DataX.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Manoj-Routhu/sentiment-analysis/blob/main/DataX.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyF8jhNkCEug",
        "outputId": "bd27fb9c-4221-4419-d755-5712fd57b6a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: textblob in /usr/local/lib/python3.7/dist-packages (0.15.3)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.7/dist-packages (from textblob) (3.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (7.1.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (2022.6.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (4.64.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (1.2.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tweepy in /usr/local/lib/python3.7/dist-packages (3.10.0)\n",
            "Requirement already satisfied: requests[socks]>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from tweepy) (2.23.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tweepy) (1.3.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tweepy) (1.15.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->tweepy) (3.2.2)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (2022.9.24)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (1.24.3)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (1.7.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pycountry\n",
            "  Downloading pycountry-22.3.5.tar.gz (10.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.1 MB 5.2 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pycountry) (57.4.0)\n",
            "Building wheels for collected packages: pycountry\n",
            "  Building wheel for pycountry (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycountry: filename=pycountry-22.3.5-py2.py3-none-any.whl size=10681845 sha256=d8e8e2531ce7de87cd7f7786b5eb135a64558ab46bae18e95f6edaa5446f2d38\n",
            "  Stored in directory: /root/.cache/pip/wheels/0e/06/e8/7ee176e95ea9a8a8c3b3afcb1869f20adbd42413d4611c6eb4\n",
            "Successfully built pycountry\n",
            "Installing collected packages: pycountry\n",
            "Successfully installed pycountry-22.3.5\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting langdetect\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
            "\u001b[K     |████████████████████████████████| 981 kB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from langdetect) (1.15.0)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993241 sha256=818960738b4bef2ecca46c4402ea74d98dd6aeccc8d6d720b706aa8589fe00bc\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/96/8a/f90c59ed25d75e50a8c10a1b1c2d4c402e4dacfa87f3aff36a\n",
            "Successfully built langdetect\n",
            "Installing collected packages: langdetect\n",
            "Successfully installed langdetect-1.0.9\n"
          ]
        }
      ],
      "source": [
        "!pip install textblob\n",
        "!pip install tweepy\n",
        "!pip install pycountry\n",
        "!pip install langdetect"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtX7XH8iD93x",
        "outputId": "c5596684-b555-479d-e078-c98ef3e674b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sklearn\n",
            "  Downloading sklearn-0.0.post1.tar.gz (3.6 kB)\n",
            "Building wheels for collected packages: sklearn\n",
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sklearn: filename=sklearn-0.0.post1-py3-none-any.whl size=2344 sha256=ce454fb733a65df509fa65f91b4fd34aa57d3d32c3907589d4267a7c7d927596\n",
            "  Stored in directory: /root/.cache/pip/wheels/42/56/cc/4a8bf86613aafd5b7f1b310477667c1fca5c51c3ae4124a003\n",
            "Successfully built sklearn\n",
            "Installing collected packages: sklearn\n",
            "Successfully installed sklearn-0.0.post1\n"
          ]
        }
      ],
      "source": [
        "!pip install sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jh78VkeUCGXd",
        "outputId": "a6f4e161-6059-4b50-b74e-c2d1a58a1e1d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "from textblob import TextBlob\n",
        "import sys\n",
        "import tweepy\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import nltk\n",
        "nltk.download('vader_lexicon')\n",
        "nltk.download('stopwords')\n",
        "import pycountry\n",
        "import re\n",
        "import string\n",
        "\n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "from PIL import Image\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "from langdetect import detect\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "# from sklearn.feature_extraction.text import CountVectorizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vu3o4iO9CM_c"
      },
      "outputs": [],
      "source": [
        "consumerKey = \"8SokfXDnGAI5xFzq5i4N7mz80\"\n",
        "consumerSecret = \"AVPd5cj9aFFEerVPwTKyjeuJtoNnrVVnyo3PC4tHwI41uEKq99\"\n",
