Sentiment_Analysis.ipynb

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    "colab": {
      "name": "IITK Sentiment Analysis .ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyMyHli3mD918OrRWw7R+4NB",
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/triakshunn/Sentiment-Analysis-for-twitter-data/blob/main/IITK_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": "xKXCY8I4N1W9"
      },
      "source": [
        "Sentiment Analysis model using LSTM,CNN's and w2v"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VPZwyvgtN5lJ"
      },
      "source": [
        "Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_LjvcErNQOZ"
      },
      "source": [
        "import tensorflow as tf\n",
        "import re\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from  nltk.stem import SnowballStemmer\n",
        "import gensim\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation, Dense, Dropout, Embedding, LSTM\n",
        "from keras import utils\n",
        "from keras import layers\n",
        "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.layers.convolutional import Conv1D\n",
        "from keras.layers.convolutional import MaxPooling1D"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37hardeWOc7c"
      },
      "source": [
        "Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVXToAUYO_yt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "e1f607a7-ec39-4504-e958-d0525c5ca0de"
      },
      "source": [
        "train_data=pd.read_csv('/content/train_data_1.csv')\n",
        "train_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>tweet_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.641838e+17</td>\n",
              "      <td>positive</td>\n",
              "      <td>Gas by my house hit $3.39!!!! I\\u2019m going t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.634051e+17</td>\n",
              "      <td>negative</td>\n",
              "      <td>Theo Walcott is still shit\\u002c watch Rafa an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.621632e+17</td>\n",
              "      <td>negative</td>\n",
              "      <td>its not that I\\u2019m a GSP fan\\u002c i just h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.642493e+17</td>\n",
              "      <td>negative</td>\n",
              "      <td>Iranian general says Israel\\u2019s Iron Dome c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.626820e+17</td>\n",
              "      <td>neutral</td>\n",
              "      <td>Tehran\\u002c Mon Amour: Obama Tried to Establi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21460</th>\n",
              "      <td>5.229490e+17</td>\n",
              "      <td>neutral</td>\n",
              "      <td>the day after newark ill be able to say \"\"i me...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21461</th>\n",
              "      <td>5.223726e+17</td>\n",
              "      <td>neutral</td>\n",
              "      <td>FEC hold farewell session for seven ministers ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21462</th>\n",
              "      <td>5.225152e+17</td>\n",
              "      <td>neutral</td>\n",
              "      <td>Luca Di Montezemolo (who's last day was Monday...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21463</th>\n",
              "      <td>5.230891e+17</td>\n",
              "      <td>positive</td>\n",
              "      <td>Coffee is pretty much the answer to all questi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21464</th>\n",
              "      <td>5.182909e+17</td>\n",
              "      <td>neutral</td>\n",
              "      <td>Niki Lauda just confirmed to Sky that Alonso w...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21465 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           tweet_id  ...                                         tweet_text\n",
              "0      2.641838e+17  ...  Gas by my house hit $3.39!!!! I\\u2019m going t...\n",
              "1      2.634051e+17  ...  Theo Walcott is still shit\\u002c watch Rafa an...\n",
              "2      2.621632e+17  ...  its not that I\\u2019m a GSP fan\\u002c i just h...\n",
              "3      2.642493e+17  ...  Iranian general says Israel\\u2019s Iron Dome c...\n",
              "4      2.626820e+17  ...  Tehran\\u002c Mon Amour: Obama Tried to Establi...\n",
              "...             ...  ...                                                ...\n",
              "21460  5.229490e+17  ...  the day after newark ill be able to say \"\"i me...\n",
              "21461  5.223726e+17  ...  FEC hold farewell session for seven ministers ...\n",
              "21462  5.225152e+17  ...  Luca Di Montezemolo (who's last day was Monday...\n",
              "21463  5.230891e+17  ...  Coffee is pretty much the answer to all questi...\n",
              "21464  5.182909e+17  ...  Niki Lauda just confirmed to Sky that Alonso w...\n",
              "\n",
              "[21465 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1wAIRvyPi_s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "1789eac4-774a-4f1a-d039-a20659909fec"
