufz6668q9w8aNAjXr1/HoUOHsHfvXpw4cQJjx45V3i+Xy9G1a1e4urriwoULWLp0KebOnYsffvihpOWSFk3rXBv+DR2RkycwbvMFRDxK03ZJRERUSeiV9AHdu3dH9+7dX9pGJpPBwcGhyPtu3ryJAwcO4Ny5c2jWrBkA4JtvvkGPHj2wbNkyODk5YcuWLcjOzsb69eshlUpRr149hISEYMWKFSpBh8o3HR0JlvdrhAdPMnA5KgkjN53DrvGtYW6kr+3SiIiogiuTMTDHjh2DnZ0dPD09MX78eDx+/N/hg+DgYFhYWCjDCwB07twZOjo6OHv2rLJN27ZtIZVKlW38/PwQGhqKJ0+eFLnNrKwsyOVylYW0z0BfFz8ObQoncwPcS0jD+C0XkJOn0HZZRERUwWk8wHTr1g0///wzjhw5gsWLF+P48ePo3r078vLyp5ePjY2FnZ2dymP09PRgZWWF2NhYZRt7e3uVNgW3C9o8b+HChTA3N1cuLi4umn5qVEp2pgZYN7w5jKW6OB32GJ/uuQ4heHo1ERGVnsYDzIABA/DWW2+hQYMG6N27N/bu3Ytz587h2LFjmt6Uijlz5iA5OVm5REXxzJfypI6jGVa95w0dCfDrv5FYdzJc2yUREVEFVuanUdeoUQM2Nja4e/cuAMDBwQHx8fEqbXJzc5GYmKgcN+Pg4IC4ONXr6RTcftHYGplMBjMzM5WFypdOdezxkX9dAMCXf9/E4Ru8ZhIREZVOmQeYBw8e4PHjx3B0dAQA+Pj4ICkpCRcuXFC2OXr0KBQKBVq2bKlsc+LECeTk5CjbHDp0CJ6enrC0tCzrkqkMjWzthoEtq0MIYPK2S7genaztkoiIqAIqcYBJTU1FSEgIQkJCAADh4eEICQlBZGQkUlNTMXPmTJw5cwYRERE4cuQIevXqBQ8PD/j5+QEA6tSpg27dumHMmDH4999/cerUKUycOBEDBgyAk5MTAGDgwIGQSqUYNWoUrl+/ju3bt+Prr7/G9OnTNffMSSskEgnmvVUPbTxskJ6dh9GbziNezgs/EhFRyUhECUdTHjt2DB06dCi0ftiwYVizZg169+6NS5cuISkpCU5OTujatSs+//xzlUG5iYmJmDhxIv766y/o6OigT58+WLVqFUxMTJRtrly5goCAAJw7dw42NjaYNGkSZs+eXew65XI5zM3NkZyczMNJ5VByRg7eWX0KYQlpaORsjm1jfWAo1dV2WUREpGXF/f4ucYCpKBhgyr/7j9PQ+7tTeJKegx4NHPDte02goyPRdllERKRFxf3+5rWQSGtcrY3x/ZBm0NeV4O+rsVhx6La2SyIiogqCAYa0qoW7FRa90xAA8G3gXfx+4YGWKyIiooqAAYa0rk9TZwR0qAkA+PCPK/g3PFHLFRERUXnHAEPlwowunujRwAE5eQLv/3Ie9x/zwo9ERPRiDDBULuRf+LExGjqb40l6DkZuPIfkjJxXP5CIiKokBhgqNwyluvhpaDM4mhsgLCENE7de5IUfiYioSAwwVK7YmRngp2HNYCTVRdCdR5j7Jy/8SEREhTHAULlTz8kcXw/whkQCbDkbiQ2nIrRdEhERlTMMMFQudalrj/91rwMA+GLfDRy9xQs/EhHRfxhgqNwa7euO91q4QCGASVsv4WaMXNslERFROcEAQ+WWRCLB/F718UZNa6QVXPgxhRd+JCIiBhgq5/R1dbBmUFPUsDHGw6QMjPn5AhLTsrVdFhERaRkDDJV75kb6WDe8OSyM9HE5KgntlgRizbEwZObkabs0IiLSEgYYqhDcbYzxy8iWqOtohpSsXCw+cAsdlx3DHxcfQKHgadZERFWNRFTSSTaKezluqlgUCoFdlx5i2T+hiEnOHw9Tz8kM/+tRB609bLRcHRERqau4398MMFQhZebkYf2pcKwJDENKVi4AoL2nLeZ0rwNPB1MtV0dERKXFAMMAUyU8Ts3CN0fvYvOZ+8hVCOhIgP7NXDC9S23YmRlouzwiIiohBhgGmCol/FEalhy4hf3XYgEAhvq6GNO2Bt5vWwPGMj0tV0dERMXFAMMAUyVduJ+IL/fdxMXIJACAjYkM07rUwrvNXKCnyzHrRETlHQMMA0yVJYTAgWuxWHzgFiIepwMAPOxM8GE3L3SqYweJRKLlComI6EUYYBhgqrzsXAW2nL2PVUfu4El6DgCgpbsVPvKvg4bOFtotjoiIisQAwwBDT8kzc7DmWBjWnwxHVq4CAPBWIyfM9POEi5WRlqsjIqJnMcAwwNBzHiZlYPnBUOwKeQghAKmuDoa94YqJHWrB3Ehf2+UREREYYBhg6IWuPUzGwv03ceruYwCAuaE+JnX0wBAfV8j0dLVcHRFR1cYAwwBDLyGEwLHbCVj09y2ExqUAAFysDDHTzws9GzpyoC8RkZYwwDDAUDHkKQR2XojC8n9uIz4lCwDQyMUCH/WogxbuVlqujoio6mGAYYChEkjPzsVPQeH4/ngY0rLzr3Ldpa49PuzuhZq2Jlqujoio6mCAYYChUkhIycJXh29j27ko5CkEdHUkeK+FC6Z0qg1bU5m2yyMiqvQYYBhgSA1341OwaH8oDt+MAwAYS3Uxrl1NjPatAUMpB/oSEZUVBhgGGNKAM/ceY+HfN3H5QTIAwN5MhhldPNGnqTN0dTjQl4hI0xhgGGBIQxQKgb+uRGPpwVA8eJIBAPByMMWcHnXQrratlqsjIqpcGGAYYEjDsnLz8PPp+/jm6B3IM3MBAL61bPBhdy/UczLXcnVERJUDAwwDDJWRpPRsfHv0Ln4Ovo/sPAUkEuBt72r4oKsnnCwMtV0eEVGFVtzvb52SdnzixAn07NkTTk5OkEgk2L17t/K+nJwczJ49Gw0aNICxsTGcnJwwdOhQREdHq/Th5uYGiUSisixatEilzZUrV+Dr6wsDAwO4uLhgyZIlJS2VqExYGEnx8Zt1cWRGO/Rs5AQhgD8uPkSHZcew5MAtyDNztF0iEVGlV+IAk5aWhkaNGuG7774rdF96ejouXryITz75BBcvXsQff/yB0NBQvPXWW4Xazp8/HzExMcpl0qRJyvvkcjm6du0KV1dXXLhwAUuXLsXcuXPxww8/lLRcojLjYmWEb97zxu6A1mjhboWsXAVWHwtD+6XHsOl0BHLyFNoukYio0lLrEJJEIsGuXbvQu3fvF7Y5d+4cWrRogfv376N69eoA8vfATJ06FVOnTi3yMWvWrMFHH32E2NhYSKVSAMCHH36I3bt349atW8WqjYeQ6HUSQuDwzXgs3H8T9xLSAADuNsaY3c0TfvUceGkCIqJiKrNDSCWVnJwMiUQCCwsLlfWLFi2CtbU1vL29sXTpUuTm5irvCw4ORtu2bZXhBQD8/PwQGhqKJ0+eFLmdrKwsyOVylYXodZFIJOhS1x4Hp7bF573rw8ZEivBHaRi3+SL6rQ3GhftFv2+JiKh0yjTAZGZmYvbs2XjvvfdUUtTkyZOxbds2BAYG4v3338eCBQswa9Ys5f2xsbGwt7dX6avgdmxsbJHbWrhwIczNzZWLi4tLGTwjopfT19XBkFauODazAyZ19ICBvg7O33+CPmtOY8KWCzgd9gjJ6RwjQ0SkLr2y6jgnJwf9+/eHEAJr1qxRuW/69OnK/zds2BBSqRTvv/8+Fi5cCJmsdNO1z5kzR6VfuVzOEENaYyLTw4yunhjU0hUrDoXitwsP8PfVWPx9NT+AO5kboI6j2TOLKdysjaHDyfGIiIqlTAJMQXi5f/8+jh49+soxKC1btkRubi4iIiLg6ekJBwcHxMXFqbQpuO3g4FBkHzKZrNThh6isOJgbYEnfRhjZxh3fBYbhUuQTPHiSgejkTEQnZ+LIrXhlW0N9XXg6mKKOoxnqOub/6+VoBhNZmf2dQURUYWn8N2NBeLlz5w4CAwNhbW39yseEhIRAR0cHdnZ2AAAfHx989NFHyMnJgb6+PgDg0KFD8PT0hKWlpaZLJipzXg5m+OY9bwBAckYObsXIcTNGjpsxKbgZK0dobAoycvIQEpWEkKgklcdWtzJCnaeBJj/cmMHZ0pADg4moSitxgElNTcXdu3eVt8PDwxESEgIrKys4Ojqib9++uHjxIvbu3Yu8vDzlmBUrKytIpVIEBwfj7Nmz6NChA0xNTREcHIxp06Zh8ODBynAycOBAzJs3D6NGjcLs2bNx7do1fP3111i5cqWGnjaR9pgb6qNlDWu0rPFfuM/NUyDicRpuxKQ8DTb5S5w8C5GJ6YhMTMfB6//tlTSV6cHrmVBTx9EMnvamvNAkEVUZJT6N+tixY+jQoUOh9cOGDcPcuXPh7u5e5OMCAwPRvn17XLx4ERMmTMCtW7eQlZUFd3d3DBkyBNOnT1c5BHTlyhUEBATg3LlzsLGxwaRJkzB79uxi18nTqKkySEzLVoaZG0/32NyNT0FOXuGPra6OBK09bNCrkRP86jvw0BMRVUi8lAADDFVS2bkKhCWkPrOnJn+vzeO0bGUbmZ4OOte1R69GTmjnaQuZHvfMEFHFwADDAENViBACEY/T8WdINPZcfqicTA/IP2TVo4ED3mpUDS3drXimExGVawwwDDBURQkhcD1ajt2XHuKvK9GIk2cp73MwM0DPRo7o1bga6jmZcSAwEZU7DDAMMETIUwicDX+MP0Oi8ffVGMgz/5vxuqatMXo1roZejZ3gam2sxSqJiP7DAMMAQ6QiKzcPx0IT8GdINA7fjENW7n8Xm2zsYoFejZ3g39ARdqYGWqySiKo6BhgGGKIXSsnMwcHrcdgT8hCn7j6C4ulvAR0J8s9kalwNfvXsYWqgr91CiajKYYBhgCEqloSULOy9Eo09IdEqk+jJ9HTQqY4dejWuhvY8k4mIXhMGGAYYohK7/zgNf4ZEY3fIQ4Q9cyaTqYEeejV2wofd63B+GSIqUwwwDDBEpVZwJtOfl6PxZ0g0YuWZAADv6hbYOKIFzA15aImIygYDDAMMkUbkKQSC7iRg6vYQJKXnoH41M/wysiUsjaXaLo2IKqHifn/rvMaaiKgC0tWRoL2nHbaObgVrYymuPZTjvR/P4FFq1qsfTERURhhgiKhY6jqZYdvYVrA1leFWbAoG/HAG8U8PLRERvW4MMERUbLXsTbHjfR84mhvgbnwq+n8fjOikDG2XRURVEAMMEZWIu40xdrzvA2dLQ0Q8Tkf/74MRlZiu7bKIqIphgCGiEnOxMsL2933gZm2EB08y8O73wQh/lPbqBxIRaQgDDBGVSjULQ2x/3wc1bY0RnZyJd78Pxt34FG2XRURVBAMMEZWavZkBtr/vAy8HU8SnZOHd78/gZoxc22URURXAAENEarExkeHXMa1Qv5oZHqdl470fz+Daw2Rtl0VElRwDDBGpzdJYii2jW6GxiwWS0nPw3o9ncCnyibbLIqJKjAGGiDTC3FAfv4xqgeZulkjJzMXgn87i3/BEbZdFRJUUAwwRaYypgT42jWyBN2paIy07D8PW/4vTdx9puywiqoQYYIhIo4ykelg/vDna1bZFRk4eRmw8h2Oh8doui4gqGQYYItI4A31d/DC0KTrXsUNWrgJjf76AQzfitF0WEVUiDDBEVCZkerpYPagpejRwQHaeAuM3X8DfV2O0XRYRVRIMMERUZqR6Olg1wBu9GjshVyEwcetF7L70UNtlEVElwABDRGVKT1cHK/o3Rr+mzlAIYNqOEOw4F6XtsoiogmOAIaIyp6sjweI+DTGoZXUIAcz6/Qo2n7mv7bKIqAJjgCGi10JHR4IvetfHiNZuAICPd1/D+pPh2i2KiCosBhgiem0kEgk+fbMuxrWrCQCYv/cG1h4P03JVRFQRMcAQ0WslkUgwu5snpnSqBQBYtP8Wvj58B0IILVdGRBWJnrYLIKKqRyKRYFqX2pDq6WDpwVCsPHwb16KTYW8mg76uDqS6OtDTlUBfV+fpkv9/PV0dSJ/7v56ODvT1/mvzbHt9XR3o6Ugg1dOBpZEUUj3+zUZUWTDAEJHWBHTwgExPB1/su1nmE91JJIC9qQGcLAzgZGGIahaGcFIuBqhmYQhzQ31IJJIyrYOINIMBhoi0arRvDdRxNMOlyCfIyRPIyVM8XfL/n/v03+wX/D9HIZCTm/+YXIVAdq4CuYqnj89VIEehQHauAgoBxMozESvPxMXIpCJrMZLqKkNNNQtDVHsadgpu25sZcC8OUTkhESU88HzixAksXboUFy5cQExMDHbt2oXevXsr7xdC4LPPPsOPP/6IpKQktG7dGmvWrEGtWrWUbRITEzFp0iT89ddf0NHRQZ8+ffD111/DxMRE2ebKlSsICAjAuXPnYGtri0mTJmHWrFnFrlMul8Pc3BzJyckwMzMryVMkokpGCIFHqdmITspAdFIGHiZlIDopM/92cv66R6nZr+xHIgHsTGUqocbJ3AAuVkZo6moJCyPpa3g2RJVbcb+/S7wHJi0tDY0aNcLIkSPxzjvvFLp/yZIlWLVqFTZt2gR3d3d88skn8PPzw40bN2BgYAAAGDRoEGJiYnDo0CHk5ORgxIgRGDt2LLZu3aosvmvXrujcuTPWrl2Lq1evYuTIkbCwsMDYsWNLWjIRVXESiQS2pjLYmsrQyMWiyDaZOXlPA07mMyGnIOBk4mFSBrJzFYiTZyFOnoVLz+3F0ZEADZwt0LaWDXxr2cK7ugX0dbm3hqislHgPjMqDJRKVPTBCCDg5OWHGjBn44IMPAADJycmwt7fHxo0bMWDAANy8eRN169bFuXPn0KxZMwDAgQMH0KNHDzx48ABOTk5Ys2YNPvroI8TGxkIqzf+L5sMPP8Tu3btx69atYtXGPTBEpEkv24tzJz4FYQlpKu2NpbrwqWmNNh428K1tixo2xhxfQ1QMZbYH5mXCw8MRGxuLzp07K9eZm5ujZcuWCA4OxoABAxAcHAwLCwtleAGAzp07Q0dHB2fPnsXbb7+N4OBgtG3bVhleAMDPzw+LFy/GkydPYGlpWWjbWVlZyMrKUt6Wy+WafGpEVMW9ai9OTHIGgu48wsk7j3Dy7iMkpmXj8M14HL4ZDwCoZmH4NMzYoHVNG1ga83ATkTo0GmBiY2MBAPb29irr7e3tlffFxsbCzs5OtQg9PVhZWam0cXd3L9RHwX1FBZiFCxdi3rx5mnkiREQl5GhuiP7NXNC/mQsUCoEbMXIE3XmEoDsJOB/xBA+TMrD9fBS2n4+CRAI0qGYO31o2aONhi6aulhwcTFRCleYspDlz5mD69OnK23K5HC4uLlqsiIiqKh0dCepXM0f9auYY374mMrLzcDb8MU7eeYSgO48QGpeCKw+SceVBMr4LDIORVBct3a3gW8sWbWvboKatCQ83Eb2CRgOMg4MDACAuLg6Ojo7K9XFxcWjcuLGyTXx8vMrjcnNzkZiYqHy8g4MD4uJU54QouF3Q5nkymQwymUwjz4OISJMMpbpo72mH9p75e5/j5JlPw0wCTt59hEep2QgMTUBgaAIAwMHMAL618sfOtK5pDWsT/m4jep5GA4y7uzscHBxw5MgRZWCRy+U4e/Ysxo8fDwDw8fFBUlISLly4gKZNmwIAjh49CoVCgZYtWyrbfPTRR8jJyYG+vj4A4NChQ/D09Czy8BERUUVib2aAPk2d0aepMxQKgVuxKcowczY8EbHyTPx24QF+u/AAAODlYIr61cxRz8kM9ZzMUcfRFKYG+lp+FkTaVeKzkFJTU3H37l0AgLe3N1asWIEOHTrAysoK1atXx+LFi7Fo0SKV06ivXLmichp19+7dERcXh7Vr1ypPo27WrJnyNOrk5GR4enqia9eumD17Nq5du4aRI0di5cqVxT6NmmchEVFFlJmTh3MRiU/HzzzCzZiiT0hwszZCPSdz1HUyQ10nM9RzMoOdqcFrrpZI84r7/V3iAHPs2DF06NCh0Pphw4Zh48aNyonsfvjhByQlJaFNmzZYvXo1ateurWybmJiIiRMnqkxkt2rVqhdOZGdjY4NJkyZh9uzZxa6TAYaIKoP4lExcikzC9Wg5bkQn43q0HDHJmUW2tTWVPd1Lk7+npp6TGVwsjaCjw/E0VHGUWYCpKBhgiKiyepyahRsxclyPLliSEf4oDUX9NjeV6aGO4397aeo5maOWvQkn2aNyiwGGAYaIqpC0rFzcik1R7qW5Hi1HaGwKsvMUhdpKdXVQ28EE9RzzD0G1qZV/5hNRecAAwwBDRFVcTp4Cd+NTlXtprkfLcTNajpSs3EJt29W2xcg27mhby4ancJNWMcAwwBARFaJQCEQ9SceNp3tpQqKScCrskfLwUy07E4xs4463vavBQF9Xu8VSlcQAwwBDRFQs9x+nYcOpCPx2Pgpp2XkAACtjKQa1rI4hrVxhZ8azm+j1YYBhgCEiKhF5Zg52nIvChlMReJiUAQDQ15WgZyMnjGrjjnpO5lqukKoCBhgGGCKiUsnNU+CfG3FYdzIcF+4/Ua5v6W6FUW3c0amOPXR5ajaVEQYYBhgiIrWFRCVh3clw/H01BnmK/K8LV2sjjHjDDf2aucBYVmkuqUflBAMMAwwRkcZEJ2Xg5+D72Hr2PuSZ+WcxmRro4b0W1THsDTdUszDUcoVUWTDAMMAQEWlcenYufr/wAOtPRSD8URoAQFdHgm71HTCqjTuaVOf16kg9DDAMMEREZUahEAgMjcf6U+E4dfexcn1jFwuMauOO7vUdoMfZfqkUGGAYYIiIXoubMXKsPxmOPSHRypl/ncwNMOwNNwxoUR3mhrxyNhUfAwwDDBHRa5WQkoXNZ+5j85n7eJyWDQAwkuqijYcNmrtZobm7Feo5mfE6TPRSDDAMMEREWpGZk4c/Q6Kx/lQ4bsWmqNxnoK8DbxdLNHezRDM3KzRxtYQJz2SiZzDAMMAQEWmVEAKXHyTj7L3HOBfxBOfvJyIpPUeljY4EqOtkhmauVvl7adwsOfNvFccAwwBDRFSuKBQC9x6l4t/wJzgfkYhz9xMRlZhRqF11KyNlmGnmZoWatsa8wGQVwgDDAENEVO7FJmfi/P1EnAtPxLmIJ7gZK8fz30pWxlI0c7VEczcrNHOzRD0nc0j1OI6msmKAYYAhIqpw5Jk5uBSZlL+HJiIRlyKTkJWrUGljoK+Dxi4WT/fSWKGFuxWvnF2JMMAwwBARVXjZuQpci05+GmjyDz09eW4cjVRPBy3drdCuti3a1baFh50JDzlVYAwwDDBERJWOEAJhCWk493QPzZmwx4hOzlRp42hugHa1bdG2ti1ae9hwHpoKhgGGAYaIqNLLDzSpOBaagBN3HuHsvccqh5x0dSRo7GKBtrVs0c7TFg2qmfNK2uUcAwwDDBFRlZOZk4ez4Yk4cTsBx28n4G58qsr9Fkb68K1li7a1bNCuti1P2S6HGGAYYIiIqryHSRk4cTsBJ24n4OTdR0h5eiXtAl4OpmjnaYt2tWzR1M0SMj0OBtY2BhgGGCIiekZungIhUUk4/jTQXHmYrHLKtpFUFz41rNHO0xZta9nCzcZYe8VWYQwwDDBERPQSiWnZCLqT8DTQPMKj1CyV+6tbGaGFuxWcLQ3hZGGIak8XB3MDnrZdhhhgGGCIiKiYFAqBm7FynLj9CMdvx+PC/SfIyXvx16ONiQzVLAzgZGGoXJ69bW0s5ancpcQAwwBDRESllJqVi+Cwx7gVI0d0cgYeJmUiOikDD59kICMn75WPl+npPA0zBnAyf2YPztO9OY7ci/NCDDAMMEREpGFCCCSl5+BhUgaiC5bkTJXb8SlZhS6HUBQbEynqOJqhbS1b+Na2gae9KffagAGGAYaIiLQiO1eB2OdCTXH24tiayuDrYYM2tfIXO9OqeYo3AwwDDBERlUNCCCRn5CAyMR3/hifi5N1HOHPvMTJzVK/55OVgCt9aNvCtZVulrvfEAMMAQ0REFURWbh4u3H+CoDuPEHQnAdceylXul+rpoIWbFdrUsoFvLRvUcTCDTiWdUZgBhgGGiIgqqMepWTgV9hgn7yQg6M4jxDx3vScbEylae+TvnfGtZQP7SjSjMAMMAwwREVUCBRewDHoaZs7ce4z0bNUxNLXtTdDGI38wcEt3KxhJ9bRUrfoYYBhgiIioEsrOVeBi5BOcfHq46fkZhaW6Omjqaok2tWzgYWeinIDPwki/QpzlpLUA4+bmhvv37xdaP2HCBHz33Xdo3749jh8/rnLf+++/j7Vr1ypvR0ZGYvz48QgMDISJiQmGDRuGhQsXQk+v+ImSAYaIiKqCJ2nZOB32WLmH5mFSRpHtDPV18+eleRpo/puEzwDVLAzhaG4IqZ7Oa66+sOJ+f2t8H9O5c+eQl/ffrq1r166hS5cu6Nevn3LdmDFjMH/+fOVtIyMj5f/z8vLg7+8PBwcHnD59GjExMRg6dCj09fWxYMECTZdLRERUoVkaS+Hf0BH+DR0hhED4ozScvPsIZ+8l4sGTdDxMysSj1Cxk5OQhLCENYQlpRfYjkQC2JrJnAo7BM7MM5/9rWY724pT5IaSpU6di7969uHPnDiQSCdq3b4/GjRvjq6++KrL9/v378eabbyI6Ohr29vYAgLVr12L27NlISEiAVCot1na5B4aIiChfZk6ecm4alflpCuamScpAVq7ilf0Y6OuoXBeqXzNnNHW10mitWtsD86zs7Gxs3rwZ06dPV0lsW7ZswebNm+Hg4ICePXvik08+Ue6FCQ4ORoMGDZThBQD8/Pwwfvx4XL9+Hd7e3kVuKysrC1lZ/12ISy6XF9mOiIioqjHQ14WbjfELr7AthEBiWjaik1Qn4Huo/Dd/L05mjgL3EtJw7+lenNYeNmjq+jqfyX/KNMDs3r0bSUlJGD58uHLdwIED4erqCicnJ1y5cgWzZ89GaGgo/vjjDwBAbGysSngBoLwdGxv7wm0tXLgQ8+bN0/yTICIiquQkEgmsTWSwNpGhgbN5kW0K9uL8F2wy0aBa0W1fhzINMOvWrUP37t3h5OSkXDd27Fjl/xs0aABHR0d06tQJYWFhqFmzZqm3NWfOHEyfPl15Wy6Xw8XFpdT9ERER0X9etRfndSuzAHP//n0cPnxYuWflRVq2bAkAuHv3LmrWrAkHBwf8+++/Km3i4uIAAA4ODi/sRyaTQSaTqVk1ERERVQRldr7Uhg0bYGdnB39//5e2CwkJAQA4OjoCAHx8fHD16lXEx8cr2xw6dAhmZmaoW7duWZVLREREFUiZ7IFRKBTYsGEDhg0bpjJ3S1hYGLZu3YoePXrA2toaV65cwbRp09C2bVs0bNgQANC1a1fUrVsXQ4YMwZIlSxAbG4uPP/4YAQEB3MNCREREAMoowBw+fBiRkZEYOXKkynqpVIrDhw/jq6++QlpaGlxcXNCnTx98/PHHyja6urrYu3cvxo8fDx8fHxgbG2PYsGEq88YQERFR1cZLCRAREVG5Udzvb+3PGUxERERUQgwwREREVOEwwBAREVGFwwBDREREFQ4DDBEREVU4DDBERERU4TDAEBERUYXDAENEREQVTplejVqbCubnk8vlWq6EiIiIiqvge/tV8+xW2gCTkpICAHBxcdFyJURERFRSKSkpMDc3f+H9lfZSAgqFAtHR0TA1NYVEItFYv3K5HC4uLoiKiiqTSxSwf+30zf6113dF778i117R+6/ItVf0/suybyEEUlJS4OTkBB2dF490qbR7YHR0dODs7Fxm/ZuZmZXpNZbYv3b6Zv/a67ui91+Ra6/o/Vfk2it6/2XV98v2vBTgIF4iIiKqcBhgiIiIqMJhgCkhmUyGzz77DDKZjP2/5v4rcu0Vvf+KXHtZ91+Ra6/o/Vfk2it6/2Vde3FU2kG8REREVHlxDwwRERFVOAwwREREVOEwwBAREVGFwwBDREREFQ4DDBEREVU4DDCklvnz5yM9Pb3Q+oyMDMyfP1+j28rMzNRof6Rd9+7d03YJJbJq1SrlezAyMvKVF5ojorLF06iLoV27dhg1ahT69esHQ0PD17rtjIwMtbeZkZEBIQSMjIwAAPfv38euXbtQt25ddO3aVa2+dXV1ERMTAzs7O5X1jx8/hp2dHfLy8tTqX6FQ4Msvv8TatWsRFxeH27dvo0aNGvjkk0/g5uaGUaNGlbjPklyhXBNTZOfl5WH37t24efMmAKBevXp46623oKurW+K+rly5Uuy2DRs2LHH/zwsLC8OGDRsQFhaGr7/+GnZ2dti/fz+qV6+OevXqqdW3jo6O8rPVt29fGBgYqF3v83Jzc3Hs2DGEhYVh4MCBMDU1RXR0NMzMzGBiYlKivvT09BAdHQ07O7sXvu/VMX369GK3XbFihca2W5bu3r2LsLAwtG3bFoaGhhBCqH1turS0NCxatAhHjhxBfHw8FAqFyv2aCsbZ2dkIDw9HzZo1oaenmavuZGRk4MKFC7CyskLdunVV7svMzMSOHTswdOhQtbZRlp/Z5+Xl5eHq1atwdXWFpaWlRvsuFkGvNGXKFGFrayvMzMzE6NGjRXBwsEb7nzRpUpHrU1NTRfv27dXuv0uXLmLNmjVCCCGePHki7O3thbOzszAwMBCrV69Wq2+JRCLi4+MLrT9y5IiwsbFRq28hhJg3b56oUaOG2Lx5szA0NBRhYWFCCCG2bdsmWrVqVao+JRKJ0NHRKdairjt37ojatWsLIyMj4e3tLby9vYWRkZHw9PQUd+/eLXXtxXkO6jp27JgwNDQUnTt3FlKpVPmzX7hwoejTp4/a/V+6dElMnjxZ2NraCnNzczF27Fhx9uxZtfstEBERIby8vISRkZHQ1dVV1j958mTx/vvvl7g/FxcXsXr1ahERESEkEom4cOGCuH//fpFLabRv315lMTMzU3nfGBsbCzMzM9GhQ4dS9f+syMhIERUVpbx99uxZMWXKFPH999+r3bcQQjx69Eh06tRJ+T4t+NmPGDFCTJ8+Xa2+BwwYIBwdHcWsWbPEypUrxVdffaWyqCstLU2MHDlS6OrqqrxvJk6cKBYuXFjqfkNDQ4Wrq6vyZ9K2bVsRHR2tvD82Nlbtz21Zf2anTJkifvrpJyGEELm5uaJ169ZCIpEIY2NjERgYqHb/JcUAU0w5OTni999/F2+99ZbQ19cXderUEUuXLhWxsbFq912jRg3x6aefqqxLTU0Vbdq0EW3atFG7f2tra3Ht2jUhhBA//vijaNiwocjLyxM7duwQXl5eperTwsJCWFpaCh0dHeX/CxYzMzOho6MjJkyYoHbtNWvWFIcPHxZCCGFiYqL8QN68eVNYWFiUqs9jx44pl40bNwoHBwfx4Ycfij179og9e/aIDz/8UDg6OoqNGzeqXX/37t1Ft27dxOPHj5XrHj16JLp16yZ69OhR4v4iIiKUy65du0TNmjXF2rVrxeXLl8Xly5fF2rVrRa1atcSuXbvUrr1Vq1Zi+fLlQgjVn/3Zs2dFtWrV1O6/QMFnq2fPnkJfX1/Uq1dPLF++vMhgXBK9evUSgwcPFllZWSr1BwYGCg8PjxL39/333wupVPrS0Fjw5aSu5cuXi549e4rExETlusTERNGrVy+xbNkytftv06aN+Pnnn4UQQsTExAgzMzPh4+MjbGxsxLx589Tuf8iQIcLPz09ERUWp/OwPHDgg6tatq1bf5ubm4uTJk2rX+CKTJ08WTZs2FUFBQcLY2FhZ++7du0Xjxo1L3W/v3r2Fv7+/SEhIEHfu3BH+/v7C3d1dGXg1EWDK+jNbrVo1ce7cOSGEELt27RJOTk4iNDRUfPzxx+KNN95Qu/+SYoAphbi4OPH5558LAwMDoa+vL3r16iWOHDlS6v7u3r0rHB0dxcqVK4UQQsjlcuHj4yN8fX1Famqq2vUaGhoqPyT9+vUTc+fOFULk/xVmaGhYqj43btwoNmzYICQSifj666/Fxo0blcvWrVvF6dOn1a5bCCEMDAxERESEEEL1A3n9+nVhbGysdv8dO3YUW7duLbR+y5Ytol27dmr3b2RkJK5cuVJofUhIiNr1N2/eXOzbt6/Q+n379okmTZqo1bcQQhgbG4t79+4JIVR/9uHh4UImk6nd//MyMzPFihUrhEwmExKJRMhkMjFkyBCVv1JLwsrKSty6dUsIUbj+0r7v5XK5uHr1qpBIJOLIkSMiJCSkyEVdTk5Oyj86nnX16lXh6Oiodv8WFhbKn83XX3+t/PI5ePCgcHd3V7t/e3t75c/h2Z99WFiY2u97Nzc3cePGDbVrfJHq1asr97I/W/udO3eEqalpqfu1s7NT+V2gUCjEuHHjRPXq1UVYWJhGAkxZf2ZlMplyz92YMWPElClThBBC3Lt3T62fTWlp5sBeFfLvv/9iw4YN2LZtG+zs7DB8+HA8fPgQb775JiZMmIBly5aVuM+aNWviwIED6NChA3R0dPDrr79CJpNh3759MDY2VrtmDw8P7N69G2+//TYOHjyIadOmAQDi4+NLPcZj2LBhAAB3d3e0bt1aY8eIn1e3bl0EBQXB1dVVZf3OnTvh7e2tdv/BwcFYu3ZtofXNmjXD6NGj1e5fJpMhJSWl0PrU1FRIpVK1+r569Src3d0LrXd3d8eNGzfU6hsALCwsEBMTU2gbly5dQrVq1dTuv8D58+exfv16bNu2DcbGxvjggw8watQoPHjwAPPmzUOvXr3w77//lrhfhUJR5BisBw8ewNTUtMT9rVq1CmPHjkX9+vWxYcMG+Pj4lNmYOLlcjoSEhELrExISinw/lVROTo7yGjaHDx/GW2+9BQDw8vJCTEyM2v2npaUpx9w9KzExUe1r53z++ef49NNPsWnTpiK3oa6EhIQixzalpaWpNX4nIyND5fekRCLBmjVrMHHiRLRr1w5bt24tdd8Fyvoza29vjxs3bsDR0REHDhzAmjVrAADp6emlGtOnttcemSqguLg4sWzZMlGvXj0hlUpFnz59xP79+4VCoVC2KdjdqI7Tp08LY2Nj0bFjR5Genq5u2Uq//fab0NfXFzo6OqJz587K9QsWLBDdunVTu/+7d++Kjz76SAwYMEDExcUJIYT4+++/i/wLsqR2794tzM3NxaJFi4SRkZFYunSpGD16tJBKpeKff/5Ru//atWuLmTNnFlo/c+ZMUbt2bbX7HzJkiKhXr544c+aMUCgUQqFQiODgYFG/fn0xbNgwtfr29vYWQ4YMEVlZWcp1WVlZYsiQIcLb21vNyoWYMWOGaNOmjYiJiRGmpqbizp074uTJk6JGjRrKvXjqWL58uahfv75yL+Zff/0l8vLyVNpERUUJXV3dUvXfv39/MWbMGCFE/l+j9+7dEykpKaJjx45i+PDhJe5PV1dX+f7W0dFR/r8sDBkyRLi5uYnff/9dREVFiaioKLFz507h7u4uhg4dqnb/LVq0ELNnzxYnTpwQBgYGyr0lwcHBGjnU0L17d/Hxxx8LIf772efl5Yl+/fqpPRajcePGwtTUVJiYmIj69esrxwgVLOry9fUVq1atUqldiPwxMH5+fqXut3nz5srDds8LCAgQFhYWau+BKevP7GeffSbMzc2Fl5eXqF69usjMzBRCCLFu3bpSj0lUBwNMMejr6wsvLy+xZMmSFx6XT05OLtGA28aNGxf64Hl7ewsrKyvh5eWl0Q+kEPnHuS9evKjyBXH27Flx8+ZNtfot60FjQghx4sQJ0blzZ2FraysMDQ1F69atxcGDBzXS9759+4SBgYGoX7++GDVqlBg1apRo0KCBMDAwKPLwTEk9efJEvPXWW0IikQipVCqkUqmQSCSid+/e4smTJ2r1ffbsWWFnZydsbW1Fp06dRKdOnYStra2ws7PTyGDYrKwsMXr0aKGnpyckEokyBA8ePFjk5uaq3b+Hh4dYsGDBSw8RZWVllXosUlRUlKhbt66oU6eO0NPTE61atRLW1tbC09OzVOGjrAfxPistLU2MHz9eyGQy5fgaqVQqxo8fr5HDyoGBgcovzBEjRijXz5kzR7z99ttq93/16lVhZ2cnunXrJqRSqejbt6+oU6eOsLe3L9Xg9WfNnTv3pYu6goKChImJiRg3bpwwMDAQU6ZMEV26dBHGxsbi/Pnzpe53wYIFonv37i+8f/z48UIikZS6fyHK/jMrhBA7d+4UK1asUBkEvnHjRrFnzx6N9F8SPI26GIKCguDr66vRPufNm1fstp999plGtlkWpzT6+PigX79+mD59OkxNTXH58mXUqFED//77L9555x08ePBAI7WXpaioKKxZswa3bt0CANSpUwfjxo2Di4uLxrZx9+5d5WnUderUgYeHh0b6TUtLw5YtW1RqHzhwoEYOPRaIjIzEtWvXkJqaCm9vb9SqVUtjfZe13NxcbNu2DVeuXEFqaiqaNGmCQYMGlerQzw8//IBJkyYhNzf3hW0KPlPqTh9QIC0tDWFhYQDyDzVr8nXNy8uDXC5XOf01IiICRkZGGjk9PDk5Gd9++y0uX76s/NkHBATA0dFR7b7LWlhYGBYtWqRS++zZs9GgQQNtl1YsZfWZfdXcXp9++qlGtlNcDDDFpMn5JJ6Vl5eHU6dOoWHDhrCwsNBcwc94/Pgx+vfvj8DAQEgkEty5cwc1atTAyJEjYWlpieXLl5e6bxMTE+VYjGcDTEREBLy8vDQ2+Vx2dnaRcz5Ur15dI/2XlRfN7SGRSGBgYAAPDw/06tULVlZWr7my8iM9PR2RkZHIzs5WWa+JeWw0LSUlBffv30fDhg1x+PBhWFtbF9muUaNGGttmwR8Bzs7OGuuzoktKSsLOnTsRFhaGmTNnwsrKChcvXoS9vb3aYz2uXbuG+vXrF3nf7t270bt3b7X6r8ieH3eYk5OD8PBw6OnpoWbNmrh48eJrrYeDeIvh/v376NatGyIjI5GVlYUuXbrA1NQUixcvRlZWVpGDQItLV1cXXbt2xc2bN8sswEybNg36+vqIjIxEnTp1lOvfffddTJ8+Xa0AU9aDxu7cuYORI0fi9OnTKuvV+Uv3dU4Gd+nSJVy8eBF5eXnw9PQEANy+fRu6urrw8vLC6tWrMWPGDJw8ebLQxFbFcefOHQQGBhYZ7tT9a0gIgZ07d76w/z/++EOt/hMSEjB8+HAcOHCgyPtL89r++eefxW5bMHC1JExNTZWDeFu3bq32gNQXUSgU+OKLL7B8+XKkpqYqtz1jxgx89NFH0NEp+STqTZo0wZEjR2BpaQlvb++X7n0tzRfR6/pcXblyBZ07d4a5uTkiIiIwZswYWFlZ4Y8//kBkZCR+/vnnUvcNAH5+fjh58mSh32m///47hg4dirS0NLX6L0sjR4586f3r169Xq/9Lly4VWieXyzF8+HC8/fbbavVdGgwwxTBlyhQ0a9YMly9fVvmL6+2338aYMWPU7r9+/fq4d+9ekWeUaMI///yDgwcPFvoLrlatWrh//75afQ8YMACzZ8/Gb7/9BolEAoVCgVOnTuGDDz5Qe0ZJABg+fDj09PSwd+9eODo6qn3ICwAaN24MiUTyyqngNXEooGDvyoYNG5RnfCUnJ2P06NFo06YNxowZg4EDB2LatGk4ePBgifr+8ccfMX78eNjY2MDBwUHlZyORSNQOMFOnTsX333+PDh06wN7eXiM/++f7T05OxtmzZ9G+fXvs2rULcXFxyi/u0ijuX8fqvrYFZ+GVlY8++gjr1q3DokWL0Lp1awDAyZMnMXfuXGRmZuLLL78scZ+9evVSBq6y2Ivw7Ofq2fdKwefs2XXq/OynT5+O4cOHY8mSJSpnk/Xo0QMDBw4sdb8FRo8ejc6dO+PUqVNwcHAAAGzfvh0jR47Exo0b1e6/LD158kTldk5ODq5du4akpCR07NixTLZpZmaGefPmoWfPnhgyZEiZbOOFXvuomwqoLOaTeNb+/ftF48aNxV9//SWio6NFcnKyyqIuExMTcfv2beX/C+o/d+6csLKyUqvvogaNSSQSjQ0aMzIyUnug8fOenQzuVYu6nJycxPXr1wutv3btmnBychJCCHHhwgVhbW1d4r6rV68uFi1apHaNL2JpaamRgcwv4uDgoBxsbGpqKkJDQ4UQQuzZs0e0bt26zLarCbm5uWLp0qWiefPmwt7eXmUiR0tLS7X7d3R0LHJQ5O7du5Xvm9LKzc0Vx48fV3sQ+fNe1ySLZmZmyoHAz/4+i4iI0Nj8RBMnThT16tUTjx8/Flu2bBGGhoZi586dGun7dcvLyxNjx44VixcvLrNtBAUFlXpiUXVwD0wxaHo+ief16NEDQP4u7ef/ctHEXgBfX1/8/PPP+PzzzwFAuadkyZIl6NChg1p9S6VS/Pjjj/j0009x9epVjQ8aq1u3Lh49eqSRvgo8P6dMWUpOTkZ8fHyhw0MJCQnKazJZWFgUGv9RHE+ePEG/fv00UmdRzM3NUaNGjTLrPy0tTTlY1NLSEgkJCahduzYaNGjw2o+ll9S8efPw008/YcaMGfj444/x0UcfISIiArt379bIQMbExER4eXkVWu/l5YXExES1+i6rw9bPfq769euHVatWKX+3AfmHjVxcXPDJJ5+otQdIJpMVeT2z27dvw9bWttT9Puubb77BoEGD0KpVKzx8+BC//vorevXqpZG+XzcdHR1Mnz4d7du3x6xZs9Tqa9WqVSq3hRCIiYnBL7/8gu7du6vVd2kwwBRD165d8dVXX+GHH34AkB8AUlNT8dlnn6l8QEsrMDBQ7T5eZsmSJejUqRPOnz+P7OxszJo1C9evX0diYiJOnTqlVt9FDVI9c+aMxgapLl68GLNmzcKCBQvQoEED6Ovrq9yv7sUWX3W8XN3DYL169cLIkSOxfPlyNG/eHABw7tw5fPDBB8pf4v/++y9q165d4r779euHf/75B+PGjVOrxheZO3cu5s2bh/Xr15fJhG2enp4IDQ2Fm5sbGjVqhO+//x5ubm5Yu3atxs5UOXLkCFauXKlyBtjUqVPRuXNntfrdsmULfvzxR/j7+2Pu3Ll47733ULNmTTRs2BBnzpzB5MmT1eq/UaNG+Pbbbwt9YXz77bcaGSBc1oety3KSxbfeegvz58/Hjh07AOT/Po6MjMTs2bPRp0+fUvVZ1Nipd955B0FBQXjvvfcgkUiUbUozdkrbwsLCXnr2XHGtXLlS5baOjg5sbW0xbNgwzJkzR+3+S4pnIRXDgwcP4OfnByEE7ty5g2bNmuHOnTuwsbHBiRMnNHpF2rJSVqc0dujQ4aWDVENDQyGRSEo9SPXZwYplsXfq+Suo5uTkID09HVKpFEZGRmr/tZuamopp06bh559/Vv4C0dPTw7Bhw7By5UoYGxsjJCQEQP4YgpJYuHAhVqxYAX9//yLDnbpfohkZGXj77bdx6tQpuLm5Fepf3b0kmzdvRm5uLoYPH44LFy6gW7duePz4MaRSKTZt2oR3331Xrf5Xr16NKVOmoG/fvvDx8QGQH6537tyJlStXIiAgoNR9Gxsb4+bNm6hevTocHR2xb98+NGnSBPfu3YO3tzeSk5PVqv348ePw9/dH9erVlbUHBwcjKioKf//9t9rTOhw4cABz5szB559/jqZNmxY6PVvdPwyaNGmC+vXr46efflLOOJ2dnY3Ro0fj2rVrar13kpOT0bdvX5w/fx4pKSlwcnJCbGwsfHx88Pfff5fqVPPiDorW5CnyZeH5PygL9pDs27cPw4YNw7fffqulysoGA0wxaXI+iRepSKeTFvjqq68QFBT0ykGqGRkZJR6kCuT/In+Zdu3alarul7lz5w7Gjx+PmTNnws/PTyN9pqam4t69ewCAGjVqqHXqfYGX/fUskUiU2yutglPv+/btW+QgXk3NT1QgPT0dt27dQvXq1WFjY6N2f87Ozvjwww8xceJElfXfffcdFixYgIcPH5a6b09PT/z8889o2bIl2rRpgzfffBMffvghtm/fjkmTJiE+Pl7d8hEdHY3vvvtOZY6fCRMmwMnJSe2+y/oPg3///Rc9e/aEEEL5++vKlSuQSCT466+/0KJFC7X6B/IHNT/7+1jdvWqVwfNDAgr2kHTs2BEjR44ss0u+aAsDTDmQkJCAESNGYP/+/UXeX55PFa5WrRoOHTpUaO/K9evX0bVrVzx8+BAXL15E165dSz2WJSkpCevWrVMeBqhbty5GjRoFc3PzUtf9KufPn8fgwYOVXx5VkbGxMQ4ePIg2bdporM8XzYtTlBUrVqi1LRMTE4SEhBSaNPDOnTvw9vZWnp5cGh9++CHMzMzwv//9D9u3b8fgwYPh5uaGyMhITJs2DYsWLVKr9rL2Ov4wKKtJFqOiojQ6ySRVXJUrjpWhspxvY+rUqUhKStLo6aSv65TGshykCuQHiW7dusHAwED5V9vKlSuxYMEC/PPPP2jSpEmpa38ZPT09REdHl0nfFYWLi4vahxKe9/w8EhcvXkRubm6hw49NmzZVe1tvvfUWdu3ahZkzZ6qs37NnD9588021+n42oLz77rtwdXXF6dOnUatWLfTs2VOtvgs8H9zr1auHkSNHaiS4l8Wey+cZGxtj7NixGu/Xzc0Nbdq0weDBg9G3b99Ch4FLo+BCnQYGBoXGHT1P3UOzZa2sJl0tj7gHphheNd+GumMBHB0dsWfPHrRo0QJmZmY4f/48ateujT///BNLlizByZMnS9zns/O7XLp0CR988AFmzpypcjx9+fLlWLJkiVpnBAwaNEjZ1/ODVN944w388ssv2LZtG5YtW4bz58+XuH9fX194eHjgxx9/VO7+zM3NxejRo3Hv3j2cOHGi1LUDhQfvFRwz/vbbb+Hi4vLCvWLlxYMHD/Dnn38WeehR3T0Y+/btwzfffIO1a9fCzc1Nrb6KsmLFChw7dgybNm1Sfgk9efIEI0aMgK+vL2bMmKFW/1988QWWLVuG1q1bq4yBOXXqFGbMmKESzkr6pXTixAm88cYbhXbJ5+bm4vTp02jbtq1atZ8/fx5+fn4wNDRUBvdz584hIyNDo8Fdk4et//zzT3Tv3h36+vqvnFBQnYGwly5dwtatW7Ft2zYkJCSgW7duGDx4MHr27FnqiQXd3d1x/vx5WFtbl/mh2bL0/KSrt2/fRo0aNTBlyhS1J10tl173edsVUVnPt2FqairCw8OV2zp58qQQQoh79+5pZJ6Z5s2bFzmfx759+0STJk3U6jslJUV5dehnLzo3ZswY5UXnLl26JC5dulSq/g0MDIqcB+b69esa+dlIJBKVRUdHR9jb24v33nvvpRcZLA8OHz4sjIyMRP369YWenp5o3LixsLCwEObm5qJDhw5q929hYaF8XU1MTDQ+14mTk1ORVyy/evWqcHR0VLt/Nze3Yi3u7u4l7vtFV6N+9OiR2lcUFkKINm3aiOHDh4ucnBzlupycHDFs2DDh6+urdv/x8fHC399f+Zl9fikNiUSi/Jk8/7l6/jOmCQqFQhw9elSMHj1aWFpaCnNzc5ULU1ZFvXr1EoMHDxZZWVkqc+QEBgYKDw8PLVeneTyEVAxlPd9GWZ9OWpanNJqYmODHH3/EypUrXzhItaRn1zzLzMwMkZGRhebEiIqK0sgcPM8fDqxI5syZgw8++ADz5s2Dqakpfv/9d9jZ2WHQoEHo1q2b2v1/9dVX6hf5EnK5HAkJCYXWJyQkICUlRe3+w8PD1e7jRcQLLoT6+PFjjVxw8fz58yp7HYH8w5qzZs1Cs2bN1O6/LA5bF3yWcnJy0L59e6xdu7ZU0wMUl0QiQYcOHdChQweMHz8eo0aNwqZNm0o1XX5xx2ZJJBK1Lr1S1oKCgnD69GnlmV8F3Nzc1Bq0Xl4xwBRDWc+3MWXKFMTExADIP7OjW7du2Lx5s/J0UnXVqVMHCxcuLHRK48KFC1WujaQOExOTMjlb6t1338WoUaOwbNkyvPHGGwCAU6dOYebMmXjvvfc0ui1RxPig8uzmzZv49ddfAeR/uWVkZMDExATz589Hr169MH78eLX6L+vp8t9++22MGDECy5cvVx4mOXv2LGbOnIl33nlHo9vS1GtbUJdEIsHw4cNVDlnk5eXhypUryvepOso6uB89ehR79uxBs2bNoKOjA1dXV3Tp0gVmZmZYuHAh/P39S923vr4+rl69WqrrNZXEgwcPsHXrVmzduhXXrl2Dj48Pvvvuu1L1VdQ1fopS3n83lPWkq+UNA0wxeHh44JNPPsGZM2fKZL6NwYMHK//fpEkT3L9/X6Onk65duxY9e/aEs7Nzkac0lmfLli2DRCLB0KFDlfOo6OvrY/z48Ro70+Pnn3/G0qVLcefOHQBA7dq1MXPmzNd/XY8SMjY2Vo5dcHR0RFhYGOrVqwcApT7jSy6XK8eGFDXb6bPUHeC7du1afPDBBxg4cCBycnIA5AexUaNGYenSpWr1XUDTr23BAFohBExNTVWmUZBKpWjVqpVGro9W1sG9rGdBHjx4MH766acyORvr+++/x9atW3Hy5EnUqVMHgwYNwp49e9SaYbusJxN9Xcp60tVyR7tHsCoGTR8/L8pPP/0k6tWrJ6RSqZBKpaJevXrixx9/1EjfQgiRmpoqvv/+ezFt2jQxbdo08cMPPyjHqFQEaWlp4sqVK+LKlSsiLS1NY/0uX75cGBkZiVmzZok9e/aIPXv2iJkzZwojIyOxYsUKjW2nLPTq1Uv88MMPQgghZsyYITw8PMQXX3whmjRpIjp16lSqPp8d21EwXuH5RZPjGITIf28WXC9Hk+/JsnxtZ86cqfI+DA8PFytXrhQHDhwodZ+XL18WeXl5Qoj8a4xNnjxZZWyZTCYTU6dOFZmZmWrVLoQQzZo1U9bas2dPMWTIEPHgwQMxa9YsUaNGDbX7nzhxojAzMxNNmzYVY8eOVf7eKVjU4ezsLGbOnClCQkLUrrOyiYqKEnXr1hV16tQRenp6olWrVsLa2lp4enoWOWarouNZSOXAp59+ihUrVmDSpEkqZwl9++23mDZtGubPn6/lCisvd3d3zJs3r9AlAzZt2oS5c+eW6TgKdd27dw+pqalo2LAh0tLSMGPGDOWpvCtWrCjVX6THjx9H69atoaenh02bNsHFxQW6uroqbRQKBSIjI8v8EJO6yvK17dKlC/r06YNx48YhKSkJXl5e0NfXx6NHj7BixYpSHb7T1dVFTEwM7OzsUKNGDZw7dw6GhoYICwsDANSsWRNGRkalrvlZZT0L8suusSaRSHD06NFS9y2EwMmTJ/H999/j3r17+O2331CtWjX88ssvcHd31+i8RRXR65h0tdzQcoCqkHJzc8WlS5dEYmKiRvqzsbERW7duLbR+69atpbpKsRD5V/TNzs5W/v9lS1Umk8nEnTt3Cq2/ffu2xq5sW1GV9Zk2Za0sX1tra2vlGVQ//vijaNiwocjLyxM7duwQXl5eperTyspKnDlzRgiRv/crPj5erRqLS6FQiLS0NHHhwgWRkJDwWrapjp07dwpDQ0MxevRoIZPJlGfafPPNN6J79+5aro5eJ46BKYapU6eiQYMGGDVqFPLy8tC2bVsEBwfDyMgIe/fuRfv27dXqPycnp8gzC5o2bVrqC3D17t0bsf9v796DoqrfP4C/FwxMAUFB8S4XUdRgBCqNlkXNSzXFsM6ioe7IRWe0RFe3cjRM08ZmFEjGSQdGLpkMGaaTqbSTsVxsXFIuomkgjhc0Ax0UgQld3O8fjPuTEH+onD174P36S4+H47OD47w5n8/neW7exODBg5/a58XaZ3sIzdvbG/v378e6devaXf/++++7baK2VJk6OWnT2NiIvn37ilDRsxHye9vc3GzeFKnT6aBUKmFjY4MpU6a068H0LObOnQuFQoGhQ4dCJpMhKCiow9uvR7qjF8mePXuQlJRk3h80duxYrFq1CrGxsS/8bCFt2bIFu3fvhlqtRnZ2tvl6cHAwtmzZImJl4hN6OK21YYDpgpycHPNG28OHD+Py5cu4cOEC9u7di/Xr17/wROdFixZh165dHRqPpaSkYMGCBc/1TEsfaZSqTZs2Yd68eSgoKEBwcDCAts2Sx48fN0+7tSYuLi5dPgnxvIMoHx0plclkiI+Pb7ds0draCoPB8EJH4y1FyO+tt7c3Dh06hPDwcPzyyy/QaDQAgNra2ufe3JySkgKlUomLFy8iLi4OS5YsEezkSGfL1hqNBlevXrXqZeu//vrriY0CBwwYgDt37li+ICuycuXKdr//73BaBphe6NatW3B3dwcAHD16FCqVCj4+PoiOjsaOHTu65e/Ys2cPdDodpkyZAqDtOOnVq1ehVqvb9Sh41u6qljrSKFVz585FcXExEhMTcejQIQBtx86Li4sxefJkcYt7AqF7swD/d6TUZDKhoqKiXU8JOzs7+Pv7Q6vVCl7Hi5o7dy4MBgOSkpK6/Xu7YcMGREZGQqPRYMaMGeYQoNPpXujZj/r3nD59GitXrhQswOzatQupqantTjS9//778PPzw4oVK6w6wLi7u+PixYsdukMXFRXB09NTnKKsRH19fYdrjw+n7Wm4ibcLRo8ejdTUVMyYMQMeHh7YtWsX3n33XZw7dw5vvvnmE//RPIunbXh73PNuftNoNLC3t7f6AXNiUKvVmDZtGkJCQuDl5SV2OVYlKioKO3bs6PZ5SD3FzZs38ffff8Pf39/8A0JxcTGcnJw69G+xNs7Ozvjjjz86LKVVVlbitddes+o3GVu3bsV3332HtLQ0zJw5E0ePHsWVK1eg0WgQHx+PFStWiF2i1empw2n5BqYLoqKiEBERYV6bfjS23WAwdMt/VEL3IDAajUhLS8Ovv/6KwMDADp1CX3RmjpTZ2dlh69atiI2NxbBhw6BQKBAaGgqFQmH1e2AUCgViYmKgUqkEOWGQnp7e7c+0tOrqaqSnp+PSpUv4+uuvMXjwYBw7dgyjRo0y98x5Xu7u7uY3s488ashn7YRYtraUtWvX4uHDh5gxYwaam5sREhICe3t7aLVahpdO9NThtHwD00U5OTm4du0aVCoVRowYAaDtOKazszPCwsJEru7phDzS2FNcv34dBQUFyM/PR35+PiorKzF06FDU1NSIXVqnVq1ahaysLLS0tCAiIgIxMTHmJUhqOxL+9ttvIzg4GAUFBTh//jw8PT3x1Vdf4dSpU8jJyRG7RNGsWLEC3377LUaOHPnEZevHm3Va6w849+/fx8WLF9HY2IgJEyb0uEnLz0Pqw2mfFQMMEdpOlRQVFSEvLw96vR4lJSWYMGFCl1uMi8VoNOKnn35CZmYmjh07Bm9vb0RHR2PRokUYMmSI2OWJaurUqVCpVFi9ejUcHR1RXl4OT09PFBcXQ6lUWnU4FZrQy9Ykjv/udZTJZHBzc8P06dORkJDQLbP1rAkDTCeSk5OxdOlS9O3bF8nJyU+990VHCZB41q1bB71ej9LSUvj6+pqXkEJCQuDi4iJ2ec+ktrYWKSkp+PLLL9Ha2op33nkHcXFxmD59utilicLBwcE8yPTxAHP58mWMHz8e//77r9glEtELYIDphIeHB06dOoVBgwY9cZLzIzKZrFt6MpA4bGxs4ObmBo1GA6VSKdmj5sXFxUhPT0d2djacnJywePFiXL9+HVlZWVi+fDm2b98udokWN2LECOzfvx9vvPFGuwBz8OBBaLVac4dbop6iq1O1AetdGnwWDDDUq5WXlyM/Px96vR6FhYWws7Mzv4UJDQ216kBTW1uLvXv3Ij09HVVVVXjvvfcQGxuL2bNnm3vFFBUVYc6cOWhsbBS5WsvTarUwGAz44Ycf4OPjg5KSEvzzzz9Qq9VQq9X4/PPPxS6RqFtNmzYNJSUlMBqNGDduHIC2k2W2trYICAgw39dTlgYZYDrR1SQrk8mQkJAgcDVkKeXl5UhKSsK+ffs6HU1vLezs7ODl5YXo6GgsXrwYbm5uHe5paGhAWFhYj5m2+yzu37+PDz/8EBkZGWhtbUWfPn1gNBqxYMECZGRkdNrllkiqEhMTodfrkZmZaV4Cr6+vR1RUFORyOdasWSNyhd2LAaYT/93k1lmqDQwM7BFJtrcymUwoLS2FXq+HXq9HUVERGhoa4OfnB4VCgaSkJLFL7FRhYSHkcrnYZVi9a9euoaKiAk1NTZg8eTK8vb3FLolIEMOHD4dOp+vQIuDs2bOYNWtWjztKzT4wnXj8J9bExEQ4Ojp2mmpJugYOHIjGxkb4+/tDoVBgyZIlkMvlcHZ2Fru0/5dcLofRaIRer0d1dTUiIyPh6OiIGzduwMnJicdKId15P0TPo6GhAXV1dR2u19XV4d69eyJUJCy+gemC3pZqe5MjR45ALpdLstvslStXMGfOHFy9ehUtLS2orKyEp6cnVq5ciZaWFuzevVvsEkXV2byfnTt3QqPRWHW7fKLnoVarUVhYiISEBHNTRYPBgI8//hhyuRyZmZkiV9jNLDn6WqocHBxMeXl5Ha7/9ttvJgcHB8sXRGQymcLCwkwLFy40tbS0mBwcHEzV1dUmk8lkysvLM3l7e4tcnfhcXV1NWVlZHa5nZWWZBg0aJEJFRMJqamoyLVu2zGRvb2+ysbEx2djYmOzs7EzLli0zNTY2il1et+MSUheEh4cjKirqialWqVSKXB31VoWFhfj999/bDVsEgDFjxuD69esiVWU9Hjx4gKCgoA7XAwMDYTQaRaiISFj9+vXDN998g23btpnbBHh5eXUYH9NTMMB0we7du6HVahEZGYkHDx4AaJstERMTg23btolcHfVWnZ2SqqmpEWyKsZRIed4P0Yvo378//Pz8xC5DcNwD8wyampp6RaolaZg3bx4GDBiAlJQUODo64syZM3Bzc0NYWBhGjRrVI4YxvoieMO+HiDrHAEMkUTU1NZg9ezZMJhOqqqoQFBSEqqoquLq6oqCgAIMHDxa7RFFx3g9Rz8YAQyRhRqMR2dnZOHPmDBobGxEQEIAFCxbg5ZdfFrs0IiJBMcAQERGR5HATL5GEVVVVIS8vD7W1tXj48GG7P9uwYYNIVRERCY9vYIgkKjU1FcuWLYOrqyvc3d3NAxyBtn0dJSUlIlZHRCQsBhgiiRo9ejSWL1+OTz/9VOxSiIgsjgGGSKKcnJxQVlYGT09PsUshIrI4G7ELIKLno1KpoNPpxC6DiEgU3MRLJFHe3t6Ij4/HyZMn8corr7RrzAYAcXFxIlVGRCQ8LiERSZSHh0enfyaTyXDp0iULVkNEZFkMMERERCQ53AND1EO0trairKwM9fX1YpdCRCQ4BhgiiVq1ahX27NkDoC28hISEICAgACNHjoRerxe3OCIigTHAEElUTk4O/P39AQCHDx/G5cuXceHCBWg0Gqxfv17k6oiIhMUAQyRRt27dgru7OwDg6NGjUKlU8PHxQXR0NCoqKkSujohIWAwwRBI1ZMgQ/Pnnn2htbUVubi5mzpwJAGhuboatra3I1RERCYt9YIgkKioqChERERg6dChkMhneeustAIDBYMD48eNFro6ISFgMMEQStXHjRkyaNAnXrl2DSqWCvb09AMDW1hZr164VuToiImGxDwwRERFJDt/AEElIcnIyli5dir59+yI5Ofmp93KUABH1ZHwDQyQhHh4eOHXqFAYNGsRRAkTUqzHAEBERkeRwCYlIQlavXt2l+2QyGRISEgSuhohIPAwwRBJSWlra7vclJSUwGo0YN24cAKCyshK2trYIDAwUozwiIothgCGSkLy8PPOvExMT4ejoiMzMTLi4uAAA6uvrERUVBblcLlaJREQWwT0wRBI1fPhw6HQ6TJw4sd31s2fPYtasWbhx44ZIlRERCY+jBIgkqqGhAXV1dR2u19XV4d69eyJURERkOQwwRBIVHh6OqKgo/Pjjj6ipqUFNTQ0OHDiAmJgYKJVKscsjIhIUl5CIJKq5uRlarRZpaWl48OABAKBPnz6IiYnBtm3b0L9/f5ErJCISDgMMkcQ1NTWhuroaAODl5cXgQkS9AgMMERERSQ73wBAREZHkMMAQERGR5DDAEBERkeQwwBBRr5GRkQFnZ2exyyCibsAAQ0RERJLDAENEPc79+/fFLoGIBMYAQ0QW9/PPP8PZ2Rmtra0AgLKyMshkMqxdu9Z8T2xsLBYuXAgAOHDgACZOnAh7e3uMGTMGCQkJ7Z43ZswYbN68GWq1Gk5OTli6dCmAtiWjUaNGoV+/fggPD8ft27ct9AmJSGgMMERkcXK5HPfu3UNpaSkAID8/H66urtDr9eZ78vPzERoaitOnTyMiIgLz589HRUUFNm7ciPj4eGRkZLR75vbt2+Hv74/S0lLEx8fDYDAgJiYGH330EcrKyjBt2jRs2bLFgp+SiITERnZEJIrAwEB88MEH0Gq1CA8Px6uvvopNmzbh9u3buHv3LkaMGIHKykps3LgRdXV10Ol05q/95JNPcOTIEZw7dw5A2xuYyZMn4+DBg+Z7IiMjcffuXRw5csR8bf78+cjNzcWdO3cs9jmJSBh8A0NEolAoFNDr9TCZTCgsLIRSqYSvry+KioqQn5+PYcOGYezYsTh//jyCg4PbfW1wcDCqqqrMS1AAEBQU1O6e8+fP4/XXX293berUqcJ9ICKyqD5iF0BEvVNoaCjS0tJQXl6Ol156CePHj0doaCj0ej3q6+uhUCie6XmcAUXUu/ANDBGJ4tE+mKSkJHNYeRRg9Ho9QkNDAQC+vr44ceJEu689ceIEfHx8YGtr2+nzfX19YTAY2l07efJk934IIhINAwwRicLFxQV+fn7Yt2+fOayEhISgpKQElZWV5lCzZs0aHD9+HJs3b0ZlZSUyMzOxc+dOaLXapz4/Li4Oubm52L59O6qqqrBz507k5uYK/bGIyEIYYIhINAqFAq2treYAM3DgQEyYMAHu7u4YN24cACAgIAD79+9HdnY2Jk2ahA0bNuCLL77A4sWLn/rsKVOmIDU1FTt27IC/vz90Oh0+++wzgT8REVkKTyERERGR5PANDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSQ4DDBEREUkOAwwRERFJDgMMERERSc7/AH29D2GOj71lAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Word Cloud"
      ],
      "metadata": {
        "id": "5-wSR4SCN6Zd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud\n",
        "\n",
        "# Generate a word cloud image\n",
        "wordcloud = WordCloud().generate(' '.join(df_Paris_low['Review_Text']))\n",
        "\n",
        "# # Display the generated image:\n",
        "# # the matplotlib way:\n",
        "# import matplotlib.pyplot as plt\n",
        "# plt.imshow(wordcloud, interpolation='bilinear')\n",
        "# plt.axis(\"off\")\n",
        "\n",
        "# lower max_font_size\n",
        "wordcloud = WordCloud(max_font_size=40).generate(' '.join(df_Paris_low['Review_Text']))\n",
        "plt.figure()\n",
        "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "a59a663c-ae2d-4d25-e856-6bf4778b729e",
        "id": "R6k0DWpQN6Zd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Tokenizer"
      ],
      "metadata": {
        "id": "wrgqNwl2N6Ze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_Paris_low['Review_Text'] = df_Paris_low.apply(lambda row: nltk.word_tokenize(row['Review_Text']), axis=1)\n",
        "df_Paris_low.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9ae34045-6089-4105-b248-947c7d341a90",
        "id": "yZet5DWVN6Ze"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Review_ID  Rating Year_Month Reviewer_Location  \\\n",
              "0  670216406       2     2019-4            Greece   \n",
              "1  669337300       1     2019-4            Greece   \n",
              "2  669121201       2     2019-4       Switzerland   \n",
              "3  668207242       1     2019-4    United Kingdom   \n",
              "4  668123744       2     2019-4    United Kingdom   \n",
              "\n",
              "                                         Review_Text            Branch  \\\n",
              "0  [booked, tickets, line, disneyland, ,, somethi...  Disneyland_Paris   \n",
              "1  [selling, fastpass, tickets, without, buy, ent...  Disneyland_Paris   \n",
              "2  [went, disneyland, paris, easter, break, famil...  Disneyland_Paris   \n",
              "3  [shame, park, carry, name, disney, ..., build,...  Disneyland_Paris   \n",
              "4  [know, disneyland, money, machine, realised, m...  Disneyland_Paris   \n",
              "\n",
              "   New_Rating  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6829ca46-9639-4fa0-bd66-00ccf0442fdc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review_ID</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Year_Month</th>\n",
              "      <th>Reviewer_Location</th>\n",
              "      <th>Review_Text</th>\n",
              "      <th>Branch</th>\n",
              "      <th>New_Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>670216406</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>Greece</td>\n",
              "      <td>[booked, tickets, line, disneyland, ,, somethi...</td>\n",
              "      <td>Disneyland_Paris</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>669337300</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>Greece</td>\n",
              "      <td>[selling, fastpass, tickets, without, buy, ent...</td>\n",
              "      <td>Disneyland_Paris</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>669121201</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>[went, disneyland, paris, easter, break, famil...</td>\n",
              "      <td>Disneyland_Paris</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>668207242</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>[shame, park, carry, name, disney, ..., build,...</td>\n",
              "      <td>Disneyland_Paris</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>668123744</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>[know, disneyland, money, machine, realised, m...</td>\n",
              "      <td>Disneyland_Paris</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6829ca46-9639-4fa0-bd66-00ccf0442fdc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6829ca46-9639-4fa0-bd66-00ccf0442fdc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6829ca46-9639-4fa0-bd66-00ccf0442fdc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-23d4f6b9-fa37-4661-8cd1-9e58a2e0c366\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-23d4f6b9-fa37-4661-8cd1-9e58a2e0c366')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-23d4f6b9-fa37-4661-8cd1-9e58a2e0c366 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_Paris_low",