        "accessToken = \"1393472744306532360-FGdHBhF3XAtTD8DgSuSo7EgyZ3s1uq\"\n",
        "accessTokenSecret = \"9nprttwHGvAbZhjVQgQ3JL6UuO3Nd6Wgg5hB3N3Meyal2\"\n",
        "auth = tweepy.OAuthHandler(consumerKey, consumerSecret)\n",
        "auth.set_access_token(accessToken, accessTokenSecret)\n",
        "api = tweepy.API(auth)\n",
        "#AAAAAAAAAAAAAAAAAAAAAOiJjQEAAAAASi%2BwSYkR%2FMqOK26umMe7KknIrLA%3DwaUTXuoTURnxinQdFYrAcUEe2PEiEQxRLU1Gz8C5ampyAn5nDp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WUMgCBPxFMr_",
        "outputId": "8cf7ccd5-c316-4b9d-d51b-ff9647d275cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please enter keyword or hashtag to search: review\n",
            "Please enter how many tweets to analyze: 2500\n"
          ]
        }
      ],
      "source": [
        "def percentage(part,whole):\n",
        " return 100 * float(part)/float(whole)\n",
        "keyword = input(\"Please enter keyword or hashtag to search: \")\n",
        "noOfTweet = int(input (\"Please enter how many tweets to analyze: \"))\n",
        "tweets = tweepy.Cursor(api.search_tweets, q=keyword).items(noOfTweet)\n",
        "positive = 0\n",
        "negative = 0\n",
        "neutral = 0\n",
        "polarity = 0\n",
        "tweet_list = []\n",
        "neutral_list = []\n",
        "negative_list = []\n",
        "positive_list = []\n",
        "for tweet in tweets:\n",
        " \n",
        " #print(tweet.text)\n",
        " tweet_list.append(tweet.text)\n",
        " analysis = TextBlob(tweet.text)\n",
        " score = SentimentIntensityAnalyzer().polarity_scores(tweet.text)\n",
        " neg = score['neg']\n",
        " neu = score['neu']\n",
        " pos = score['pos']\n",
        " comp = score['compound']\n",
        " polarity += analysis.sentiment.polarity\n",
        " \n",
        " if neg > pos:\n",
        "  negative_list.append(tweet.text)\n",
        "  negative += 1\n",
        " elif pos > neg:\n",
        "  positive_list.append(tweet.text)\n",
        "  positive += 1\n",
        " \n",
        " elif pos == neg:\n",
        "  neutral_list.append(tweet.text)\n",
        "  neutral += 1\n",
        "positive = percentage(positive, noOfTweet)\n",
        "negative = percentage(negative, noOfTweet)\n",
        "neutral = percentage(neutral, noOfTweet)\n",
        "polarity = percentage(polarity, noOfTweet)\n",
        "positive = format(positive, '.1f')\n",
        "negative = format(negative, '.1f')\n",
        "neutral = format(neutral, '.1f')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9JhnhATGAho",
        "outputId": "160e3bd6-2c50-4110-8534-9440d836a287"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total number:  2500\n",
            "positive sentiment:  877\n",
            "negative sentiment:  396\n",
            "neutral sentiment:  1227\n"
          ]
        }
      ],
      "source": [
        "tweet_list = pd.DataFrame(tweet_list)\n",
        "neutral_list = pd.DataFrame(neutral_list)\n",
        "negative_list = pd.DataFrame(negative_list)\n",
        "positive_list = pd.DataFrame(positive_list)\n",
        "print(\"total number: \",len(tweet_list))\n",
        "print(\"positive sentiment: \",len(positive_list))\n",
        "print(\"negative sentiment: \", len(negative_list))\n",
        "print(\"neutral sentiment: \",len(neutral_list))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "pQN8rUNVGDwU",
        "outputId": "634c2e45-e82d-4752-b20e-b2a20d362471"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
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          "data": {
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              "\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Selalu cv ke ibuk karena baik banget dan cepat...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @BWCISU: This question built on a @UNIDIR @...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @GemsOfBollywood: Among a spate of films in...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cybeart Apex Series Signature Edition review: ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @thehoneypop: BREAKING: #FaithInTheFuture i...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>2495</th>\n",
              "      <td>Implications of the endocannabinoid system and...</td>\n",
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              "    <tr>\n",
              "      <th>2496</th>\n",
              "      <td>A lovely comment from a #madhoney customer mak...</td>\n",
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              "    <tr>\n",
              "      <th>2497</th>\n",
              "      <td>RT @kentparktweet: When Costello’s Australian ...</td>\n",
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              "    <tr>\n",
              "      <th>2498</th>\n",