      },
      "source": [
        "test_data=pd.read_csv('/content/Test_data_IITK.csv')\n",
        "test_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>tweet_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>264238274963451904</td>\n",
              "      <td>@jjuueellzz down in the Atlantic city, ventnor...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>218775148495515649</td>\n",
              "      <td>Musical awareness: Great Big Beautiful Tomorro...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>258965201766998017</td>\n",
              "      <td>On Radio786 100.4fm 7:10 Fri Oct 19 Labour ana...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>262926411352903682</td>\n",
              "      <td>Kapan sih lo ngebuktiin,jan ngomong doang Susa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>171874368908050432</td>\n",
              "      <td>Excuse the connectivity of this live stream, f...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5393</th>\n",
              "      <td>210378118865756160</td>\n",
              "      <td>It's a Wednesday girls night out as '90's band...</td>\n",
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              "    <tr>\n",
              "      <th>5394</th>\n",
              "      <td>245177521304399872</td>\n",
              "      <td>night college course sorted, just have to enro...</td>\n",
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              "    <tr>\n",
              "      <th>5395</th>\n",
              "      <td>259280987089932288</td>\n",
              "      <td>For the 1st time in 30 years. For your splendi...</td>\n",
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              "    <tr>\n",
              "      <th>5396</th>\n",
              "      <td>201113950211940352</td>\n",
              "      <td>NURSES DAY - 12 MAY 2012. Nursing: The heart b...</td>\n",
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              "      <th>5397</th>\n",
              "      <td>237999067286876160</td>\n",
              "      <td>We have 15 minutes left until the 2nd episode ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5398 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                tweet_id                                         tweet_text\n",
              "0     264238274963451904  @jjuueellzz down in the Atlantic city, ventnor...\n",
              "1     218775148495515649  Musical awareness: Great Big Beautiful Tomorro...\n",
              "2     258965201766998017  On Radio786 100.4fm 7:10 Fri Oct 19 Labour ana...\n",
              "3     262926411352903682  Kapan sih lo ngebuktiin,jan ngomong doang Susa...\n",
              "4     171874368908050432  Excuse the connectivity of this live stream, f...\n",
              "...                  ...                                                ...\n",
              "5393  210378118865756160  It's a Wednesday girls night out as '90's band...\n",
              "5394  245177521304399872  night college course sorted, just have to enro...\n",
              "5395  259280987089932288  For the 1st time in 30 years. For your splendi...\n",
              "5396  201113950211940352  NURSES DAY - 12 MAY 2012. Nursing: The heart b...\n",
              "5397  237999067286876160  We have 15 minutes left until the 2nd episode ...\n",
              "\n",
              "[5398 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C25Ar39rSWKA"
      },
      "source": [
        "Data pre-processing starts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngkuPR98QBhg"
      },
      "source": [
        "def convert_text_to_number(dataset,column):\n",
        "     dataset[str(column)]=dataset[str(column)].apply(lambda x:re.sub('positive','0',x))\n",
        "     dataset[str(column)]=dataset[str(column)].apply(lambda x:re.sub('negative','1',x))\n",
        "     dataset[str(column)]=dataset[str(column)].apply(lambda x:re.sub('neutral','2',x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkVXLwbpQFV_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "1b42a48c-23d7-4289-e77d-2a082ecefc2b"
      },
      "source": [
        "convert_text_to_number(train_data,'sentiment')\n",
        "train_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_id</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>tweet_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.641838e+17</td>\n",
              "      <td>0</td>\n",
              "      <td>Gas by my house hit $3.39!!!! I\\u2019m going t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.634051e+17</td>\n",
              "      <td>1</td>\n",
              "      <td>Theo Walcott is still shit\\u002c watch Rafa an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.621632e+17</td>\n",
              "      <td>1</td>\n",
              "      <td>its not that I\\u2019m a GSP fan\\u002c i just h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.642493e+17</td>\n",
              "      <td>1</td>\n",
