Lecture_11_Movie_Review_Sentiment_Analysis.ipynb

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          }
        },
        "b4568df326654d9da4be79a48f6286d3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "4af78b75a89e415e9f9d114f89798f85": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
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            "_model_name": "LayoutModel",
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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishpatel26/Ganpat-University-Data-Science/blob/main/Lecture_11_Movie_Review_Sentiment_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv5jfFJ5DNHV"
      },
      "source": [
        "### 1.Load Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPmbVsY74oN0"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import one_hot, Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras import Sequential\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm.notebook import tqdm\n",
        "# import shutils\n",
        "# shutils.unpack_archive('/content/archive (1).zip', '/contents/', \"zip\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeZtRPL2DQx6"
      },
      "source": [
        "### 2.Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DdzlU3U_48AY",
        "outputId": "b27b695a-4338-42e7-c7bf-5d1c24b07921"
      },
      "source": [
        "mv_review = pd.read_csv(\"https://raw.githubusercontent.com/LawrenceDuan/IMDb-Review-Analysis/master/IMDb_Reviews.csv\")\n",
        "mv_review.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>My family and I normally do not watch local mo...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Believe it or not, this was at one time the wo...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>After some internet surfing, I found the \"Home...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>One of the most unheralded great works of anim...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>It was the Sixties, and anyone with long hair ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review  sentiment\n",
              "0  My family and I normally do not watch local mo...          1\n",
              "1  Believe it or not, this was at one time the wo...          0\n",
              "2  After some internet surfing, I found the \"Home...          0\n",
              "3  One of the most unheralded great works of anim...          1\n",
              "4  It was the Sixties, and anyone with long hair ...          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "7k0Mbr935IXo",
        "outputId": "b1a6fe1b-c80b-4190-d986-1f01f0df4d8b"
      },
      "source": [
        "temp = mv_review.iloc[1, 0]\n",
        "temp"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Believe it or not, this was at one time the worst movie I had ever seen. Since that time, I have seen many more movies that are worse (how is it possible??) Therefore, to be fair, I had to give this movie a 2 out of 10. But it was a tough call.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5MQlzwIDTo0"
      },
      "source": [
        "### 3.Sanity Check of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CTvPTcG5TrW",
        "outputId": "1a1f56bd-74c3-49a5-813d-eafb3fd5cad7"
      },
      "source": [
        "mv_review.isnull().sum()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "review       0\n",
              "sentiment    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2purw55q8bPG",
        "outputId": "c6fcc887-20a7-4cfe-92b8-f35408a191f1"
      },
      "source": [
        "mv_review.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EDnoqgADXAo"
      },
      "source": [
        "### 4.Imbalance Checking on Target variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Wbzz4E2U8icb",
        "outputId": "441abb03-d2bd-43c7-a605-0eb1ecb938c7"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.countplot(x=\"sentiment\", data=mv_review)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd32e5c650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f38T3yM39NEO"
      },
      "source": [
        "### 5.Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "nYRKehig8157",
        "outputId": "9150ad6a-35db-4b15-8f29-a247d655364f"
      },
      "source": [
        "temp"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Believe it or not, this was at one time the worst movie I had ever seen. Since that time, I have seen many more movies that are worse (how is it possible??) Therefore, to be fair, I had to give this movie a 2 out of 10. But it was a tough call.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHHjTpBD-_Uf"
      },
      "source": [
        "TAG_re = re.compile(r'<[^>]+>')\n",
        "\n",
        "def remove_tags(text):\n",
        "  return TAG_re.sub('', text)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NARZrZ77_GZr"
      },
