twitter_sentiment_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USYGUrTduDQn"
      },
      "source": [
        "# Nama: Felix Pratamasan\n",
        "# Email: felixpratama242@gmail.com"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Vp0tTzuzuLU_"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import zipfile\n",
        "import seaborn as sns\n",
        "from google.colab import files\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.callbacks import EarlyStopping\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3r6m03xv1k_"
      },
      "source": [
        "# Download File from Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "rwYIY0zDuR_S",
        "outputId": "d9d9a15e-e0f6-400a-f5ae-c1b0b20afb8d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2a9e816c-483f-4bf4-832f-abeeec33bae6\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2a9e816c-483f-4bf4-832f-abeeec33bae6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle (1).json\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"felixpratama\",\"key\":\"85471e6fceb4fb346ab6077119c87951\"}'}"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#you need to create your own API first in kaggle\n",
        "#upload kaggle json\n",
        "\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36_MAmLZvmM5",
        "outputId": "2c1d49ad-53f1-4dfc-af03-d4cfd88e8606"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ],
      "source": [
        "#create a kaggle folder\n",
        "!mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "lN8-JKyGvoon"
      },
      "outputs": [],
      "source": [
        "#copy kaggle.json to folder created\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "#permission for the json to act\n",
        "! chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9uODkqPuBdA",
        "outputId": "fffb38de-5eab-48fb-8a43-bfdd3e59534a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "twitter-entity-sentiment-analysis.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d jp797498e/twitter-entity-sentiment-analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8Ir9d0Pv7lp"
      },
      "source": [
        "#Read Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "etDxjVBxv52u"
      },
      "outputs": [],
      "source": [
        "test_local_zip = '/content/twitter-entity-sentiment-analysis.zip'\n",
        "zip_ref = zipfile.ZipFile(test_local_zip, 'r')\n",
        "zip_ref.extractall('/content/dataset')\n",
        "\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Y6wPKfYPwISg"
      },
      "outputs": [],
      "source": [
        "training_data = pd.read_csv('/content/dataset/twitter_training.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wZkGX7czwT7G",
        "outputId": "0049091f-8919-4ef1-93ad-75fb505d6f2f"
      },
      "outputs": [
        {
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              "    <tr>\n",
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              "      <td>Positive</td>\n",
              "      <td>im coming on borderlands and i will murder you...</td>\n",
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              "      <th>3</th>\n",
              "      <td>2401</td>\n",
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              "      <td>Positive</td>\n",
              "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
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              "   2401  Borderlands  Positive  \\\n",
              "0  2401  Borderlands  Positive   \n",
              "1  2401  Borderlands  Positive   \n",
              "2  2401  Borderlands  Positive   \n",
              "3  2401  Borderlands  Positive   \n",
              "4  2401  Borderlands  Positive   \n",
              "\n",
              "  im getting on borderlands and i will murder you all ,  \n",
              "0  I am coming to the borders and I will kill you...     \n",
              "1  im getting on borderlands and i will kill you ...     \n",
              "2  im coming on borderlands and i will murder you...     \n",
              "3  im getting on borderlands 2 and i will murder ...     \n",
              "4  im getting into borderlands and i can murder y...     "
            ]
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      "source": [
        "training_data.head()"
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      "execution_count": 9,
      "metadata": {
        "id": "kvcbODhIwykP"
      },
      "outputs": [],
      "source": [
        "# add columns name\n",
        "\n",
        "columns = ['tweet_id', 'entity', 'sentiment', 'tweet_content']\n",
        "training_data.columns = columns"
      ]
    },
    {
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      "execution_count": 10,
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        "outputId": "12999aae-70a3-4fb2-ba23-19c8030c7b4c"
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              "      <th>tweet_id</th>\n",
              "      <th>entity</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>tweet_content</th>\n",
