TwitterDataAnalysisLSTM01.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "\n",
        "$$Suicidal Ideation$$\n",
        "Importing important libraries "
      ]
    },
    {
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      "execution_count": null,
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        "outputId": "80e9f4ea-9137-4a7b-c509-559ed9a2e87d"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "# from keras.models import Sequential \n",
        "# from keras.layers import Embedding, LSTM, Dense, Dropout\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAxLINFw_nzz"
      },
      "source": [
        "#Working the Twitter Sentiment Dataset  "
      ]
    },
    {
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      "execution_count": null,
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              "      <th></th>\n",
              "      <th>message</th>\n",
              "      <th>result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>when modi promised “minimum government maximum...</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
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              "      <td>kiya tho refresh maarkefir comment karo</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>9</th>\n",
              "      <td>gandhi was gay does modi</td>\n",
              "      <td>1.0</td>\n",
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            "text/plain": [
              "                                             message  result\n",
              "0  when modi promised “minimum government maximum...    -1.0\n",
              "1  talk all the nonsense and continue all the dra...     0.0\n",
              "2  what did just say vote for modi  welcome bjp t...     1.0\n",
              "3  asking his supporters prefix chowkidar their n...     1.0\n",
              "4  answer who among these the most powerful world...     1.0\n",
              "5           kiya tho refresh maarkefir comment karo      0.0\n",
              "6  surat women perform yagna seeks divine grace f...     0.0\n",
              "7  this comes from cabinet which has scholars lik...     0.0\n",
              "8  with upcoming election india saga going import...     1.0\n",
              "9                         gandhi was gay does modi       1.0"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentiment_data = pd.read_csv('/content/drive/MyDrive/Datasets /Twitter_Data.csv')\n",
        "sentiment_data=sentiment_data.rename(columns={'clean_text':'message', 'category':'result'})\n",
        "sentiment_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HdLAyUttlpn8"
      },
      "outputs": [],
      "source": [
        "#Unzipping the necessary datasets \n",
        "sentiment_data.dropna(subset = [\"message\"], inplace=True)\n",
        "sentiment_data.dropna(subset=['result'], inplace=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nXOMAG4IToJ"
      },
      "source": [
        "<h3> Distribution of the target variables </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CngnYbvBIgD",
        "outputId": "8f5abb6a-a0bc-4199-f8fa-a7c3c21c725e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<bound method DataFrame.dropna of                                                   message  result\n",
              "0       when modi promised “minimum government maximum...    -1.0\n",
              "1       talk all the nonsense and continue all the dra...     0.0\n",
              "2       what did just say vote for modi  welcome bjp t...     1.0\n",
              "3       asking his supporters prefix chowkidar their n...     1.0\n",
              "4       answer who among these the most powerful world...     1.0\n",
              "...                                                   ...     ...\n",
              "162975  why these 456 crores paid neerav modi not reco...    -1.0\n",
              "162976  dear rss terrorist payal gawar what about modi...    -1.0\n",
              "162977  did you cover her interaction forum where she ...     0.0\n",
              "162978  there big project came into india modi dream p...     0.0\n",
              "162979  have you ever listen about like gurukul where ...     1.0\n",
              "\n",
              "[162969 rows x 2 columns]>"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentiment_data.dropna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A1yZv66-taIX"
      },
      "outputs": [],
      "source": [
        "size = int(sentiment_data.shape[0]*0.01)\n",
        "sentiment_data = sentiment_data[:size]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVBETMO_ILUB"
      },
      "source": [
        "<h3> Factoring the sentences into words "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kS0Kw7ZYBvIJ",
        "outputId": "7bed0818-1915-4b3d-b24f-d3f284129def"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "when modi promised “minimum government maximum governance” expected him begin the difficult job reforming the state why does take years get justice state should and not business and should exit psus and temples\n",