      "source": [
        "def preprocessing_text(text):\n",
        "\n",
        "  sentence = remove_tags(text) # remove tag\n",
        "\n",
        "  sentence = re.sub(r'[^a-zA-Z]\\s+',' ', sentence) # remove whitespace and numbers\n",
        "\n",
        "  sentence = re.sub(r'\\s+[^a-zA-Z]\\s+',' ', sentence) #single character remove\n",
        "\n",
        "  sentence = re.sub(r'\\s+',' ', sentence) #single character remove\n",
        "\n",
        "  return sentence"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "d2a5020fdbd340beba556f26a3eea455",
            "c93e2a9a40a84890826c809f08b84e3b",
            "3b4c097ee83f46218471896da8d0561b",
            "2cc8c8104e764b948cdac59f302935b2",
            "3d9179943f4b4dd296303597ab7b30b1",
            "436197d5ad70480eb8f7598efd79f6ac",
            "fc2552dfd83a4c76acc897070ff76c4d",
            "f151268fccf84ee9bd11dbc7a3f71a88"
          ]
        },
        "id": "cHePFJmMALQy",
        "outputId": "c005c3b8-2800-4333-cdbb-37c9e8fcac83"
      },
      "source": [
        "X = []\n",
        "for sent in tqdm(list(mv_review[\"review\"])):\n",
        "  X.append(preprocessing_text(sent))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2a5020fdbd340beba556f26a3eea455",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPFhdwOsEU32"
      },
      "source": [
        "y = mv_review[\"sentiment\"].values"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZrEqN5LEzng"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state = 42)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-XMLaFy8FknS",
        "outputId": "01203c6d-6a28-49e1-fcfb-9bf3a5d85fa9"
      },
      "source": [
        "len(X_train), len(y_train)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 40000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaYHMiiiFq9g",
        "outputId": "8032859b-e434-4110-fd1e-d987994b286b"
      },
      "source": [
        "len(X_test), len(y_test)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 10000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lZOPyt1GAIb"
      },
      "source": [
        "### 6.Embedding Layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQAp_GOAFuJP"
      },
      "source": [
        "# Token the sentences into words\n",
        "tokernizer = Tokenizer(num_words = 5000)\n",
        "tokernizer.fit_on_texts(X_train)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WL4R2Y_RGt1O"
      },
      "source": [
        "# Transforms each text in texts to a sequence of integers.\n",
        "X_train = tokernizer.texts_to_sequences(X_train)\n",
        "X_test = tokernizer.texts_to_sequences(X_test)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4P65t6bbJGh6",
        "outputId": "53602c03-3098-4e91-d729-58c377ce03a2"
      },
      "source": [
        "vocal_length = len(tokernizer.word_index) + 1\n",
        "print(vocal_length)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "110316\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIYwl-cwL7Ar"
      },
      "source": [
        "max_len = 100"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2na7CSiLMFQq"
      },
      "source": [
        "X_train = pad_sequences(X_train, padding=\"post\", maxlen = 100)\n",
        "X_test = pad_sequences(X_test, padding=\"post\", maxlen = 100)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7_70rr5NEVJ",
        "outputId": "bf8c011f-9da7-477a-a5f3-28c8ea78c8ec"
      },
      "source": [
        "!wget https://media.githubusercontent.com/media/rohanrao619/Twitter_Sentiment_Analysis/master/glove.6B.100d.txt"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-31 12:15:42--  https://media.githubusercontent.com/media/rohanrao619/Twitter_Sentiment_Analysis/master/glove.6B.100d.txt\n",
            "Resolving media.githubusercontent.com (media.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to media.githubusercontent.com (media.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 347116733 (331M) [text/plain]\n",
            "Saving to: ‘glove.6B.100d.txt’\n",
            "\n",
            "glove.6B.100d.txt   100%[===================>] 331.04M   161MB/s    in 2.1s    \n",
            "\n",
            "2021-05-31 12:15:58 (161 MB/s) - ‘glove.6B.100d.txt’ saved [347116733/347116733]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZAJYpxNOf25"
      },
      "source": [
        "emb_dict = {}\n",
        "glove_file = open(\"/content/glove.6B.100d.txt\", encoding=\"utf8\")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "39217361b5e84a1a9c969cb449d8f6e4",
            "20da303e961146f78f4d33a52703f102",
            "a65b656c01cf49af9146ff119c9c8d0f",
            "1fe3bbfab2fb4ef99cc08b914becdc1c",
            "4a578a1f27894e5f839771f6f4fe6fce",
            "d7a48879e6574c569491fc92800812d7",
            "5d682c2223bd42daaf2f157fec898bf6",
            "7acfaea1b4b74e10be2ba8472c79db58"
          ]