              "      <td>RT @verritaee: D'FESTA THE MOVIE - BTS  (D'FES...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2499</th>\n",
              "      <td>Conservative attorney George Conway calls form...</td>\n",
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              "                                                      0\n",
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              "2     RT @GemsOfBollywood: Among a spate of films in...\n",
              "3     Cybeart Apex Series Signature Edition review: ...\n",
              "4     RT @thehoneypop: BREAKING: #FaithInTheFuture i...\n",
              "...                                                 ...\n",
              "2495  Implications of the endocannabinoid system and...\n",
              "2496  A lovely comment from a #madhoney customer mak...\n",
              "2497  RT @kentparktweet: When Costello’s Australian ...\n",
              "2498  RT @verritaee: D'FESTA THE MOVIE - BTS  (D'FES...\n",
              "2499  Conservative attorney George Conway calls form...\n",
              "\n",
              "[2500 rows x 1 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels = ['Positive ['+str(positive)+'%]' , 'Neutral ['+str(neutral)+'%]','Negative ['+str(negative)+'%]']\n",
        "sizes = [positive, neutral, negative]\n",
        "colors = ['yellowgreen', 'purple','yellow']\n",
        "patches, texts = plt.pie(sizes,colors=colors, startangle=90)\n",
        "plt.style.use('default')\n",
        "plt.legend(labels)\n",
        "plt.title(\"Sentiment Analysis Result for keyword = \"+keyword+\"\" )\n",
        "plt.axis('equal')\n",
        "plt.show()\n",
        "\n",
        "tweet_list\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "itVGntAEGJlL"
      },
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        "tweet_list.drop_duplicates(inplace = True)"
      ]
    },
    {
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              "                                                   0  \\\n",
              "0  Selalu cv ke ibuk karena baik banget dan cepat...   \n",
              "1  RT @BWCISU: This question built on a @UNIDIR @...   \n",
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              "\n",
              "  sentiment  neg  neu  pos  compound  \n",
              "0   neutral  0.0  1.0  0.0       0.0  \n",
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              "7   neutral  0.0  1.0  0.0       0.0  \n",
              "8   neutral  0.0  1.0  0.0       0.0  \n",
              "9   neutral  0.0  1.0  0.0       0.0  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tw_list = pd.DataFrame(tweet_list)\n",
        "tw_list[\"text\"] = tw_list[0]\n",
        "\n",
        "remove_rt = lambda x: re.sub('RT @\\w+: ',\" \",x)\n",
        "rt = lambda x: re.sub(\"(@[A-Za-z0–9]+)|([-⁰9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\",\" \",x)\n",
        "tw_list[\"text\"] = tw_list.text.map(remove_rt).map(rt)\n",
        "tw_list[\"text\"] = tw_list.text.str.lower()\n",
        "tw_list.head(10)"
      ]
    },
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              "      <td>RT @BWCISU: This question built on a @UNIDIR @...</td>\n",
              "      <td>\\n\\n     ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @GemsOfBollywood: Among a spate of films in...</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cybeart Apex Series Signature Edition review: ...</td>\n",
              "      <td>: ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @thehoneypop: BREAKING: #FaithInTheFuture i...</td>\n",
              "      <td>: #                    '             ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>🗞—Cabinet Approves new Rent Bill.\\n\\n—“The rev...</td>\n",
              "      <td>🗞—                              .\\n\\n—“       ...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>https://t.co/MvqrpxGHiE via @SueWall61433384</td>\n",
              "      <td>:// .  /                61433384</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ASUS Zenfone Go　完全攻略マスター　9．Zenfone Goの便利な機能を紹介...</td>\n",
              "      <td>完全攻略マスター　 ．          の便利な機能を紹介...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>That would be due to certain facts he said whi...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>pano ba ‘yan next month pa exam namin, naga-ad...</td>\n",
              "      <td>‘                            ,        ...</td>\n",
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              "      <td>0.0</td>\n",
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              "  "
            ],
            "text/plain": [
              "                                                   0  \\\n",
              "0  Selalu cv ke ibuk karena baik banget dan cepat...   \n",