              "      <td>Iranian general says Israel\\u2019s Iron Dome c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.626820e+17</td>\n",
              "      <td>2</td>\n",
              "      <td>Tehran\\u002c Mon Amour: Obama Tried to Establi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>21460</th>\n",
              "      <td>5.229490e+17</td>\n",
              "      <td>2</td>\n",
              "      <td>the day after newark ill be able to say \"\"i me...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21461</th>\n",
              "      <td>5.223726e+17</td>\n",
              "      <td>2</td>\n",
              "      <td>FEC hold farewell session for seven ministers ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21462</th>\n",
              "      <td>5.225152e+17</td>\n",
              "      <td>2</td>\n",
              "      <td>Luca Di Montezemolo (who's last day was Monday...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21463</th>\n",
              "      <td>5.230891e+17</td>\n",
              "      <td>0</td>\n",
              "      <td>Coffee is pretty much the answer to all questi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21464</th>\n",
              "      <td>5.182909e+17</td>\n",
              "      <td>2</td>\n",
              "      <td>Niki Lauda just confirmed to Sky that Alonso w...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21465 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           tweet_id  ...                                         tweet_text\n",
              "0      2.641838e+17  ...  Gas by my house hit $3.39!!!! I\\u2019m going t...\n",
              "1      2.634051e+17  ...  Theo Walcott is still shit\\u002c watch Rafa an...\n",
              "2      2.621632e+17  ...  its not that I\\u2019m a GSP fan\\u002c i just h...\n",
              "3      2.642493e+17  ...  Iranian general says Israel\\u2019s Iron Dome c...\n",
              "4      2.626820e+17  ...  Tehran\\u002c Mon Amour: Obama Tried to Establi...\n",
              "...             ...  ...                                                ...\n",
              "21460  5.229490e+17  ...  the day after newark ill be able to say \"\"i me...\n",
              "21461  5.223726e+17  ...  FEC hold farewell session for seven ministers ...\n",
              "21462  5.225152e+17  ...  Luca Di Montezemolo (who's last day was Monday...\n",
              "21463  5.230891e+17  ...  Coffee is pretty much the answer to all questi...\n",
              "21464  5.182909e+17  ...  Niki Lauda just confirmed to Sky that Alonso w...\n",
              "\n",
              "[21465 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjKYqO5CQLY4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "3efce485-29ec-4e38-8ff8-5977add32897"
      },
      "source": [
        "train_data=train_data.drop('tweet_id',axis=1)\n",
        "train_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21465 entries, 0 to 21464\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   sentiment   21465 non-null  object\n",
            " 1   tweet_text  21465 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 335.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLN7sb-BSZb8"
      },
      "source": [
        "Cleaning the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIgQcKdGQMo9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "46588798-129f-4546-96c9-b371b8211126"
      },
      "source": [
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "stop_words=stopwords.words(\"english\")\n",
        "stop_words=stop_words[:116] # #Left the rest of the words because they include 'don't','not' etc which may help deciding polarisibilty.\n",
        "stop_words\n",
        "stemmer=SnowballStemmer(\"english\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSrPwumMQUJ3"
      },
      "source": [
        "urlPattern= r\"((http://)[^ ]*|(https://)[^ ]*|( www\\.)[^ ]*)\"\n",
        "userPattern= '@[^\\s]+'\n",
        "alphaPattern= \"[^a-zA-Z0-9]\"\n",
        "sequencePattern= r\"(.)\\1\\1+\"\n",
        "seqReplacePattern= r\"\\1\\1\"\n",
        "emojis = {':)': 'smile', ':-)': 'smile', ';d': 'wink', ':-E': 'vampire', ':(': 'sad', \n",
        "          ':-(': 'sad', ':-<': 'sad', ':P': 'raspberry', ':O': 'surprised',\n",
        "          ':-@': 'shocked', ':@': 'shocked',':-$': 'confused', ':\\\\': 'annoyed', \n",
        "          ':#': 'mute', ':X': 'mute', ':^)': 'smile', ':-&': 'confused', '$_$': 'greedy',\n",
        "          '@@': 'eyeroll', ':-!': 'confused', ':-D': 'smile', ':-0': 'yell', 'O.o': 'confused',\n",
        "          '<(-_-)>': 'robot', 'd[-_-]b': 'dj', \":'-)\": 'sadsmile', ';)': 'wink', \n",
        "          ';-)': 'wink', 'O:-)': 'angel','O*-)': 'angel','(:-D': 'gossip', '=^.^=': 'cat'}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTJ2i8prQVVM"
      },
      "source": [
        "def data_cleaning(text,stem=False):\n",
        "    for emoji in emojis.keys():\n",
        "        text=text.replace(emoji,\"EMOJI\"+emojis[emoji])\n",