        },
        "id": "f5nalXmQO0d7",
        "outputId": "45cb0abf-d789-4b9e-9a67-713f175aee55"
      },
      "source": [
        "for line in tqdm(glove_file):\n",
        "  records = line.split()\n",
        "  word = records[0]\n",
        "  vect_dim = np.asarray(records[1:], dtype=\"float32\")\n",
        "  emb_dict[word] = vect_dim\n",
        "\n",
        "glove_file.close()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "39217361b5e84a1a9c969cb449d8f6e4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nigqb-40PZVv"
      },
      "source": [
        "emb_matrix = np.zeros((vocal_length, 100))\n",
        "for word, index in tokernizer.word_index.items():\n",
        "  emb_vect = emb_dict.get(word)\n",
        "  if emb_vect is not None:\n",
        "    emb_matrix[index] = emb_vect"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "yTzWc41MREBu",
        "outputId": "e472e1e4-3f86-4154-f6e5-314d11e085f7"
      },
      "source": [
        "pd.DataFrame(emb_matrix)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>76</th>\n",
              "      <th>77</th>\n",
              "      <th>78</th>\n",
              "      <th>79</th>\n",
              "      <th>80</th>\n",
              "      <th>81</th>\n",
              "      <th>82</th>\n",
              "      <th>83</th>\n",
              "      <th>84</th>\n",
              "      <th>85</th>\n",
              "      <th>86</th>\n",
              "      <th>87</th>\n",
              "      <th>88</th>\n",
              "      <th>89</th>\n",
              "      <th>90</th>\n",
              "      <th>91</th>\n",
              "      <th>92</th>\n",
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              "      <th>94</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.00000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.038194</td>\n",
              "      <td>-0.244870</td>\n",
              "      <td>0.728120</td>\n",
              "      <td>-0.399610</td>\n",
              "      <td>0.083172</td>\n",
              "      <td>0.043953</td>\n",
              "      <td>-0.39141</td>\n",
              "      <td>0.33440</td>\n",
              "      <td>-0.57545</td>\n",
              "      <td>0.087459</td>\n",
              "      <td>0.28787</td>\n",
              "      <td>-0.067310</td>\n",
              "      <td>0.309060</td>\n",
              "      <td>-0.263840</td>\n",
              "      <td>-0.13231</td>\n",
              "      <td>-0.207570</td>\n",
              "      <td>0.33395</td>\n",
              "      <td>-0.338480</td>\n",
              "      <td>-0.317430</td>\n",
              "      <td>-0.483360</td>\n",
              "      <td>0.146400</td>\n",
              "      <td>-0.373040</td>\n",
              "      <td>0.345770</td>\n",
              "      <td>0.052041</td>\n",
              "      <td>0.449460</td>\n",
              "      <td>-0.46971</td>\n",
              "      <td>0.02628</td>\n",
              "      <td>-0.54155</td>\n",
              "      <td>-0.155180</td>\n",
              "      <td>-0.141070</td>\n",
              "      <td>-0.039722</td>\n",
              "      <td>0.282770</td>\n",
              "      <td>0.143930</td>\n",
              "      <td>0.23464</td>\n",
              "      <td>-0.310210</td>\n",
              "      <td>0.086173</td>\n",
              "      <td>0.203970</td>\n",
              "      <td>0.52624</td>\n",
              "      <td>0.171640</td>\n",
              "      <td>-0.082378</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.249780</td>\n",
              "      <td>0.92136</td>\n",
              "      <td>0.034514</td>\n",
              "      <td>0.467450</td>\n",
              "      <td>1.10790</td>\n",
              "      <td>-0.193580</td>\n",
              "      <td>-0.074575</td>\n",
              "      <td>0.233530</td>\n",
              "      <td>-0.052062</td>\n",
              "      <td>-0.220440</td>\n",
              "      <td>0.057162</td>\n",
              "      <td>-0.15806</td>\n",
              "      <td>-0.307980</td>\n",
              "      <td>-0.416250</td>\n",
              "      <td>0.37972</td>\n",
              "      <td>0.150060</td>\n",
              "      <td>-0.532120</td>\n",
              "      <td>-0.205500</td>\n",
              "      <td>-1.25260</td>\n",
              "      <td>0.071624</td>\n",
              "      <td>0.705650</td>\n",
              "      <td>0.497440</td>\n",
              "      <td>-0.42063</td>\n",
              "      <td>0.261480</td>\n",
              "      <td>-1.53800</td>\n",
              "      <td>-0.302230</td>\n",
              "      <td>-0.073438</td>\n",
              "      <td>-0.283120</td>\n",
              "      <td>0.371040</td>\n",
              "      <td>-0.25217</td>\n",
              "      <td>0.016215</td>\n",