              "1  RT @BWCISU: This question built on a @UNIDIR @...   \n",
              "2  RT @GemsOfBollywood: Among a spate of films in...   \n",
              "3  Cybeart Apex Series Signature Edition review: ...   \n",
              "4  RT @thehoneypop: BREAKING: #FaithInTheFuture i...   \n",
              "5  🗞—Cabinet Approves new Rent Bill.\\n\\n—“The rev...   \n",
              "6       https://t.co/MvqrpxGHiE via @SueWall61433384   \n",
              "7  ASUS Zenfone Go　完全攻略マスター　9．Zenfone Goの便利な機能を紹介...   \n",
              "8  That would be due to certain facts he said whi...   \n",
              "9  pano ba ‘yan next month pa exam namin, naga-ad...   \n",
              "\n",
              "                                                text  polarity  subjectivity  \\\n",
              "0                                                ...       0.0           0.0   \n",
              "1                                       \\n\\n     ...       0.0           0.0   \n",
              "2                                                ...       0.0           0.0   \n",
              "3                                              : ...       0.0           0.0   \n",
              "4           : #                    '             ...       0.0           0.0   \n",
              "5  🗞—                              .\\n\\n—“       ...       0.0           0.0   \n",
              "6                   :// .  /                61433384       0.0           0.0   \n",
              "7                 　完全攻略マスター　 ．          の便利な機能を紹介...       0.0           0.0   \n",
              "8                                                ...       0.0           0.0   \n",
              "9          ‘                            ,        ...       0.0           0.0   \n",
              "\n",
              "  sentiment  neg  neu  pos  compound  \n",
              "0   neutral  0.0  1.0  0.0       0.0  \n",
              "1   neutral  0.0  1.0  0.0       0.0  \n",
              "2   neutral  0.0  0.0  0.0       0.0  \n",
              "3   neutral  0.0  1.0  0.0       0.0  \n",
              "4   neutral  0.0  0.0  0.0       0.0  \n",
              "5   neutral  0.0  1.0  0.0       0.0  \n",
              "6   neutral  0.0  1.0  0.0       0.0  \n",
              "7   neutral  0.0  1.0  0.0       0.0  \n",
              "8   neutral  0.0  1.0  0.0       0.0  \n",
              "9   neutral  0.0  1.0  0.0       0.0  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tw_list[['polarity', 'subjectivity']] = tw_list['text'].apply(lambda Text: pd.Series(TextBlob(Text).sentiment))\n",
        "for index, row in tw_list['text'].iteritems():\n",
        " score = SentimentIntensityAnalyzer().polarity_scores(row)\n",
        " neg = score['neg']\n",
        " neu = score['neu']\n",
        " pos = score['pos']\n",
        " comp = score['compound']\n",
        "\n",
        " if neg > pos:\n",
        "   tw_list.loc[index, 'sentiment'] = \"negative\"\n",
        " elif pos > neg:\n",
        "   tw_list.loc[index, 'sentiment'] = \"positive\"\n",
        " else:\n",
        "   tw_list.loc[index, 'sentiment'] = \"neutral\"\n",
        "   tw_list.loc[index, 'neg'] = neg\n",
        "   tw_list.loc[index, 'neu'] = neu\n",
        "   tw_list.loc[index, 'pos'] = pos\n",
        "   tw_list.loc[index, 'compound'] = comp\n",
        "tw_list.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xu0qal0MGRfl"
      },
      "outputs": [],
      "source": [
        "tw_list_negative = tw_list[tw_list[\"sentiment\"]==\"negative\"]\n",
        "tw_list_positive = tw_list[tw_list[\"sentiment\"]==\"positive\"]\n",
        "tw_list_neutral = tw_list[tw_list[\"sentiment\"]==\"neutral\"]\n",
        "\n",
        "def count_values_in_column(data,feature):\n",
        " total=data.loc[:,feature].value_counts(dropna=False)\n",
        " percentage=round(data.loc[:,feature].value_counts(dropna=False,normalize=True)*100,2)\n",
        " return pd.concat([total,percentage],axis=1,keys=['Total','Percentage'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 143
        },
        "id": "aC_k0-U9GXAQ",
        "outputId": "1aa7130f-3eb4-40cd-9992-28590565e05b"
      },
      "outputs": [
        {
          "data": {
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              "  <tbody>\n",
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              "      <th>neutral</th>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "          Total  Percentage\n",
              "neutral    1784       99.89\n",
              "negative      1        0.06\n",
              "positive      1        0.06"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "count_values_in_column(tw_list,\"sentiment\")"
      ]
    },
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        },