            "<--------------------->\n",
            "['when', 'modi', 'promised', '“', 'minimum', 'government', 'maximum', 'governance', '”', 'expected', 'him', 'begin', 'the', 'difficult', 'job', 'reforming', 'the', 'state', 'why', 'does', 'take', 'years', 'get', 'justice', 'state', 'should', 'and', 'not', 'business', 'and', 'should', 'exit', 'psus', 'and', 'temples']\n"
          ]
        }
      ],
      "source": [
        "texts = sentiment_data['message'][0]\n",
        "print(texts)\n",
        "print('<--------------------->')\n",
        "print(word_tokenize(texts))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5zMn3E7lIBES"
      },
      "outputs": [],
      "source": [
        "corpus = []\n",
        "for text in sentiment_data['message']:\n",
        "    words = [word.lower() for word in word_tokenize(texts)]\n",
        "    corpus.append(words)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQyo-AYoJ2cd",
        "outputId": "d76f3a87-f91e-4324-b05d-e25738a46440"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1629\n"
          ]
        }
      ],
      "source": [
        "num_words = len(corpus)\n",
        "print(num_words)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9tNB7LxgGJk"
      },
      "source": [
        "<h3><b>Splitting Data to Train(80%) and Test(20%)</b></h3>\n",
        "In our case Train and Test data already splitted "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KJgZ_q5hEDD5"
      },
      "outputs": [],
      "source": [
        "X = sentiment_data['message']\n",
        "y = sentiment_data['result']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0gDTy-bfFrDq"
      },
      "outputs": [],
      "source": [
        "# Splitting data\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.20, random_state=25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiX_a9J4NxTD"
      },
      "source": [
        "<h3> Tokenizing the words and padding for equal input dimensions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f5zlizuQhH3C"
      },
      "outputs": [],
      "source": [
        "train_size = int(X_train.shape[0])\n",
        "X_train = X_train[:train_size]\n",
        "y_train = y_train[:train_size]\n",
        "test_size = int(X_test.shape[0])\n",
        "X_test = X_test[:test_size]\n",
        "y_test = X_test[:test_size]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G4BkIGQHKNGd"
      },
      "outputs": [],
      "source": [
        "tokenizer  = Tokenizer(num_words)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_train = pad_sequences(X_train, maxlen=128, truncating='post', padding='post')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQZqY5KGNtvk",
        "outputId": "27c3e221-f883-4723-99eb-79a5dfdecf16"
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        {
          "data": {
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          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train[0], len(X_train[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vL1MSs6Cjttt"
      },
      "source": [
        "<h3>Test</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ejFex0_xEzW1"
      },
      "outputs": [],
      "source": [
        "X_test = tokenizer.texts_to_sequences(X_test)\n",
        "X_test = pad_sequences(X_test, maxlen=128, truncating='post', padding='post')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "imv8N3h7ks8P",
        "outputId": "c0e9a54a-2dc7-4146-b2a9-4751506fd632"
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      "outputs": [
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      "source": [
        "X_test[0], len(X_test[0])"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvGXdIbQl6fU"
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      "source": [
        "<h3>Shape of my Dataset</h3> "
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      "cell_type": "code",
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      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "nI1axz6Clo1f",
        "outputId": "d3f2e3e3-88d4-481f-a479-127ba3625eac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1303, 128) (1303,)\n",
            "(326, 128) (326,)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape, y_train.shape)\n",
        "print(X_test.shape, y_test.shape)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPdPMFL4nKCT"
      },
      "source": [
        "<h3> <b>Encoding label</b> </h3>\n",
        "In this dataset no need to encoding label as they are already binary ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5p2BRfn9nJUL",
        "outputId": "5d59f7b9-418b-45e0-9a74-0ffd6aa24436"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([2, 0, 1, 1, 0, 1, 2, 0, 2, 2, 2, 1, 0, 2, 1, 0, 1, 1, 2, 1, 2, 1,\n",
              "       2, 1, 1, 1, 1, 2, 1, 2, 0, 1, 0, 2, 0, 2, 2, 2, 1, 1, 0, 2, 2, 0,\n",