              "      <td>-0.017099</td>\n",
              "      <td>-0.389840</td>\n",
              "      <td>0.874240</td>\n",
              "      <td>-0.725690</td>\n",
              "      <td>-0.51058</td>\n",
              "      <td>-0.52028</td>\n",
              "      <td>-0.14590</td>\n",
              "      <td>0.82780</td>\n",
              "      <td>0.270620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.071953</td>\n",
              "      <td>0.231270</td>\n",
              "      <td>0.023731</td>\n",
              "      <td>-0.506380</td>\n",
              "      <td>0.339230</td>\n",
              "      <td>0.195900</td>\n",
              "      <td>-0.32943</td>\n",
              "      <td>0.18364</td>\n",
              "      <td>-0.18057</td>\n",
              "      <td>0.289630</td>\n",
              "      <td>0.20448</td>\n",
              "      <td>-0.549600</td>\n",
              "      <td>0.273990</td>\n",
              "      <td>0.583270</td>\n",
              "      <td>0.20468</td>\n",
              "      <td>-0.492280</td>\n",
              "      <td>0.19974</td>\n",
              "      <td>-0.070237</td>\n",
              "      <td>-0.880490</td>\n",
              "      <td>0.294850</td>\n",
              "      <td>0.140710</td>\n",
              "      <td>-0.100900</td>\n",
              "      <td>0.994490</td>\n",
              "      <td>0.369730</td>\n",
              "      <td>0.445540</td>\n",
              "      <td>0.28998</td>\n",
              "      <td>-0.13760</td>\n",
              "      <td>-0.56365</td>\n",
              "      <td>-0.029365</td>\n",
              "      <td>-0.412200</td>\n",
              "      <td>-0.252690</td>\n",
              "      <td>0.631810</td>\n",
              "      <td>-0.447670</td>\n",
              "      <td>0.24363</td>\n",
              "      <td>-0.108130</td>\n",
              "      <td>0.251640</td>\n",
              "      <td>0.469670</td>\n",
              "      <td>0.37550</td>\n",
              "      <td>-0.236130</td>\n",
              "      <td>-0.141290</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.223220</td>\n",
              "      <td>0.72932</td>\n",
              "      <td>-0.053211</td>\n",
              "      <td>0.435070</td>\n",
              "      <td>0.85011</td>\n",
              "      <td>-0.429350</td>\n",
              "      <td>0.926640</td>\n",
              "      <td>0.390510</td>\n",
              "      <td>1.058500</td>\n",
              "      <td>-0.245610</td>\n",
              "      <td>-0.182650</td>\n",
              "      <td>-0.53280</td>\n",
              "      <td>0.059518</td>\n",
              "      <td>-0.660190</td>\n",
              "      <td>0.18991</td>\n",
              "      <td>0.288360</td>\n",
              "      <td>-0.243400</td>\n",
              "      <td>0.527840</td>\n",
              "      <td>-0.65762</td>\n",
              "      <td>-0.140810</td>\n",
              "      <td>1.049100</td>\n",
              "      <td>0.513400</td>\n",
              "      <td>-0.23816</td>\n",
              "      <td>0.698950</td>\n",
              "      <td>-1.48130</td>\n",
              "      <td>-0.248700</td>\n",
              "      <td>-0.179360</td>\n",
              "      <td>-0.059137</td>\n",
              "      <td>-0.080560</td>\n",
              "      <td>-0.48782</td>\n",
              "      <td>0.014487</td>\n",
              "      <td>-0.625900</td>\n",
              "      <td>-0.323670</td>\n",
              "      <td>0.418620</td>\n",
              "      <td>-1.080700</td>\n",
              "      <td>0.46742</td>\n",
              "      <td>-0.49931</td>\n",
              "      <td>-0.71895</td>\n",
              "      <td>0.86894</td>\n",
              "      <td>0.195390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.270860</td>\n",
              "      <td>0.044006</td>\n",
              "      <td>-0.020260</td>\n",
              "      <td>-0.173950</td>\n",
              "      <td>0.644400</td>\n",
              "      <td>0.712130</td>\n",
              "      <td>0.35510</td>\n",
              "      <td>0.47138</td>\n",
              "      <td>-0.29637</td>\n",
              "      <td>0.544270</td>\n",
              "      <td>-0.72294</td>\n",
              "      <td>-0.004761</td>\n",
              "      <td>0.040611</td>\n",
              "      <td>0.043236</td>\n",
              "      <td>0.29729</td>\n",
              "      <td>0.107250</td>\n",
              "      <td>0.40156</td>\n",
              "      <td>-0.536620</td>\n",
              "      <td>0.033382</td>\n",
              "      <td>0.067396</td>\n",
              "      <td>0.645560</td>\n",
              "      <td>-0.085523</td>\n",
              "      <td>0.141030</td>\n",