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              "      <td>Positive</td>\n",
              "      <td>im getting on borderlands and i will kill you ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>im coming on borderlands and i will murder you...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2401</td>\n",
              "      <td>Borderlands</td>\n",
              "      <td>Positive</td>\n",
              "      <td>im getting into borderlands and i can murder y...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bb8ae1e-c1dc-4f87-989f-7f2eda303248')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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-0bb8ae1e-c1dc-4f87-989f-7f2eda303248 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-0bb8ae1e-c1dc-4f87-989f-7f2eda303248');\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",
              "  "
            ],
            "text/plain": [
              "   tweet_id       entity sentiment  \\\n",
              "0      2401  Borderlands  Positive   \n",
              "1      2401  Borderlands  Positive   \n",
              "2      2401  Borderlands  Positive   \n",
              "3      2401  Borderlands  Positive   \n",
              "4      2401  Borderlands  Positive   \n",
              "\n",
              "                                       tweet_content  \n",
              "0  I am coming to the borders and I will kill you...  \n",
              "1  im getting on borderlands and i will kill you ...  \n",
              "2  im coming on borderlands and i will murder you...  \n",
              "3  im getting on borderlands 2 and i will murder ...  \n",
              "4  im getting into borderlands and i can murder y...  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffVkY0j_41Ky",
        "outputId": "cd54bba2-18f4-46ca-b253-037b386a49a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(74681, 4)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#print shape of training data\n",
        "training_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hy6g1GJA0PnV",
        "outputId": "dfa4b2ef-02b4-425e-e9aa-bdc16f023e97"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 74681 entries, 0 to 74680\n",
            "Data columns (total 4 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   tweet_id       74681 non-null  int64 \n",
            " 1   entity         74681 non-null  object\n",
            " 2   sentiment      74681 non-null  object\n",
            " 3   tweet_content  73995 non-null  object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ],
      "source": [
        "training_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRWJ-tW24uWI",
        "outputId": "635b664c-053c-4114-f18e-91fbaed4e6b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tweet_id           0\n",
              "entity             0\n",
              "sentiment          0\n",
              "tweet_content    686\n",
              "dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#check missing value\n",
        "training_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "HFc0T4zH4yh7"
      },
      "outputs": [],
      "source": [
        "#remove missing values\n",
        "training_data = training_data.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LKmFu-85FIl",
        "outputId": "118ba85e-4d0f-4037-d935-9e2d90a537db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tweet_id         0\n",
              "entity           0\n",
              "sentiment        0\n",
              "tweet_content    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#check missing values after remove them\n",
        "training_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lt43Mmy57Sz_"
      },
      "source": [
        "# Exploratory Data Analysist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "sLHD3rYv0kAp"
      },
      "outputs": [],
      "source": [
        "#dropping 'tweet_id', 'entity'\n",
        "training_data = training_data.drop(['tweet_id', 'entity'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tgc7DNW5hIM",
        "outputId": "da798a41-41f2-455c-ac54-ef8524bc209d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape after remove missing values and drop columns: (73995, 2)\n"
          ]
        }
      ],
      "source": [
        "print('shape after remove missing values and drop columns: {}'.format(training_data.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "YySRhmwr1QEl"
      },
      "outputs": [],
      "source": [
        "sentiments = training_data['sentiment']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "t73dko3O2YA_",
        "outputId": "e4da5895-1b2c-4764-b134-17790e3e7578"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Negative      22358\n",
            "Positive      20654\n",
            "Neutral       18108\n",
            "Irrelevant    12875\n",
            "Name: sentiment, dtype: int64\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot label\n",
        "x_axis = sentiments.value_counts().index\n",