              "       2, 1, 0, 1, 2, 2, 2, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 2, 0, 1, 1, 2,\n",
              "       2, 2, 2, 2, 1, 2, 0, 1, 0, 1, 2, 0, 1, 1, 2, 2, 2, 2, 0, 2, 2, 0,\n",
              "       0, 1, 2, 0, 2, 2, 1, 1, 2, 2, 1, 2])"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lab_enc  = LabelEncoder()\n",
        "y_train = lab_enc.fit_transform(y_train)\n",
        "y_test = lab_enc. fit_transform(y_test)\n",
        "y_train[:100]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCS3bex5saSf"
      },
      "source": [
        "<h3> Training the model </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5laNLpV3Ke8R"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2iZaUwHo2AX",
        "outputId": "427c0a6f-41c1-44bd-9c64-994a4161d351"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_2 (Embedding)     (None, None, 128)         208512    \n",
            "                                                                 \n",
            " bidirectional_2 (Bidirectio  (None, 256)              263168    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 505,350\n",
            "Trainable params: 505,350\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "embedding_dim = 128\n",
        "model = tf.keras.Sequential([\n",
        "    # Add an Embedding layer expecting input vocab of size 5000, and output embedding dimension of size 64 we set at the top\n",
        "    tf.keras.layers.Embedding(num_words, embedding_dim),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim)),\n",
        "    #tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    # use ReLU in place of tanh function since they are very good alternatives of each other.\n",
        "    tf.keras.layers.Dense(embedding_dim, activation='relu'),\n",
        "    # Add a Dense layer with 6 units and softmax activation.\n",
        "    # When we have multiple outputs, softmax convert outputs layers into a probability distribution.\n",
        "    tf.keras.layers.Dense(6, activation='softmax')\n",
        "])\n",
        "model.compile(\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    optimizer= 'adam',\n",
        "    metrics= ['accuracy']\n",
        ")\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5Xi7QhdsXq_",
        "outputId": "6bf21259-0547-49aa-8669-7440f9d855a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, None, 64)          104256    \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 128)              66048     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 6)                 390       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 178,950\n",
            "Trainable params: 178,950\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsraaDTTsyO2",
        "outputId": "f1e565df-2e12-46fd-8b70-7e2aedf6d44c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "ename": "InvalidArgumentError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-306095779820>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mHistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 59\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m:  Received a label value of 310 which is outside the valid range of [0, 6).  Label values: 302 117 74 159 93 299 46 230 101 290 185 193 120 282 119 278 26 310 254 226 202 108 38 207 161 305 215 168 137 138 155 52 70 152 63 42 56 151 304 252 30 286 81 217 189 76 10 36 292 245 2 219 106 266 75 164 203 210 223 17 167 154 68 104\n\t [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits\n (defined at /usr/local/lib/python3.7/dist-packages/keras/backend.py:5114)\n]] [Op:__inference_test_function_20374]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits:\nIn[0] sparse_categorical_crossentropy/Reshape_1 (defined at /usr/local/lib/python3.7/dist-packages/keras/backend.py:5109)\t\nIn[1] sparse_categorical_crossentropy/Reshape (defined at /usr/local/lib/python3.7/dist-packages/keras/backend.py:3561)\n\nOperation defined at: (most recent call last)\n>>>   File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n>>>     \"__main__\", mod_spec)\n>>> \n>>>   File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n>>>     exec(code, run_globals)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n>>>     app.launch_new_instance()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py\", line 846, in launch_instance\n>>>     app.start()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py\", line 499, in start\n>>>     self.io_loop.start()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 132, in start\n>>>     self.asyncio_loop.run_forever()\n>>> \n>>>   File \"/usr/lib/python3.7/asyncio/base_events.py\", line 541, in run_forever\n>>>     self._run_once()\n>>> \n>>>   File \"/usr/lib/python3.7/asyncio/base_events.py\", line 1786, in _run_once\n>>>     handle._run()\n>>> \n>>>   File \"/usr/lib/python3.7/asyncio/events.py\", line 88, in _run\n>>>     self._context.run(self._callback, *self._args)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n>>>     