              "      <td>0.094539</td>\n",
              "      <td>0.749470</td>\n",
              "      <td>-0.19400</td>\n",
              "      <td>-0.68739</td>\n",
              "      <td>-0.41741</td>\n",
              "      <td>-0.228070</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>-0.489990</td>\n",
              "      <td>0.809450</td>\n",
              "      <td>0.045138</td>\n",
              "      <td>-0.11898</td>\n",
              "      <td>0.201610</td>\n",
              "      <td>0.392760</td>\n",
              "      <td>-0.201210</td>\n",
              "      <td>0.31354</td>\n",
              "      <td>0.753040</td>\n",
              "      <td>0.259070</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.033425</td>\n",
              "      <td>0.69553</td>\n",
              "      <td>-0.644720</td>\n",
              "      <td>-0.007228</td>\n",
              "      <td>0.89575</td>\n",
              "      <td>0.200150</td>\n",
              "      <td>0.464930</td>\n",
              "      <td>0.619330</td>\n",
              "      <td>-0.106600</td>\n",
              "      <td>0.086910</td>\n",
              "      <td>-0.462300</td>\n",
              "      <td>0.18262</td>\n",
              "      <td>-0.158490</td>\n",
              "      <td>0.020791</td>\n",
              "      <td>0.19373</td>\n",
              "      <td>0.063426</td>\n",
              "      <td>-0.316730</td>\n",
              "      <td>-0.481770</td>\n",
              "      <td>-1.38480</td>\n",
              "      <td>0.136690</td>\n",
              "      <td>0.968590</td>\n",
              "      <td>0.049965</td>\n",
              "      <td>-0.27380</td>\n",
              "      <td>-0.035686</td>\n",
              "      <td>-1.05770</td>\n",
              "      <td>-0.244670</td>\n",
              "      <td>0.903660</td>\n",
              "      <td>-0.124420</td>\n",
              "      <td>0.080776</td>\n",
              "      <td>-0.83401</td>\n",
              "      <td>0.572010</td>\n",
              "      <td>0.088945</td>\n",
              "      <td>-0.425320</td>\n",
              "      <td>-0.018253</td>\n",
              "      <td>-0.079995</td>\n",
              "      <td>-0.28581</td>\n",
              "      <td>-0.01089</td>\n",
              "      <td>-0.49230</td>\n",
              "      <td>0.63687</td>\n",
              "      <td>0.236420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.152900</td>\n",
              "      <td>-0.242790</td>\n",
              "      <td>0.898370</td>\n",
              "      <td>0.169960</td>\n",
              "      <td>0.535160</td>\n",
              "      <td>0.487840</td>\n",
              "      <td>-0.58826</td>\n",
              "      <td>-0.17982</td>\n",
              "      <td>-1.35810</td>\n",
              "      <td>0.425410</td>\n",
              "      <td>0.15377</td>\n",
              "      <td>0.242150</td>\n",
              "      <td>0.134740</td>\n",
              "      <td>0.411930</td>\n",
              "      <td>0.67043</td>\n",
              "      <td>-0.564180</td>\n",
              "      <td>0.42985</td>\n",
              "      <td>-0.012183</td>\n",
              "      <td>-0.116770</td>\n",
              "      <td>0.317810</td>\n",
              "      <td>0.054177</td>\n",
              "      <td>-0.054273</td>\n",
              "      <td>0.355160</td>\n",
              "      <td>-0.302410</td>\n",
              "      <td>0.314340</td>\n",
              "      <td>-0.33846</td>\n",
              "      <td>0.71715</td>\n",
              "      <td>-0.26855</td>\n",
              "      <td>-0.158370</td>\n",
              "      <td>-0.474670</td>\n",
              "      <td>0.051581</td>\n",
              "      <td>-0.332520</td>\n",
              "      <td>0.150030</td>\n",
              "      <td>-0.12990</td>\n",
              "      <td>-0.546170</td>\n",
              "      <td>-0.378430</td>\n",
              "      <td>0.642610</td>\n",
              "      <td>0.82187</td>\n",
              "      <td>-0.080006</td>\n",
              "      <td>0.078479</td>\n",
              "      <td>...</td>\n",
              "      <td>0.048850</td>\n",
              "      <td>0.78267</td>\n",
              "      <td>0.384970</td>\n",
              "      <td>0.420970</td>\n",
              "      <td>0.67882</td>\n",
              "      <td>0.103370</td>\n",
              "      <td>0.632800</td>\n",
              "      <td>-0.026595</td>\n",
              "      <td>0.586470</td>\n",
              "      <td>-0.443320</td>\n",
              "      <td>0.330570</td>\n",
              "      <td>-0.12022</td>\n",
              "      <td>-0.556450</td>\n",
              "      <td>0.073611</td>\n",
              "      <td>0.20915</td>\n",
              "      <td>0.433950</td>\n",