              "summary": "{\n  \"name\": \"df_Paris_low\",\n  \"rows\": 1872,\n  \"fields\": [\n    {\n      \"column\": \"Review_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 180684140,\n        \"min\": 2216235,\n        \"max\": 670216406,\n        \"num_unique_values\": 1872,\n        \"samples\": [\n          114819291,\n          188760405,\n          150184207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year_Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 105,\n        \"samples\": [\n          \"2017-1\",\n          \"2013-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Reviewer_Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"Canada\",\n          \"Greece\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review_Text\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Disneyland_Paris\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_Paris_low['Review_Text'][0])\n",
        "print(df_Paris_low['Review_Text'][1])\n",
        "print(df_Paris_low['Review_Text'][2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db90e047-58e6-4ac8-c595-c361765acfcf",
        "id": "PaDjm6VoN6Ze"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['booked', 'tickets', 'line', 'disneyland', ',', 'something', 'recommend', ',', 'lines', 'already', 'long', 'got', 'there', ',', 'booking', 'tickets', 'advance', 'help.i', 'advised', 'buy', 'fast', 'passes', ',', 'decided', 'to', ',', 'clear', 'website', 'also', 'counted', 'entrance', 'ticket', ',', 'meaning', ';', 'total', 'price', '90', 'euro', 'entrancevinstead', '74', '68', 'extra', '90', 'euro', \"'s\", ',', 'depending', 'age', 'child.but', 'even', 'would', 'have', ',', 'lines', 'long', 'well', ',', 'also', ',', 'kind', 'system', 'this', '?', 'people', 'much', 'spend', 'held', 'back', ',', 'people', 'money', 'obviously', 'skip', 'lines', '...', 'absolutely', 'ridiculous.getting', 'something', 'eat', 'horrible', ',', 'lines', 'everywhere', '...', 'just', 'eat', 'someting', 'right', '...', 'and', 'get', 'started', 'staff', ',', 'trying', 'figure', 'amount', '3', 'icecreams', 'small', 'casio', 'computer', ',', 'able', 'to', ',', 'rude', 'staff', '.', '2', 'ladies', 'friendly', ',', 'honest', '.', 'all', ',', 'disappointing', ',', 'long', 'waiting', 'lines', ',', 'able', 'sit', 'somewhere', 'like', 'human', 'being', ',', 'bad', 'service.our', 'kids', 'disappointed', 'wanted', 'leave.we', 'even', 'heard', 'people', 'saying', ':', \"'never\", \"again'.absolutely\", 'worth', 'money', '.']\n",
            "['selling', 'fastpass', 'tickets', 'without', 'buy', 'entrance', 'tickets', '?', '?', '?', 'answer', '?', '?', '?', 'shame', 'you', '!', '!', '!', 'came', 'greece', 'entrance', '!', '!', '!', 'avoid', 'place', '!', '!', '!']\n",
            "['went', 'disneyland', 'paris', 'easter', 'break', 'family', 'members', 'come', 'states', '.', 'experience', 'disneyland', 'anaheim', 'disneyworld', 'orlando', ',', 'perhaps', 'expectations', 'high', '.', 'first', 'off', ',', 'moving', 'sidewalks', 'parking', 'lot', 'park', 'entrance', 'broken', '.', 'great', 'first', 'impression', '.', 'furthermore', ',', 'lot', 'gardening', 'work', 'ongoing', 'arrived', '.', 'disney', 'supposed', 'magic', ',', 'everything', 'looking', 'top', 'shape', 'perfect', '?', 'security', 'line', 'moved', ',', 'first', 'bottleneck', '.', 'second', 'major', 'bottleneck', 'actually', 'enter', 'park', '.', 'bought', 'tickets', 'online', ',', 'thought', 'would', 'able', 'enter', 'park', 'fairly', 'easily', '.', 'however', ',', 'pretty', 'much', 'mass', 'chaos', 'ticket', 'check', 'point', ',', 'many', 'people', 'jumping', 'queue', 'others', 'standing', 'around', '.', 'eventually', 'made', 'made', 'way', 'fantasyland.we', 'managed', 'ride', 'rides', ',', 'came', 'time', 'get', 'teacups', ',', 'closed', 'ride', '.', 'day', ',', '45', 'minutes', 'disperse', 'line', '.', 'caused', 'many', 'people', 'loiter', 'around', 'entrance', ',', 'waiting', 'ride', 'open', 'again', '.', 'great', '.', 'manage', 'eat', 'lunch', 'early', 'enough', 'wait', 'long', 'time', '.', 'however', ',', 'food', 'got', 'ordered', '.', 'none', 'us', 'wherewithal', 'go', 'back', 'try', 'fix', 'order', '.', 'ate', 'got', '.', 'came', 'time', 'ice', 'cream', ',', 'took', '45', 'minutes', 'get', 'front', 'line', ',', 'another', '10', 'minutes', 'process', 'order', '.', 'staff', 'counter', 'poorly', 'trained', ',', 'one', 'person', 'knew', 'use', 'register', ',', 'others', 'seemed', 'like', 'trainees', '.', 'plenty', 'foodcarts', 'around', 'park', 'empty', '.', 'enough', 'staff', 'park', 'operate', 'these', '.', 'also', 'plenty', 'rides', '(', 'like', 'autopia', 'dumbo', ')', 'closed', 'renovation', '.', 'disneyland', 'paris', 'carry', 'iconic', 'felt', 'mouse', 'ears', '.', 'definitely', 'going', 'back', ',', 'ca', \"n't\", 'recommend', 'disneyland', 'paris', 'anyone', 'looking', 'true', 'disney', 'experience', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lexicon Normalization"
      ],
      "metadata": {
        "id": "a86IOFbMN6Ze"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Stemming"
      ],
      "metadata": {
        "id": "SDjU1mN6N6Ze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "\n",
        "# this is the function we will use\n",
        "ps = PorterStemmer()\n",
        "\n",
        "filtered_sent = df_Paris_low['Review_Text'][1]\n",
        "\n",
        "stemmed_words=[]\n",
        "for w in filtered_sent:\n",
        "    stemmed_words.append(ps.stem(w))\n",
        "\n",
        "print(\"Filtered Sentence:\",filtered_sent)\n",
        "print(\"Stemmed Sentence:\",stemmed_words)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4d4d9b0-c391-4e1a-c4f8-f512ec14611a",