        "y_axis = sentiments.value_counts()\n",
        "\n",
        "print(y_axis)\n",
        "\n",
        "plt.title('Label')\n",
        "sns.barplot(x = x_axis, y = y_axis,  data=training_data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHogm6xS0dXH"
      },
      "source": [
        "# Data Preprocessing\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9pqAZF723Tfy",
        "outputId": "a58f2aa8-2375-485f-d471-a402e9d17794"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-51447d75-d082-45ed-9941-80738fea3533\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet_content</th>\n",
              "      <th>Irrelevant</th>\n",
              "      <th>Negative</th>\n",
              "      <th>Neutral</th>\n",
              "      <th>Positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I am coming to the borders and I will kill you...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>im getting on borderlands and i will kill you ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>im coming on borderlands and i will murder you...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>im getting on borderlands 2 and i will murder ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>im getting into borderlands and i can murder y...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74676</th>\n",
              "      <td>Just realized that the Windows partition of my...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74677</th>\n",
              "      <td>Just realized that my Mac window partition is ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74678</th>\n",
              "      <td>Just realized the windows partition of my Mac ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74679</th>\n",
              "      <td>Just realized between the windows partition of...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74680</th>\n",
              "      <td>Just like the windows partition of my Mac is l...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>73995 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51447d75-d082-45ed-9941-80738fea3533')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-51447d75-d082-45ed-9941-80738fea3533 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-51447d75-d082-45ed-9941-80738fea3533');\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",
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              "  "
            ],
            "text/plain": [
              "                                           tweet_content  Irrelevant  \\\n",
              "0      I am coming to the borders and I will kill you...           0   \n",
              "1      im getting on borderlands and i will kill you ...           0   \n",
              "2      im coming on borderlands and i will murder you...           0   \n",
              "3      im getting on borderlands 2 and i will murder ...           0   \n",
              "4      im getting into borderlands and i can murder y...           0   \n",
              "...                                                  ...         ...   \n",
              "74676  Just realized that the Windows partition of my...           0   \n",
              "74677  Just realized that my Mac window partition is ...           0   \n",
              "74678  Just realized the windows partition of my Mac ...           0   \n",
              "74679  Just realized between the windows partition of...           0   \n",
              "74680  Just like the windows partition of my Mac is l...           0   \n",
              "\n",
              "       Negative  Neutral  Positive  \n",
              "0             0        0         1  \n",
              "1             0        0         1  \n",
              "2             0        0         1  \n",
              "3             0        0         1  \n",
              "4             0        0         1  \n",
              "...         ...      ...       ...  \n",
              "74676         0        0         1  \n",
              "74677         0        0         1  \n",
              "74678         0        0         1  \n",
              "74679         0        0         1  \n",
              "74680         0        0         1  \n",
              "\n",
              "[73995 rows x 5 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "onehot_sentiments = pd.get_dummies(training_data['sentiment'])\n",
        "training_data_clean = pd.concat([training_data, onehot_sentiments], axis=1)\n",
        "training_data_clean = training_data_clean.drop(columns='sentiment')\n",
        "training_data_clean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "5cQUaAm64Wo1"
      },
      "outputs": [],
      "source": [
        "# change column into numpy array\n",
        "tweet = training_data_clean['tweet_content'].values\n",
        "label = training_data_clean[['Irrelevant','Negative', 'Neutral', 'Positive']].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pT5exanF6voD",