        "    text=re.sub(urlPattern,' URL',str(text).lower()).strip()\n",
        "    text=re.sub(userPattern,' USER',str(text).lower()).strip()\n",
        "    text=re.sub(alphaPattern,' ',str(text).lower()).strip()\n",
        "    text=re.sub(sequencePattern,seqReplacePattern,str(text).lower()).strip()\n",
        "    new_text=[]\n",
        "    for texts in text.split():\n",
        "        if texts not in stop_words:\n",
        "            if stem:\n",
        "                new_text.append(stemmer.stem(texts))\n",
        "            else:\n",
        "                new_text.append(texts)\n",
        "    return \" \".join(new_text)            \n",
        "            "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqtRSOzFQY-Z"
      },
      "source": [
        "train_data[\"tweet_text\"]=train_data[\"tweet_text\"].apply(lambda x: data_cleaning(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHhhBq2oQbha"
      },
      "source": [
        "test_data[\"tweet_text\"]=test_data[\"tweet_text\"].apply(lambda x: data_cleaning(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agsnXLlvO2bD"
      },
      "source": [
        "w2v starts here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtlfODQNO30k"
      },
      "source": [
        "Defining the constants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayNenJHE0rn6"
      },
      "source": [
        "W2V_SIZE = 300\n",
        "W2V_WINDOW = 7\n",
        "W2V_EPOCH = 32\n",
        "W2V_MIN_COUNT = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9lHj024d0JLf"
      },
      "source": [
        "documents = [_text.split() for _text in train_data.tweet_text] \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcqUtMKe0kvy"
      },
      "source": [
        "w2v_model = gensim.models.word2vec.Word2Vec(size=W2V_SIZE, \n",
        "                                            window=W2V_WINDOW, \n",
        "                                            min_count=W2V_MIN_COUNT,\n",
        "                                            workers=8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LciWMxvu0woh"
      },
      "source": [
        "w2v_model.build_vocab(documents)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfsP43Du02Pp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "055d14eb-6492-481f-f51e-289466c5ce44"
      },
      "source": [
        "words = w2v_model.wv.vocab.keys()\n",
        "vocab_size = len(words)\n",
        "print(\"Vocab size\", vocab_size)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocab size 3969\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUpndDzJ06ft",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "1dbbe2ad-0adb-4a7c-8564-09baeb737a46"
      },
      "source": [
        "%%time\n",
        "w2v_model.train(documents, total_examples=len(documents), epochs=W2V_EPOCH)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 32.7 s, sys: 70.1 ms, total: 32.8 s\n",
            "Wall time: 17.3 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6796010, 9385280)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HA6upQsH1LY3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "1f698ab1-1a5f-4718-b49c-5b3b4c6561cd"
      },
      "source": [
        "w2v_model.most_similar(\"love\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('xx', 0.44422441720962524),\n",
              " ('x', 0.4244207441806793),\n",
              " ('appreciate', 0.42357149720191956),\n",
              " ('poetic', 0.4039784073829651),\n",
              " ('thought', 0.39778438210487366),\n",
              " ('stan', 0.39643192291259766),\n",
              " ('thank', 0.3956957161426544),\n",
              " ('hate', 0.3933345079421997),\n",
              " ('zayn', 0.3848564922809601),\n",
              " ('mind', 0.38388752937316895)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7izufIdQenf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "75c5ced8-843a-4323-a248-b51d8afb6903"
      },
      "source": [
        "tokenizer=Tokenizer()\n",
        "tokenizer.fit_on_texts(train_data[\"tweet_text\"])\n",
        "vocabulary_size = len(tokenizer.word_index) + 1\n",
        "vocabulary_size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28290"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJLC1AXMQkDz"
      },
      "source": [
        "SEQUENCE_LENGTH = 300"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQwOej8KQm58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f665747e-aa1c-4435-9c1c-0c4c6c61b773"
      },
      "source": [
        "x_train = pad_sequences(tokenizer.texts_to_sequences(train_data.tweet_text), maxlen=SEQUENCE_LENGTH)\n",
        "x_test = pad_sequences(tokenizer.texts_to_sequences(test_data.tweet_text), maxlen=SEQUENCE_LENGTH)\n",
        "x_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5398, 300)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DrcK__iQrJV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "d2ab4ae1-0e9d-4a5b-f116-c3177693cf5c"
      },
      "source": [
        "from keras.utils.np_utils import to_categorical\n",