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        {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pichart = count_values_in_column(tw_list,\"sentiment\")\n",
        "names= pichart.index\n",
        "size=pichart[\"Percentage\"]\n",
        " \n",
        "\n",
        "my_circle=plt.Circle( (0,0), 0.7, color='white')\n",
        "plt.pie(size, labels=names, colors=['purple', 'yellow' ,'yellowgreen'])\n",
        "p=plt.gcf()\n",
        "p.gca().add_artist(my_circle)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "jCUPlEnLGdZs",
        "outputId": "78f303e8-b902-4ba2-f660-ffaae4af5ff1"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>text_word_count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sentiment</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>negative</th>\n",
              "      <td>9.0</td>\n",
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              "      <th>neutral</th>\n",
              "      <td>7.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>18.0</td>\n",
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              "</div>\n",
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              "  <style>\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3185cb11-439f-487c-ae2e-87373090dc19 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-3185cb11-439f-487c-ae2e-87373090dc19');\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",
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              "  "
            ],
            "text/plain": [
              "           text_word_count\n",
              "sentiment                 \n",
              "negative               9.0\n",
              "neutral                7.4\n",
              "positive              18.0"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "tw_list['text_len'] = tw_list['text'].astype(str).apply(len)\n",
        "tw_list['text_word_count'] = tw_list['text'].apply(lambda x: len(str(x).split()))\n",
        "round(pd.DataFrame(tw_list.groupby(\"sentiment\").text_len.mean()),2)\n",
        "\n",
        "round(pd.DataFrame(tw_list.groupby(\"sentiment\").text_word_count.mean()),2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d04co5GoGf2A"
      },
      "outputs": [],
      "source": [
        "def remove_punct(text):\n",
        "    text  = \"\".join([char for char in text if char not in string.punctuation])\n",
        "    text = re.sub('[0-9]+', '', text)\n",
        "    return text\n",
        "\n",
        "tw_list['punct'] = tw_list['text'].apply(lambda x: remove_punct(x))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cciskg-IGj0g"
      },
      "outputs": [],
      "source": [
        "def tokenization(text):\n",
        "    text = re.split('\\W+', text)\n",
        "    return text\n",
        "\n",
        "tw_list['tokenized'] = tw_list['punct'].apply(lambda x: tokenization(x.lower()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHLsFPQWGn9-"
      },
      "outputs": [],
      "source": [
        "stopword = nltk.corpus.stopwords.words('english')\n",
        "def remove_stopwords(text):\n",
        "    text = [word for word in text if word not in stopword]\n",
        "    return text\n",
        "    \n",
        "tw_list['nonstop'] = tw_list['tokenized'].apply(lambda x: remove_stopwords(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-f7smeCGrI4"
      },
      "outputs": [],
      "source": [
        "ps = nltk.PorterStemmer()\n",
        "\n",
        "def stemming(text):\n",
        "    text = [ps.stem(word) for word in text]\n",
        "    return text\n",
        "\n",
        "tw_list['stemmed'] = tw_list['nonstop'].apply(lambda x: stemming(x))"
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              "                                                      0  \\\n",
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              "2495                                                ...       0.0   \n",
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              "2499                                                ...       0.0   \n",
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              "\n",
              "      text_word_count                                              punct  \\\n",
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              "2491                6                                                      \n",
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        "def clean_text(text):\n",
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        "    text_rc = re.sub('[0-9]+', '', text_lc)\n",
        "    tokens = re.split('\\W+', text_rc)    # tokenization\n",
        "    text = [ps.stem(word) for word in tokens if word not in stopword]  # remove stopwords \n",
        "    return text\n",
        "\n",
        "tw_list"
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