        "outputId": "4b565f75-1d54-4bea-bc37-819fa1b17a6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "twwet type: <class 'numpy.ndarray'>\n",
            "['I am coming to the borders and I will kill you all,'\n",
            " 'im getting on borderlands and i will kill you all,'\n",
            " 'im coming on borderlands and i will murder you all,' ...\n",
            " 'Just realized the windows partition of my Mac is now 6 years behind on Nvidia drivers and I have no idea how he didn’t notice'\n",
            " 'Just realized between the windows partition of my Mac is like being 6 years behind on Nvidia drivers and cars I have no fucking idea how I ever didn ’ t notice'\n",
            " 'Just like the windows partition of my Mac is like 6 years behind on its drivers So you have no idea how I didn’t notice']\n",
            "\n",
            "label type: <class 'numpy.ndarray'>\n",
            "[[0 0 0 1]\n",
            " [0 0 0 1]\n",
            " [0 0 0 1]\n",
            " ...\n",
            " [0 0 0 1]\n",
            " [0 0 0 1]\n",
            " [0 0 0 1]]\n"
          ]
        }
      ],
      "source": [
        "print('twwet type: {}\\n{}\\n'.format(type(tweet),tweet))\n",
        "print('label type: {}\\n{}'.format(type(label),label))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "vEGw9Mdk8QLf"
      },
      "outputs": [],
      "source": [
        "#split data\n",
        "train_tweet, test_tweet, train_label, test_label = train_test_split(tweet, label, test_size = 0.2, random_state = 42) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7SL1bJH9Hc2",
        "outputId": "b1365ce3-fba8-425a-b830-98537fd9090d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape train_tweet: (59196,)\n",
            "shape test_tweet: (14799,)\n",
            "shape label_train: (59196, 4)\n",
            "shape label_test: (14799, 4)\n"
          ]
        }
      ],
      "source": [
        "print('shape train_tweet: {}'.format(train_tweet.shape))\n",
        "print('shape test_tweet: {}'.format(test_tweet.shape))\n",
        "print('shape label_train: {}'.format(train_label.shape))\n",
        "print('shape label_test: {}'.format(test_label.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "f75NK5ZH-Hg0"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer(num_words=10000, oov_token = '<oov>')\n",
        "label_tokenizer = Tokenizer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "LA-l15Ex-SE5"
      },
      "outputs": [],
      "source": [
        "tokenizer.fit_on_texts(train_tweet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hr0cicFl-SHQ",
        "outputId": "426475bf-1d63-4d2d-8797-10b66aed9ef3"
      },
      "outputs": [],
      "source": [
        "#check word index in tokenizer\n",
        "# print(tokenizer.word_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "cygITXnJ-SJa"
      },
      "outputs": [],
      "source": [
        "train_sequences = tokenizer.texts_to_sequences(train_tweet)\n",
        "test_sequences = tokenizer.texts_to_sequences(test_tweet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "IoMcd2y--SMD"
      },
      "outputs": [],
      "source": [
        "train_pad = pad_sequences(train_sequences,\n",
        "                          maxlen = 150,\n",
        "                          padding = 'post',\n",
        "                          truncating = 'post')\n",
        "\n",
        "test_pad = pad_sequences(test_sequences,\n",
        "                          maxlen = 150,\n",
        "                          padding = 'post',\n",
        "                          truncating = 'post')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Byv5AtGYAK1i"
      },
      "source": [
        "# Build Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "16KV5agsB-UE"
      },
      "outputs": [],
      "source": [
        "# class myCallback(tf.keras.callbacks.Callback):\n",
        "#   def on_epoch_end(self, epoch, logs={}):\n",
        "#     if(logs.get('accuracy')>=0.92 and logs.get('val_accuracy')>=0.92):\n",
        "#       print(\"\\nAkurasi telah mencapai >= 92%!\")\n",
        "#       self.model.stop_training = True\n",
        "# callbacks = myCallback()\n",
        "\n",
        "callbacks = EarlyStopping(monitor = 'val_loss',\n",
        "                        patience = 3,\n",
        "                        verbose = 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "PUIW87XKAKYl"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(input_dim = 10000, output_dim=16, input_length = 150),\n",
        "    # tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences = True)),\n",
        "    # tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    # tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    # tf.keras.layers.LSTM(16),\n",
        "    # tf.keras.layers.LSTM(32, return_sequences=True),\n",
        "    #tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    #tf.keras.layers.Dropout(0.5),\n",
        "    # tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(4, activation='softmax')\n",
        "\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PjuCb94wDmtH",
