Twitter_Sentiment_Analysis.ipynb

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        "id": "EMlnBf2LxDL1"
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      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"../content/drive/MyDrive/Twitter_Data.csv\")"
      ],
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        "id": "Fhgdm6nvxSf-"
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      "execution_count": 3,
      "outputs": []
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              "                                          clean_text  category\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"
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        "import numpy as np"
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              "                                          clean_text  category  Labels\n",
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              "                                          clean_text  Labels\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9eb42ea0-01e1-442a-b54d-0d93622f3df1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9eb42ea0-01e1-442a-b54d-0d93622f3df1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Labels'].values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BXHS-ISz7pN",
        "outputId": "fb9f2697-d4d8-46cf-c49b-d9095b8c1e78"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2., 0., 1., ..., 0., 0., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j1spq7qL5u6I",
        "outputId": "c047cac8-5c9e-4d13-8f36-998fcd7d7695"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clean_text    4\n",
              "Labels        7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "4bfFet4C5zCP"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abgTIZ5T54Xl",
        "outputId": "064dd6b4-f80d-4d5c-f8fd-9f2e5d0b570a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "clean_text    0\n",
              "Labels        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "y=tf.keras.utils.to_categorical(df['Labels'].values,num_classes=num_classes)\n",
        "x_train,x_test,y_train,y_test=train_test_split(df['clean_text'],y,test_size=0.2)"
      ],
      "metadata": {
        "id": "ni2wPc5yylOP"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upjtzCJnzGQb",
        "outputId": "72eb33df-c97f-4d4f-8b19-b8fbcdb24169"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [1., 0., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6ZFjPm876LIK",
        "outputId": "817279be-a769-4aaa-f2d6-b285583c5f45"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow_text\n",
            "  Downloading tensorflow_text-2.10.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.9 MB 6.3 MB/s \n",
            "\u001b[?25hCollecting tensorflow<2.11,>=2.10.0\n",
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            "\u001b[K     |████████████████████████████████| 578.0 MB 17 kB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow-hub>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_text) (0.12.0)\n",
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            "Collecting flatbuffers>=2.0\n",
            "  Downloading flatbuffers-22.10.26-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.11,>=2.10.0->tensorflow_text) (57.4.0)\n",
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            "  Downloading tensorboard-2.10.1-py3-none-any.whl (5.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.9 MB 57.2 MB/s \n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.11,>=2.10.0->tensorflow_text) (21.3)\n",
            "Collecting keras<2.11,>=2.10.0\n",
            "  Downloading keras-2.10.0-py2.py3-none-any.whl (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 48.0 MB/s \n",
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            "Collecting tensorflow-estimator<2.11,>=2.10.0\n",
            "  Downloading tensorflow_estimator-2.10.0-py2.py3-none-any.whl (438 kB)\n",
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            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (2.23.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (0.4.6)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (1.0.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (4.2.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (4.13.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (3.10.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (2022.9.24)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (1.24.3)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow<2.11,>=2.10.0->tensorflow_text) (3.2.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow<2.11,>=2.10.0->tensorflow_text) (3.0.9)\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, keras, flatbuffers, tensorflow, tensorflow-text\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.9.0\n",
            "    Uninstalling tensorflow-estimator-2.9.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.9.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.9.1\n",
            "    Uninstalling tensorboard-2.9.1:\n",
            "      Successfully uninstalled tensorboard-2.9.1\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.9.0\n",
            "    Uninstalling keras-2.9.0:\n",
            "      Successfully uninstalled keras-2.9.0\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 1.12\n",
            "    Uninstalling flatbuffers-1.12:\n",
            "      Successfully uninstalled flatbuffers-1.12\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.9.2\n",
            "    Uninstalling tensorflow-2.9.2:\n",
            "      Successfully uninstalled tensorflow-2.9.2\n",
            "Successfully installed flatbuffers-22.10.26 keras-2.10.0 tensorboard-2.10.1 tensorflow-2.10.0 tensorflow-estimator-2.10.0 tensorflow-text-2.10.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "flatbuffers",
                  "keras",
                  "tensorboard",
                  "tensorflow",
                  "tensorflow_estimator"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text\n",
        "\n",
        "encoder=hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4\")\n",
        "preprocessor=hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xo-Hyw2J6vXh",
        "outputId": "32d7e572-0820-448d-81dd-b5de9c4fc6af"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_embeddings(sentences):\n",
        "  preprocessed_text=preprocessor(sentences)\n",
        "  return encoder(preprocessed_text)['pooled_output']"
      ],
      "metadata": {
        "id": "Qyx5IgsQ7Y0-"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "\n",
        "def balanced_recall(y_true, y_pred):\n",
        "    \"\"\"This function calculates the balanced recall metric\n",