              "      <td>-0.012761</td>\n",
              "      <td>0.089874</td>\n",
              "      <td>-1.79910</td>\n",
              "      <td>0.084808</td>\n",
              "      <td>0.771120</td>\n",
              "      <td>0.631050</td>\n",
              "      <td>-0.90685</td>\n",
              "      <td>0.603260</td>\n",
              "      <td>-1.75150</td>\n",
              "      <td>0.185960</td>\n",
              "      <td>-0.506870</td>\n",
              "      <td>-0.702030</td>\n",
              "      <td>0.665780</td>\n",
              "      <td>-0.81304</td>\n",
              "      <td>0.187120</td>\n",
              "      <td>-0.018488</td>\n",
              "      <td>-0.267570</td>\n",
              "      <td>0.727000</td>\n",
              "      <td>-0.593630</td>\n",
              "      <td>-0.34839</td>\n",
              "      <td>-0.56094</td>\n",
              "      <td>-0.59100</td>\n",
              "      <td>1.00390</td>\n",
              "      <td>0.206640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110311</th>\n",
              "      <td>-0.241980</td>\n",
              "      <td>-0.624000</td>\n",
              "      <td>-0.017437</td>\n",
              "      <td>-0.350910</td>\n",
              "      <td>0.453320</td>\n",
              "      <td>-0.268770</td>\n",
              "      <td>-1.53820</td>\n",
              "      <td>0.16492</td>\n",
              "      <td>0.19361</td>\n",
              "      <td>-0.175950</td>\n",
              "      <td>0.93783</td>\n",
              "      <td>0.697680</td>\n",
              "      <td>-0.260220</td>\n",
              "      <td>0.551710</td>\n",
              "      <td>0.10940</td>\n",
              "      <td>-0.826670</td>\n",
              "      <td>0.62749</td>\n",
              "      <td>0.215730</td>\n",
              "      <td>0.470870</td>\n",
              "      <td>-0.282040</td>\n",
              "      <td>0.263790</td>\n",
              "      <td>0.268190</td>\n",
              "      <td>0.259380</td>\n",
              "      <td>0.188840</td>\n",
              "      <td>-0.453790</td>\n",
              "      <td>0.65771</td>\n",
              "      <td>0.18863</td>\n",
              "      <td>0.15029</td>\n",
              "      <td>-1.063600</td>\n",
              "      <td>0.627950</td>\n",
              "      <td>-0.965520</td>\n",
              "      <td>-0.237820</td>\n",
              "      <td>0.340370</td>\n",
              "      <td>0.51847</td>\n",
              "      <td>-0.422290</td>\n",
              "      <td>-0.214320</td>\n",
              "      <td>-0.104130</td>\n",
              "      <td>0.51002</td>\n",
              "      <td>-0.435780</td>\n",
              "      <td>-0.260600</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.562630</td>\n",
              "      <td>0.22614</td>\n",
              "      <td>-0.381360</td>\n",
              "      <td>-0.251940</td>\n",
              "      <td>-0.34625</td>\n",
              "      <td>-0.188070</td>\n",
              "      <td>-0.416370</td>\n",
              "      <td>-0.518740</td>\n",
              "      <td>0.551050</td>\n",
              "      <td>0.194110</td>\n",
              "      <td>0.175060</td>\n",
              "      <td>0.13755</td>\n",
              "      <td>0.162360</td>\n",
              "      <td>-0.584780</td>\n",
              "      <td>-0.30659</td>\n",
              "      <td>-0.878010</td>\n",
              "      <td>-0.496120</td>\n",
              "      <td>0.261300</td>\n",
              "      <td>-0.36305</td>\n",
              "      <td>0.566840</td>\n",
              "      <td>0.049381</td>\n",
              "      <td>-0.344210</td>\n",
              "      <td>0.90951</td>\n",
              "      <td>0.852430</td>\n",
              "      <td>1.08820</td>\n",
              "      <td>-0.236530</td>\n",
              "      <td>0.419880</td>\n",
              "      <td>0.287960</td>\n",
              "      <td>0.848410</td>\n",
              "      <td>-0.22789</td>\n",
              "      <td>-0.550270</td>\n",
              "      <td>-0.246690</td>\n",
              "      <td>0.190090</td>\n",
              "      <td>-0.452750</td>\n",
              "      <td>0.599870</td>\n",
              "      <td>0.68903</td>\n",
              "      <td>0.62804</td>\n",
              "      <td>-0.21833</td>\n",
              "      <td>-0.10639</td>\n",
              "      <td>-0.308880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110312</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110313</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110314</th>\n",
              "      <td>0.330170</td>\n",
              "      <td>-0.143070</td>\n",
              "      <td>-0.131440</td>\n",
              "      <td>-0.014791</td>\n",