        "outputId": "c7cd55ad-9da2-42b2-d714-c1d6c9318ee8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 150, 16)           160000    \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 16)               0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 68        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 160,068\n",
            "Trainable params: 160,068\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "3mQG2n5TBPtL"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics='accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWRw6BGfB_GZ",
        "outputId": "0fd078c2-7389-4879-b75a-bec534bfebc9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "462/462 [==============================] - 38s 75ms/step - loss: 1.3578 - accuracy: 0.3168 - val_loss: 1.3444 - val_accuracy: 0.3431\n",
            "Epoch 2/50\n",
            "462/462 [==============================] - 9s 19ms/step - loss: 1.3171 - accuracy: 0.4346 - val_loss: 1.2922 - val_accuracy: 0.4834\n",
            "Epoch 3/50\n",
            "462/462 [==============================] - 5s 10ms/step - loss: 1.2500 - accuracy: 0.5134 - val_loss: 1.2252 - val_accuracy: 0.5192\n",
            "Epoch 4/50\n",
            "462/462 [==============================] - 5s 10ms/step - loss: 1.1779 - accuracy: 0.5499 - val_loss: 1.1610 - val_accuracy: 0.5487\n",
            "Epoch 5/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 1.1115 - accuracy: 0.5734 - val_loss: 1.1062 - val_accuracy: 0.5700\n",
            "Epoch 6/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 1.0549 - accuracy: 0.5958 - val_loss: 1.0587 - val_accuracy: 0.5925\n",
            "Epoch 7/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 1.0055 - accuracy: 0.6185 - val_loss: 1.0185 - val_accuracy: 0.6034\n",
            "Epoch 8/50\n",
            "462/462 [==============================] - 4s 9ms/step - loss: 0.9631 - accuracy: 0.6371 - val_loss: 0.9836 - val_accuracy: 0.6192\n",
            "Epoch 9/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 0.9243 - accuracy: 0.6531 - val_loss: 0.9523 - val_accuracy: 0.6313\n",
            "Epoch 10/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 0.8892 - accuracy: 0.6675 - val_loss: 0.9286 - val_accuracy: 0.6392\n",
            "Epoch 11/50\n",
            "462/462 [==============================] - 4s 7ms/step - loss: 0.8562 - accuracy: 0.6814 - val_loss: 0.8981 - val_accuracy: 0.6534\n",
            "Epoch 12/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 0.8270 - accuracy: 0.6940 - val_loss: 0.8761 - val_accuracy: 0.6663\n",
            "Epoch 13/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.7983 - accuracy: 0.7046 - val_loss: 0.8530 - val_accuracy: 0.6730\n",
            "Epoch 14/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.7737 - accuracy: 0.7146 - val_loss: 0.8323 - val_accuracy: 0.6813\n",
            "Epoch 15/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.7486 - accuracy: 0.7257 - val_loss: 0.8149 - val_accuracy: 0.6842\n",
            "Epoch 16/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 0.7265 - accuracy: 0.7350 - val_loss: 0.7962 - val_accuracy: 0.6950\n",
            "Epoch 17/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.7038 - accuracy: 0.7452 - val_loss: 0.7801 - val_accuracy: 0.7016\n",
            "Epoch 18/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.6852 - accuracy: 0.7520 - val_loss: 0.7670 - val_accuracy: 0.7007\n",
            "Epoch 19/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.6660 - accuracy: 0.7587 - val_loss: 0.7509 - val_accuracy: 0.7142\n",
            "Epoch 20/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.6477 - accuracy: 0.7681 - val_loss: 0.7366 - val_accuracy: 0.7191\n",
            "Epoch 21/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.6313 - accuracy: 0.7730 - val_loss: 0.7254 - val_accuracy: 0.7220\n",
            "Epoch 22/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.6172 - accuracy: 0.7782 - val_loss: 0.7143 - val_accuracy: 0.7251\n",
            "Epoch 23/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.6002 - accuracy: 0.7853 - val_loss: 0.7019 - val_accuracy: 0.7326\n",
            "Epoch 24/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.5879 - accuracy: 0.7892 - val_loss: 0.6914 - val_accuracy: 0.7373\n",
            "Epoch 25/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.5739 - accuracy: 0.7938 - val_loss: 0.6841 - val_accuracy: 0.7419\n",
            "Epoch 26/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.5624 - accuracy: 0.7992 - val_loss: 0.6733 - val_accuracy: 0.7443\n",
            "Epoch 27/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.5494 - accuracy: 0.8043 - val_loss: 0.6644 - val_accuracy: 0.7481\n",
            "Epoch 28/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.5372 - accuracy: 0.8099 - val_loss: 0.6556 - val_accuracy: 0.7529\n",
            "Epoch 29/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.5293 - accuracy: 0.8111 - val_loss: 0.6488 - val_accuracy: 0.7543\n",