        "id": "c4MdM2CAN6Zf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtered Sentence: ['selling', 'fastpass', 'tickets', 'without', 'buy', 'entrance', 'tickets', '?', '?', '?', 'answer', '?', '?', '?', 'shame', 'you', '!', '!', '!', 'came', 'greece', 'entrance', '!', '!', '!', 'avoid', 'place', '!', '!', '!']\n",
            "Stemmed Sentence: ['sell', 'fastpass', 'ticket', 'without', 'buy', 'entranc', 'ticket', '?', '?', '?', 'answer', '?', '?', '?', 'shame', 'you', '!', '!', '!', 'came', 'greec', 'entranc', '!', '!', '!', 'avoid', 'place', '!', '!', '!']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_Paris_low['Stemmed'] = df_Paris_low['Review_Text'].apply(lambda x: [ps.stem(y) for y in x]) # Stem every word."
      ],
      "metadata": {
        "id": "t9kQvIZUN6Zf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check your work!\n",
        "print(df_Paris_low['Review_Text'][0]) # reported\n",
        "print(df_Paris_low['Stemmed'][0]) # report\n",
        "\n",
        "print(df_Paris_low['Review_Text'][10]) # note the difference between inches, reported, covered\n",
        "print(df_Paris_low['Stemmed'][10]) # inch, report, cover"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82505bd4-7d9a-4fd2-dce4-92ebf9541b03",
        "id": "DHii3vqZN6Zf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['booked', 'tickets', 'line', 'disneyland', ',', 'something', 'recommend', ',', 'lines', 'already', 'long', 'got', 'there', ',', 'booking', 'tickets', 'advance', 'help.i', 'advised', 'buy', 'fast', 'passes', ',', 'decided', 'to', ',', 'clear', 'website', 'also', 'counted', 'entrance', 'ticket', ',', 'meaning', ';', 'total', 'price', '90', 'euro', 'entrancevinstead', '74', '68', 'extra', '90', 'euro', \"'s\", ',', 'depending', 'age', 'child.but', 'even', 'would', 'have', ',', 'lines', 'long', 'well', ',', 'also', ',', 'kind', 'system', 'this', '?', 'people', 'much', 'spend', 'held', 'back', ',', 'people', 'money', 'obviously', 'skip', 'lines', '...', 'absolutely', 'ridiculous.getting', 'something', 'eat', 'horrible', ',', 'lines', 'everywhere', '...', 'just', 'eat', 'someting', 'right', '...', 'and', 'get', 'started', 'staff', ',', 'trying', 'figure', 'amount', '3', 'icecreams', 'small', 'casio', 'computer', ',', 'able', 'to', ',', 'rude', 'staff', '.', '2', 'ladies', 'friendly', ',', 'honest', '.', 'all', ',', 'disappointing', ',', 'long', 'waiting', 'lines', ',', 'able', 'sit', 'somewhere', 'like', 'human', 'being', ',', 'bad', 'service.our', 'kids', 'disappointed', 'wanted', 'leave.we', 'even', 'heard', 'people', 'saying', ':', \"'never\", \"again'.absolutely\", 'worth', 'money', '.']\n",
            "['book', 'ticket', 'line', 'disneyland', ',', 'someth', 'recommend', ',', 'line', 'alreadi', 'long', 'got', 'there', ',', 'book', 'ticket', 'advanc', 'help.i', 'advis', 'buy', 'fast', 'pass', ',', 'decid', 'to', ',', 'clear', 'websit', 'also', 'count', 'entranc', 'ticket', ',', 'mean', ';', 'total', 'price', '90', 'euro', 'entrancevinstead', '74', '68', 'extra', '90', 'euro', \"'s\", ',', 'depend', 'age', 'child.but', 'even', 'would', 'have', ',', 'line', 'long', 'well', ',', 'also', ',', 'kind', 'system', 'thi', '?', 'peopl', 'much', 'spend', 'held', 'back', ',', 'peopl', 'money', 'obvious', 'skip', 'line', '...', 'absolut', 'ridiculous.get', 'someth', 'eat', 'horribl', ',', 'line', 'everywher', '...', 'just', 'eat', 'somet', 'right', '...', 'and', 'get', 'start', 'staff', ',', 'tri', 'figur', 'amount', '3', 'icecream', 'small', 'casio', 'comput', ',', 'abl', 'to', ',', 'rude', 'staff', '.', '2', 'ladi', 'friendli', ',', 'honest', '.', 'all', ',', 'disappoint', ',', 'long', 'wait', 'line', ',', 'abl', 'sit', 'somewher', 'like', 'human', 'be', ',', 'bad', 'service.our', 'kid', 'disappoint', 'want', 'leave.w', 'even', 'heard', 'peopl', 'say', ':', \"'never\", \"again'.absolut\", 'worth', 'money', '.']\n",
            "['visited', 'dismal', 'land', 'paris', 'april', '2019', 'truly', 'disappointing', 'experience', 'was', '!', 'previously', 'visited', 'disney', 'parks', 'united', 'states', 'gulf', 'two', 'obvious', '.', 'dated', ',', 'tired', 'park', 'alternative', 'rides', ',', 'enough', 'entice', 'visit', 'first', 'day', 'broken', 'rides', ',', 'endless', 'waiting', 'line', 'everything', 'little', 'reward', 'realise', 'spent', 'elsewhere', 'boosted', 'economy', '.', 'park', 'staff', 'friendly', ',', 'rooms', 'small', 'clean', 'paying', 'inclusive', 'meals', ',', 'food', 'adequate', '.', 'cultural', 'differences', 'withstanding', ',', 'generally', 'unpleasant', 'nothing', 'poor', 'substitution', 'american', 'idea', 'remained', 'least', 'carefully', 'selected', 'location', 'concept', 'politeness', 'courtesy', 'would', 'lost', '.', 'never', 'again', '.', 'favour', 'dollar', 'trumps', 'euro', 'one', ',', 'big', 'time', '.']\n",
            "['visit', 'dismal', 'land', 'pari', 'april', '2019', 'truli', 'disappoint', 'experi', 'wa', '!', 'previous', 'visit', 'disney', 'park', 'unit', 'state', 'gulf', 'two', 'obviou', '.', 'date', ',', 'tire', 'park', 'altern', 'ride', ',', 'enough', 'entic', 'visit', 'first', 'day', 'broken', 'ride', ',', 'endless', 'wait', 'line', 'everyth', 'littl', 'reward', 'realis', 'spent', 'elsewher', 'boost', 'economi', '.', 'park', 'staff', 'friendli', ',', 'room', 'small', 'clean', 'pay', 'inclus', 'meal', ',', 'food', 'adequ', '.', 'cultur', 'differ', 'withstand', ',', 'gener', 'unpleas', 'noth', 'poor', 'substitut', 'american', 'idea', 'remain', 'least', 'care', 'select', 'locat', 'concept', 'polit', 'courtesi', 'would', 'lost', '.', 'never', 'again', '.', 'favour', 'dollar', 'trump', 'euro', 'one', ',', 'big', 'time', '.']\n"
          ]
        }
      ]
    }
  ]
}