              "      <td>-0.107520</td>\n",
              "      <td>0.003284</td>\n",
              "      <td>-0.16908</td>\n",
              "      <td>0.12471</td>\n",
              "      <td>0.16198</td>\n",
              "      <td>0.077754</td>\n",
              "      <td>-0.17810</td>\n",
              "      <td>0.205520</td>\n",
              "      <td>0.151380</td>\n",
              "      <td>-0.132800</td>\n",
              "      <td>-0.35236</td>\n",
              "      <td>-0.071375</td>\n",
              "      <td>-0.19114</td>\n",
              "      <td>-0.029959</td>\n",
              "      <td>0.026904</td>\n",
              "      <td>-0.148770</td>\n",
              "      <td>-0.838620</td>\n",
              "      <td>-0.377610</td>\n",
              "      <td>-0.020586</td>\n",
              "      <td>-0.388750</td>\n",
              "      <td>-0.082611</td>\n",
              "      <td>-0.15153</td>\n",
              "      <td>-0.23068</td>\n",
              "      <td>0.12477</td>\n",
              "      <td>0.054594</td>\n",
              "      <td>0.075513</td>\n",
              "      <td>0.419900</td>\n",
              "      <td>-0.021933</td>\n",
              "      <td>0.096259</td>\n",
              "      <td>-0.21576</td>\n",
              "      <td>-0.059682</td>\n",
              "      <td>0.001851</td>\n",
              "      <td>-0.097293</td>\n",
              "      <td>-0.45902</td>\n",
              "      <td>0.383740</td>\n",
              "      <td>-0.007226</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.038041</td>\n",
              "      <td>-0.44214</td>\n",
              "      <td>0.278130</td>\n",
              "      <td>-0.174710</td>\n",
              "      <td>0.18667</td>\n",
              "      <td>-0.079669</td>\n",
              "      <td>-0.478180</td>\n",
              "      <td>-0.198750</td>\n",
              "      <td>-0.056847</td>\n",
              "      <td>-0.097353</td>\n",
              "      <td>0.554240</td>\n",
              "      <td>0.12893</td>\n",
              "      <td>0.161910</td>\n",
              "      <td>0.189860</td>\n",
              "      <td>-0.35560</td>\n",
              "      <td>0.100390</td>\n",
              "      <td>-0.159350</td>\n",
              "      <td>-0.475390</td>\n",
              "      <td>0.65174</td>\n",
              "      <td>0.013868</td>\n",
              "      <td>-0.569670</td>\n",
              "      <td>-0.038193</td>\n",
              "      <td>0.34158</td>\n",
              "      <td>-0.069680</td>\n",
              "      <td>0.44658</td>\n",
              "      <td>-0.040184</td>\n",
              "      <td>-0.006468</td>\n",
              "      <td>0.115300</td>\n",
              "      <td>0.375530</td>\n",
              "      <td>0.25118</td>\n",
              "      <td>0.047441</td>\n",
              "      <td>-0.108890</td>\n",
              "      <td>0.046765</td>\n",
              "      <td>0.162810</td>\n",
              "      <td>0.387340</td>\n",
              "      <td>-0.11133</td>\n",
              "      <td>0.36294</td>\n",
              "      <td>0.00214</td>\n",
              "      <td>-0.37141</td>\n",
              "      <td>-0.045805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110315</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110316 rows × 100 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              0         1         2   ...       97       98        99\n",
              "0       0.000000  0.000000  0.000000  ...  0.00000  0.00000  0.000000\n",
              "1      -0.038194 -0.244870  0.728120  ... -0.14590  0.82780  0.270620\n",
              "2      -0.071953  0.231270  0.023731  ... -0.71895  0.86894  0.195390\n",
              "3      -0.270860  0.044006 -0.020260  ... -0.49230  0.63687  0.236420\n",
              "4      -0.152900 -0.242790  0.898370  ... -0.59100  1.00390  0.206640\n",
              "...          ...       ...       ...  ...      ...      ...       ...\n",
              "110311 -0.241980 -0.624000 -0.017437  ... -0.21833 -0.10639 -0.308880\n",
              "110312  0.000000  0.000000  0.000000  ...  0.00000  0.00000  0.000000\n",
              "110313  0.000000  0.000000  0.000000  ...  0.00000  0.00000  0.000000\n",
              "110314  0.330170 -0.143070 -0.131440  ...  0.00214 -0.37141 -0.045805\n",
              "110315  0.000000  0.000000  0.000000  ...  0.00000  0.00000  0.000000\n",
              "\n",
              "[110316 rows x 100 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaFM3TpzRenS"
      },
      "source": [
        "### 7.LSTM Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7wM2CkeROIv",
        "outputId": "2d293d51-53e8-423c-8014-451f80d6db9a"
      },
      "source": [