        "    recall = TP / (TP + FN)\n",
        "    \"\"\"\n",
        "    recall_by_class = 0\n",
        "    # iterate over each predicted class to get class-specific metric\n",
        "    for i in range(y_pred.shape[1]):\n",
        "        y_pred_class = y_pred[:, i]\n",
        "        y_true_class = y_true[:, i]\n",
        "        true_positives = K.sum(K.round(K.clip(y_true_class * y_pred_class, 0, 1)))\n",
        "        possible_positives = K.sum(K.round(K.clip(y_true_class, 0, 1)))\n",
        "        recall = true_positives / (possible_positives + K.epsilon())\n",
        "        recall_by_class = recall_by_class + recall\n",
        "    return recall_by_class / y_pred.shape[1]\n",
        "\n",
        "def balanced_precision(y_true, y_pred):\n",
        "    \"\"\"This function calculates the balanced precision metric\n",
        "    precision = TP / (TP + FP)\n",
        "    \"\"\"\n",
        "    precision_by_class = 0\n",
        "    # iterate over each predicted class to get class-specific metric\n",
        "    for i in range(y_pred.shape[1]):\n",
        "        y_pred_class = y_pred[:, i]\n",
        "        y_true_class = y_true[:, i]\n",
        "        true_positives = K.sum(K.round(K.clip(y_true_class * y_pred_class, 0, 1)))\n",
        "        predicted_positives = K.sum(K.round(K.clip(y_pred_class, 0, 1)))\n",
        "        precision = true_positives / (predicted_positives + K.epsilon())\n",
        "        precision_by_class = precision_by_class + precision\n",
        "    # return average balanced metric for each class\n",
        "    return precision_by_class / y_pred.shape[1]\n",
        "\n",
        "def balanced_f1_score(y_true, y_pred):\n",
        "    \"\"\"This function calculates the F1 score metric\"\"\"\n",
        "    precision = balanced_precision(y_true, y_pred)\n",
        "    recall = balanced_recall(y_true, y_pred)\n",
        "    return 2 * ((precision * recall) / (precision + recall + K.epsilon()))"
      ],
      "metadata": {
        "id": "oU3ggkJN7sBJ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i=tf.keras.layers.Input(shape=(),dtype=tf.string,name='text')\n",
        "x=preprocessor(i)\n",
        "x=encoder(x)\n",
        "x=tf.keras.layers.Dropout(0.2,name='dropout')(x['pooled_output'])\n",
        "x=tf.keras.layers.Dense(num_classes,activation='softmax',name=\"output\")(x)"
      ],
      "metadata": {
        "id": "vpTIoQ678RJr"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=tf.keras.Model(i,x)"
      ],
      "metadata": {
        "id": "xspJBsxu89s6"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_epochs =4 \n",
        "\n",
        "METRICS = [\n",
        "      tf.keras.metrics.CategoricalAccuracy(name=\"accuracy\"),\n",
        "      balanced_recall,\n",
        "      balanced_precision,\n",
        "      balanced_f1_score\n",
        "]\n",
        "\n",
        "earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\", \n",
        "                                                      patience = 3,\n",
        "                                                      restore_best_weights = True)\n",
        "\n",
        "model.compile(optimizer = \"adam\",\n",
        "              loss = \"categorical_crossentropy\",\n",
        "              metrics = METRICS)"
      ],
      "metadata": {
        "id": "Rmi6FSrd9CNb"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1sUkkxc_dQA",
        "outputId": "6d7c5889-6eaa-4ef3-8ecb-8c9599e737fa"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " text (InputLayer)              [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " keras_layer_1 (KerasLayer)     {'input_word_ids':   0           ['text[0][0]']                   \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_type_ids':                                                \n",
            "                                (None, 128),                                                      \n",
            "                                 'input_mask': (Non                                               \n",
            "                                e, 128)}                                                          \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)       {'sequence_output':  109482241   ['keras_layer_1[0][0]',          \n",
            "                                 (None, 128, 768),                'keras_layer_1[0][1]',          \n",
            "                                 'default': (None,                'keras_layer_1[0][2]']          \n",
            "                                768),                                                             \n",
            "                                 'encoder_outputs':                                               \n",
            "                                 [(None, 128, 768),                                               \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768),                                                \n",
            "                                 (None, 128, 768)],                                               \n",
            "                                 'pooled_output': (                                               \n",
            "                                None, 768)}                                                       \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 768)          0           ['keras_layer[0][13]']           \n",
            "                                                                                                  \n",
            " output (Dense)                 (None, 3)            2307        ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 109,484,548\n",
            "Trainable params: 2,307\n",
            "Non-trainable params: 109,482,241\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit=model.fit(x_train,\n",
        "                    y_train,\n",
        "                    n_epochs,\n",
        "                    validation_data=(x_test,y_test),\n",
        "                    callbacks=[earlystop_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXEdBoDT_fYi",
        "outputId": "bfb54809-69ec-49a8-91d0-b862d639cda9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32594/32594 [==============================] - 1734s 53ms/step - loss: 1.0256 - accuracy: 0.5028 - balanced_recall: 0.2778 - balanced_precision: 0.2882 - balanced_f1_score: 0.2678 - val_loss: 0.9982 - val_accuracy: 0.5135 - val_balanced_recall: 0.3243 - val_balanced_precision: 0.2493 - val_balanced_f1_score: 0.2669\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WCknwh0Z_7eI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}