handler_func(fileobj, events)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n>>>     return fn(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py\", line 452, in _handle_events\n>>>     self._handle_recv()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py\", line 481, in _handle_recv\n>>>     self._run_callback(callback, msg)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py\", line 431, in _run_callback\n>>>     callback(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n>>>     return fn(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n>>>     return self.dispatch_shell(stream, msg)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n>>>     handler(stream, idents, msg)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n>>>     user_expressions, allow_stdin)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n>>>     res = shell.run_cell(code, store_history=store_history, silent=silent)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n>>>     return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2718, in run_cell\n>>>     interactivity=interactivity, compiler=compiler, result=result)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2822, in run_ast_nodes\n>>>     if self.run_code(code, result):\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n>>>     exec(code_obj, self.user_global_ns, self.user_ns)\n>>> \n>>>   File \"<ipython-input-44-306095779820>\", line 1, in <module>\n>>>     History=model.fit(X_train, y_train, epochs=10, batch_size=64, validation_data=(X_test,y_test), verbose=2)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n>>>     return fn(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1263, in fit\n>>>     _use_cached_eval_dataset=True)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n>>>     return fn(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1537, in evaluate\n>>>     tmp_logs = self.test_function(iterator)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1366, in test_function\n>>>     return step_function(self, iterator)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1356, in step_function\n>>>     outputs = model.distribute_strategy.run(run_step, args=(data,))\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1349, in run_step\n>>>     outputs = model.test_step(data)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1306, in test_step\n>>>     y, y_pred, sample_weight, regularization_losses=self.losses)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py\", line 201, in __call__\n>>>     loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 141, in __call__\n>>>     losses = call_fn(y_true, y_pred)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 245, in call\n>>>     return ag_fn(y_true, y_pred, **self._fn_kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/losses.py\", line 1738, in sparse_categorical_crossentropy\n>>>     y_true, y_pred, from_logits=from_logits, axis=axis)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/keras/backend.py\", line 5114, in sparse_categorical_crossentropy\n>>>     labels=target, logits=output)\n>>> "
          ]
        }
      ],
      "source": [
        "History=model.fit(X_train, y_train, epochs=10, batch_size=64, validation_data=(X_test,y_test), verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ucySHq4sxAQE"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "epochs = range(1, len(History.history['accuracy'])+1)\n",
        "plt.plot(epochs, History.history['loss'], 'b', label='Training Loss', color='red')\n",
        "plt.plot(epochs, History.history['val_loss'], 'b', label='Validation Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZTP7iUr29Dpv"
      },
      "outputs": [],
      "source": [
        "validation_sentence = ['I will commit suicide']\n",
        "validation_sentence_tockened = tokenizer.texts_to_sequences(validation_sentence)\n",
        "validation_sentence_padded = pad_sequences(validation_sentence_tockened, maxlen=16, truncating ='post', padding='post')\n",
        "print(validation_sentence[0])\n",
        "print('Probablity of Suicide:{}'.format(model.predict(validation_sentence_padded)[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sgUsKeJJA1en"
      },
      "outputs": [],
      "source": [
        "validation_sentence = ['I am really happy in my life ']\n",
        "validation_sentence_tockened = tokenizer.texts_to_sequences(validation_sentence)\n",
        "validation_sentence_padded = pad_sequences(validation_sentence_tockened, maxlen=16, truncating ='post', padding='post')\n",
        "print(validation_sentence[0])\n",
        "print('Probablity of Suicide:{}'.format(model.predict(validation_sentence_padded)[0]))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [],
      "name": "TwitterDataAnalysisLSTM01.ipynb",
      "provenance": [],
      "mount_file_id": "1cPmnkZ225HPS662Qw25_BjR_sL3h-Ifj",
      "authorship_tag": "ABX9TyNkKx09aB2SWSABs5lDch23",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}