        "embedding_layer = Embedding(vocal_length, 100, weights = [emb_matrix], input_length=max_len, trainable=False)\n",
        "#------------Model\n",
        "model = Sequential()\n",
        "model.add(embedding_layer)\n",
        "model.add(LSTM(128))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss=\"binary_crossentropy\", metrics = ['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 100, 100)          11031600  \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 128)               117248    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 11,148,977\n",
            "Trainable params: 117,377\n",
            "Non-trainable params: 11,031,600\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ed5dGQUvbGRs"
      },
      "source": [
        "### 8.Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rgsihoUSr0l",
        "outputId": "ee1387f9-11a2-4699-c129-2fdfa799f17d"
      },
      "source": [
        "history = model.fit(X_train, y_train, batch_size=128, epochs=5, verbose=1, validation_split=0.2)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "250/250 [==============================] - 9s 38ms/step - loss: 0.3807 - accuracy: 0.8281 - val_loss: 0.3745 - val_accuracy: 0.8321\n",
            "Epoch 2/5\n",
            "250/250 [==============================] - 8s 33ms/step - loss: 0.3566 - accuracy: 0.8410 - val_loss: 0.3581 - val_accuracy: 0.8381\n",
            "Epoch 3/5\n",
            "250/250 [==============================] - 8s 31ms/step - loss: 0.3399 - accuracy: 0.8506 - val_loss: 0.3478 - val_accuracy: 0.8464\n",
            "Epoch 4/5\n",
            "250/250 [==============================] - 8s 31ms/step - loss: 0.3237 - accuracy: 0.8584 - val_loss: 0.3500 - val_accuracy: 0.8465\n",
            "Epoch 5/5\n",
            "250/250 [==============================] - 8s 31ms/step - loss: 0.3095 - accuracy: 0.8664 - val_loss: 0.3321 - val_accuracy: 0.8545\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kge74pkFbX0h"
      },
      "source": [
        "### 9.Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_F5GnGlTPkz",
        "outputId": "38e5aa68-5110-4421-c8ed-60d7955943cf"
      },
      "source": [
        "score = model.evaluate(X_test, y_test, verbose=1)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 3s 10ms/step - loss: 0.3356 - accuracy: 0.8558\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG8E3psAbbSB"
      },
      "source": [
        "### 10.Model Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "nS-WVlUhXQmD",
        "outputId": "92d47269-4831-40d1-f50d-6675c8f3b8fc"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N62Za50zbfXY"
      },
      "source": [
        "### 11.Model Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "UrlGxw0FXlkX",
        "outputId": "1eafb0df-6176-4973-96bc-da75ba981156"
      },
      "source": [
        "X[20]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Pretty bad PRC cheapie which I rarely bother to watch over again and it\\'s no wonder it\\'s slow and creaky and dull as a butter knife Mad doctor George Zucco is at it again turning a dimwitted farmhand in overalls (Glenn Strange into a wolf-man Unfortunately the makeup is virtually non-existent consisting only of a beard and dimestore fangs for the most part If it were not for Zucco and Strange\\'s presence along with the cute Anne Nagel this would be completely unwatchable Strange who would go on to play Frankenstein\\'s monster for Unuiversal in two years does a Lenny impression from \"Of Mice and Men\" it seems.*1/ (of Four)'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k55Yt0xmYHR1"
      },
      "source": [
        "def Result(X):\n",
        "  ex = tokernizer.texts_to_sequences(X)\n",
        "  flat_list = []\n",
        "  for subplit in tqdm(ex):\n",
        "    for item in subplit:\n",
        "      flat_list.append(item)\n",
        "  ex = pad_sequences([flat_list], padding=\"post\", maxlen=max_len)\n",
        "  n2c = [\"Negative\",\"Positive\"]\n",
        "  result = n2c[np.argmax(model.predict(ex))]\n",
        "  return result"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "35dde44773a3431ab1e784bca26c017e",
            "c289b7b8296a4b6eb2f2b2976733f4a1",
            "9d242748dc734863b232580d419989a0",
            "3f7f7ea88ed04bb2a4b2a63967930ec5",
            "51124c93db354432b6e8e241ede9720b",
            "05a550941bc84208a0cdf813a7cb8ce6",
            "b4568df326654d9da4be79a48f6286d3",
            "4af78b75a89e415e9f9d114f89798f85"
          ]
        },
        "id": "WOBR3cZFY035",
        "outputId": "28a3315d-fdd8-4201-d3f2-6047d3f3a4c5"
      },
      "source": [
        "Result(X[20])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35dde44773a3431ab1e784bca26c017e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=627.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Negative'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    }
  ]
}