            "Epoch 30/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.5172 - accuracy: 0.8153 - val_loss: 0.6417 - val_accuracy: 0.7563\n",
            "Epoch 31/50\n",
            "462/462 [==============================] - 3s 5ms/step - loss: 0.5079 - accuracy: 0.8190 - val_loss: 0.6375 - val_accuracy: 0.7603\n",
            "Epoch 32/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.5004 - accuracy: 0.8212 - val_loss: 0.6284 - val_accuracy: 0.7628\n",
            "Epoch 33/50\n",
            "462/462 [==============================] - 2s 4ms/step - loss: 0.4888 - accuracy: 0.8260 - val_loss: 0.6220 - val_accuracy: 0.7651\n",
            "Epoch 34/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4818 - accuracy: 0.8289 - val_loss: 0.6180 - val_accuracy: 0.7662\n",
            "Epoch 35/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4753 - accuracy: 0.8303 - val_loss: 0.6118 - val_accuracy: 0.7711\n",
            "Epoch 36/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 0.4654 - accuracy: 0.8349 - val_loss: 0.6085 - val_accuracy: 0.7697\n",
            "Epoch 37/50\n",
            "462/462 [==============================] - 3s 5ms/step - loss: 0.4581 - accuracy: 0.8372 - val_loss: 0.6020 - val_accuracy: 0.7717\n",
            "Epoch 38/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4527 - accuracy: 0.8388 - val_loss: 0.6022 - val_accuracy: 0.7745\n",
            "Epoch 39/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4458 - accuracy: 0.8414 - val_loss: 0.5945 - val_accuracy: 0.7763\n",
            "Epoch 40/50\n",
            "462/462 [==============================] - 3s 7ms/step - loss: 0.4409 - accuracy: 0.8424 - val_loss: 0.5893 - val_accuracy: 0.7788\n",
            "Epoch 41/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.4324 - accuracy: 0.8460 - val_loss: 0.5867 - val_accuracy: 0.7793\n",
            "Epoch 42/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4274 - accuracy: 0.8478 - val_loss: 0.5833 - val_accuracy: 0.7805\n",
            "Epoch 43/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4209 - accuracy: 0.8495 - val_loss: 0.5796 - val_accuracy: 0.7825\n",
            "Epoch 44/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4176 - accuracy: 0.8514 - val_loss: 0.5773 - val_accuracy: 0.7838\n",
            "Epoch 45/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.4116 - accuracy: 0.8541 - val_loss: 0.5757 - val_accuracy: 0.7852\n",
            "Epoch 46/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.4060 - accuracy: 0.8555 - val_loss: 0.5724 - val_accuracy: 0.7846\n",
            "Epoch 47/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.4005 - accuracy: 0.8578 - val_loss: 0.5728 - val_accuracy: 0.7838\n",
            "Epoch 48/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.3979 - accuracy: 0.8581 - val_loss: 0.5676 - val_accuracy: 0.7899\n",
            "Epoch 49/50\n",
            "462/462 [==============================] - 2s 5ms/step - loss: 0.3939 - accuracy: 0.8590 - val_loss: 0.5670 - val_accuracy: 0.7883\n",
            "Epoch 50/50\n",
            "462/462 [==============================] - 3s 6ms/step - loss: 0.3872 - accuracy: 0.8625 - val_loss: 0.5676 - val_accuracy: 0.7880\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_pad, train_label,\n",
        "                    batch_size = 128,\n",
        "                    steps_per_epoch = len(train_tweet) // 128,\n",
        "                    epochs = 50,\n",
        "                    validation_data = (test_pad, test_label),\n",
        "                    validation_steps = len(test_tweet) // 128,\n",
        "                    verbose = 1,\n",
        "                    callbacks=[callbacks])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "W0d15Bo7moyB",
        "outputId": "fb36269e-8f14-4482-c998-2fc72c83484d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the training and validation accuracies for each epoch\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "fig = plt.figure(figsize = (15,5))\n",
        "ax1 = fig.add_subplot(1,2,1)\n",
        "ax2 = fig.add_subplot(1,2,2)\n",
        "\n",
        "\n",
        "ax1.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "ax1.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "ax1.set_title('Training and validation accuracy')\n",
        "ax1.legend(loc=0)\n",
        "\n",
        "\n",
        "ax2.plot(epochs, loss, 'r', label='Training loss')\n",
        "ax2.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "ax2.set_title('Training and validation loss')\n",
        "ax2.legend(loc=0)\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "wbxi1GFpYMqC"
      },
      "outputs": [],
      "source": [
        "model.save('sentiment_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "# saving\n",
        "with open('tokenizer.pickle', 'wb') as handle:\n",
        "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "cb575880da7fb1b63f39f76a29ec1124782f92e8ba6b24ca6b4135e5a600188f"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}