        "y_train=train_data.sentiment\n",
        "y_train_oh = to_categorical(y_train)\n",
        "y_train_oh"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 1., 0.],\n",
              "       ...,\n",
              "       [0., 0., 1.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwqFWh8z1clJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "acc88921-1a38-449a-9f89-5913c7c75a1b"
      },
      "source": [
        "embedding_matrix = np.zeros((vocabulary_size, W2V_SIZE))\n",
        "for word, i in tokenizer.word_index.items():\n",
        "  if word in w2v_model.wv:\n",
        "    embedding_matrix[i] = w2v_model.wv[word]\n",
        "print(embedding_matrix.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28290, 300)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5IQCat71gon"
      },
      "source": [
        "embedding_layer = Embedding(vocabulary_size, W2V_SIZE, weights=[embedding_matrix], input_length=SEQUENCE_LENGTH, trainable=False)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-d5e1IYQbLD"
      },
      "source": [
        "w2v training ends here;Also data has been processed to feed to our model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WslssWIQfFi"
      },
      "source": [
        "Training starts here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1JuwFOnQyK2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "61ed10c0-eab2-4cc2-cf53-8ce160157471"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(embedding_layer)\n",
        "model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))\n",
        "model.add(MaxPooling1D(pool_size=2))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(LSTM(200,dropout=0.2,recurrent_dropout=0.2))\n",
        "model.add(Dense(units = 150, activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(3, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 300, 300)          8487000   \n",
            "_________________________________________________________________\n",
            "conv1d_4 (Conv1D)            (None, 300, 32)           28832     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_4 (MaxPooling1 (None, 150, 32)           0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 150, 32)           0         \n",
            "_________________________________________________________________\n",
            "lstm_4 (LSTM)                (None, 200)               186400    \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 150)               30150     \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 150)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 3)                 453       \n",
            "=================================================================\n",
            "Total params: 8,732,835\n",
            "Trainable params: 245,835\n",
            "Non-trainable params: 8,487,000\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dXnlakhQ2Aw"
      },
      "source": [
        "callbacks = [ ReduceLROnPlateau(monitor='val_loss', patience=5, cooldown=0),\n",
        "             EarlyStopping(monitor='val_accuracy', min_delta=1e-4, patience=5)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lASaGAzdQ4xF"
      },
      "source": [
        "EPOCHS = 20\n",
        "BATCH_SIZE = 1024"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0RLR7wyQ5eL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "cda8e6a0-73c5-47b7-fad3-4bbb0e2704bd"
      },
      "source": [
        "%%time\n",
        "history = model.fit(x_train, y_train_oh,\n",
        "                    batch_size=BATCH_SIZE,\n",
        "                    epochs=EPOCHS,\n",
        "                    validation_split=0.05,\n",
        "                    verbose=1,\n",
        "                    callbacks=callbacks)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 20391 samples, validate on 1074 samples\n",
            "Epoch 1/20\n",
            "20391/20391 [==============================] - 12s 586us/step - loss: 0.6082 - accuracy: 0.6593 - val_loss: 0.5506 - val_accuracy: 0.7272\n",
            "Epoch 2/20\n",
            "20391/20391 [==============================] - 11s 539us/step - loss: 0.5588 - accuracy: 0.7074 - val_loss: 0.5180 - val_accuracy: 0.7418\n",
            "Epoch 3/20\n",
            "20391/20391 [==============================] - 11s 542us/step - loss: 0.5334 - accuracy: 0.7279 - val_loss: 0.5042 - val_accuracy: 0.7461\n",
            "Epoch 4/20\n",
            "20391/20391 [==============================] - 11s 540us/step - loss: 0.5239 - accuracy: 0.7345 - val_loss: 0.5015 - val_accuracy: 0.7502\n",
            "Epoch 5/20\n",
            "20391/20391 [==============================] - 11s 549us/step - loss: 0.5169 - accuracy: 0.7386 - val_loss: 0.4986 - val_accuracy: 0.7523\n",
            "Epoch 6/20\n",
            "20391/20391 [==============================] - 11s 541us/step - loss: 0.5104 - accuracy: 0.7459 - val_loss: 0.4981 - val_accuracy: 0.7542\n",
            "Epoch 7/20\n",
            "20391/20391 [==============================] - 11s 543us/step - loss: 0.5064 - accuracy: 0.7456 - val_loss: 0.4985 - val_accuracy: 0.7508\n",
            "Epoch 8/20\n",
            "20391/20391 [==============================] - 11s 543us/step - loss: 0.5043 - accuracy: 0.7489 - val_loss: 0.4946 - val_accuracy: 0.7536\n",
            "Epoch 9/20\n",
            "20391/20391 [==============================] - 11s 546us/step - loss: 0.5006 - accuracy: 0.7505 - val_loss: 0.4948 - val_accuracy: 0.7554\n",
            "Epoch 10/20\n",
            "20391/20391 [==============================] - 11s 538us/step - loss: 0.4986 - accuracy: 0.7519 - val_loss: 0.4941 - val_accuracy: 0.7536\n",
            "Epoch 11/20\n",
            "20391/20391 [==============================] - 11s 546us/step - loss: 0.4959 - accuracy: 0.7547 - val_loss: 0.4935 - val_accuracy: 0.7511\n",
            "Epoch 12/20\n",
            "20391/20391 [==============================] - 11s 551us/step - loss: 0.4939 - accuracy: 0.7548 - val_loss: 0.4956 - val_accuracy: 0.7523\n",
            "Epoch 13/20\n",
            "20391/20391 [==============================] - 11s 540us/step - loss: 0.4905 - accuracy: 0.7577 - val_loss: 0.4921 - val_accuracy: 0.7567\n",
            "Epoch 14/20\n",
            "20391/20391 [==============================] - 11s 545us/step - loss: 0.4889 - accuracy: 0.7587 - val_loss: 0.4933 - val_accuracy: 0.7523\n",
            "Epoch 15/20\n",
            "20391/20391 [==============================] - 11s 536us/step - loss: 0.4878 - accuracy: 0.7601 - val_loss: 0.4916 - val_accuracy: 0.7561\n",
            "Epoch 16/20\n",
            "20391/20391 [==============================] - 11s 541us/step - loss: 0.4862 - accuracy: 0.7616 - val_loss: 0.4919 - val_accuracy: 0.7567\n",
            "Epoch 17/20\n",
            "20391/20391 [==============================] - 11s 536us/step - loss: 0.4840 - accuracy: 0.7626 - val_loss: 0.4902 - val_accuracy: 0.7551\n",
            "Epoch 18/20\n",
            "20391/20391 [==============================] - 11s 537us/step - loss: 0.4810 - accuracy: 0.7642 - val_loss: 0.4928 - val_accuracy: 0.7539\n",
            "CPU times: user 4min 27s, sys: 38.2 s, total: 5min 5s\n",
            "Wall time: 3min 21s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G9qGXb7UQsvE"
      },
      "source": [
        "Training ended"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cn2VtKfQQvA8"
      },
      "source": [
        "Code for predicting the output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXSSKyplQ8q9"
      },
      "source": [
        "def predict(model):\n",
        "    \n",
        "    \n",
        "    history=model.predict(x_test\n",
        "                         ,batch_size=BATCH_SIZE\n",
        "                         ,callbacks=callbacks\n",
        "                         ,verbose=1)\n",
        "    return history\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcmSZkcmR-0k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6147dfe1-7175-458b-c452-7cdc8c61fb40"
      },
      "source": [
        "output=predict(model)\n",
        "print(len(predict(model)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5398/5398 [==============================] - 1s 173us/step\n",
            "5398/5398 [==============================] - 1s 157us/step\n",
            "5398\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeqgovOaSDGl"
      },
      "source": [
        "array_init=np.argmax(output, axis=1)  # using argmax to get output from one hot encode to '0','1','2'.\n",
        "array_init=list(array_init)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ILZeR-QSHW5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "91e43a4b-77a1-45c6-b276-17a7e526d159"
      },
      "source": [
        "for i in range(len(predict(model))): #converting numbers to positive,negative,neutral.\n",
        "    if array_init[i]==0:\n",
        "        array_init[i]='positive'\n",
        "    elif array_init[i]==1:\n",
        "        array_init[i]='negative'\n",
        "    elif array_init[i]==2:\n",
        "        array_init[i]='neutral'\n",
        "array_init"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5398/5398 [==============================] - 1s 156us/step\n"
          ],
          "name": "stdout"
        },
        {
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              " 'neutral',\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJY2tGU5RZ9l"
      },
      "source": [
        "Converting the output to pandas dataframe for evaluation in Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sErWrBRKSKBg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ae5a61ee-90ae-4e19-d3af-3c97a8e6c1b1"
      },
      "source": [
        "output = pd.DataFrame({'tweet_id': test_data.tweet_id, 'sentiment': array_init})\n",
        "output.to_csv('my_submission.csv', index=False)\n",
        "print(\"Your submission was successfully saved!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your submission was successfully saved!\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}