Text Classification - Deep Learning.ipynb

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      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "nFn_Xncf_40I"
      },
      "outputs": [],
      "source": [
        "import gensim\n",
        "import pandas as pd\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Embedding, Dense, Flatten\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.preprocessing import scale\n",
        "import numpy as np\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from gensim.models import KeyedVectors\n",
        "import seaborn as sn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MoXU8dzyIJS"
      },
      "source": [
        "# Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-gc33WxgxF3-",
        "outputId": "a09bf4c5-cbf9-439b-81ea-8734026ef23b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                             text_a label\n",
              "0           0  betewe buka twitter cuman ngetweet liat home b...    no\n",
              "1           1  mas piyuuu mugo2 corona tuh mulut tersumpal ma...    no\n",
              "2           2  e100ss gini buka informasi sejelas nya identit...   yes\n",
              "3           3  neng solo wes ono terduga corona cobo neng ati...    no\n",
              "4           4  midiahn nii akun gak takut takut nya isu coron...    no"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>text_a</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>betewe buka twitter cuman ngetweet liat home b...</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>mas piyuuu mugo2 corona tuh mulut tersumpal ma...</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>e100ss gini buka informasi sejelas nya identit...</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>neng solo wes ono terduga corona cobo neng ati...</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>midiahn nii akun gak takut takut nya isu coron...</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae3144fe-8735-49ac-8e3b-b4623306d763')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\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",
              "      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",
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              "\n",
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              "          document.querySelector('#df-ae3144fe-8735-49ac-8e3b-b4623306d763 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df = pd.read_csv(\"train.csv\")\n",
        "df_test = pd.read_csv(\"test.csv\")\n",
        "df_dev = pd.read_csv(\"dev.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJbnMVj-x7mW",
        "outputId": "0321f4ed-ce27-4563-cb1c-8973e670c64d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data shape: (21601, 3)\n",
            "Dev data shape: (2800, 2)\n",
            "Test data shape: (2800, 2)\n"
          ]
        }
      ],
      "source": [
        "print(\"Train data shape:\", df.shape)\n",
        "print(\"Dev data shape:\", df_dev.shape)\n",
        "print(\"Test data shape:\", df_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  text_a\n",
              "0      [betewe, buka, twitter, cuman, ngetweet, liat,...\n",
              "1      [mas, piyuuu, mugo, corona, tuh, mulut, tersum...\n",
              "2      [ss, gini, buka, informasi, sejelas, nya, iden...\n",
              "3      [neng, solo, wes, ono, terduga, corona, cobo, ...\n",
              "4      [midiahn, nii, akun, gak, takut, takut, nya, i...\n",
              "...                                                  ...\n",
              "21596  [depok, panas, ga, karuan, kereta, sampe, pasm...\n",
              "21597  [oxfara, arie, kriting, yg, lebi, goblo, nya, ...\n",
              "21598  [virus, corona, menyaba, depok, cuci, tangan, ...\n",
              "21599  [mata, sipit, tinggal, depok, udah, abis, dah,...\n",
              "21600  [ak, batuk, pilek, pusing, demam, anjir, ak, d...\n",
              "\n",
              "[21601 rows x 1 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text_a</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[betewe, buka, twitter, cuman, ngetweet, liat,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[mas, piyuuu, mugo, corona, tuh, mulut, tersum...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[ss, gini, buka, informasi, sejelas, nya, iden...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[neng, solo, wes, ono, terduga, corona, cobo, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[midiahn, nii, akun, gak, takut, takut, nya, i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21596</th>\n",
              "      <td>[depok, panas, ga, karuan, kereta, sampe, pasm...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21597</th>\n",
              "      <td>[oxfara, arie, kriting, yg, lebi, goblo, nya, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21598</th>\n",
              "      <td>[virus, corona, menyaba, depok, cuci, tangan, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21599</th>\n",
              "      <td>[mata, sipit, tinggal, depok, udah, abis, dah,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21600</th>\n",
              "      <td>[ak, batuk, pilek, pusing, demam, anjir, ak, d...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>21601 rows × 1 columns</p>\n",
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              "      \n",
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              "      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",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-2a081cd8-3e90-4b47-9db3-904725b2c770 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-2a081cd8-3e90-4b47-9db3-904725b2c770');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "text_a = df.text_a.apply(gensim.utils.simple_preprocess)\n",
        "pd.DataFrame(text_a)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_text = len(text_a)\n",
        "print(\"Number of texts:\", n_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fe1SMdzA6rM_",
        "outputId": "c2d75447-59af-42fc-8e83-b64082ec5d61"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of texts: 21601\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMiG1V_oyK7b"
      },
      "source": [
        "# Training Word2Vec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "mGH6i9fQyN_r"
      },
      "outputs": [],
      "source": [
        "w2v_model = gensim.models.Word2Vec(\n",
        "    window=20,\n",
        "    min_count=10,\n",
        "    workers=4,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4dhc03GayR88"
      },
      "outputs": [],
      "source": [
        "w2v_model.build_vocab(text_a, progress_per=1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NzoG3vcyTaX",
        "outputId": "33b24b34-dfc9-4dff-af89-5a573a8b0c9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:gensim.models.base_any2vec:Effective 'alpha' higher than previous training cycles\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1017989, 1605060)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "w2v_model.train(text_a, total_examples=w2v_model.corpus_count, epochs=w2v_model.epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "KD_rupZeydmc"
      },
      "outputs": [],
      "source": [
        "w2v_model.save(\"./train.model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHsVdPpKyxCL",
        "outputId": "4b9d90af-7d92-46c8-802c-901febc9d4f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vector Length: 100\n"
          ]
        }
      ],
      "source": [
        "vec_length = 100\n",
        "print(\"Vector Length:\", vec_length)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_words = len(w2v_model.wv.vocab)\n",
        "print(\"Number of words:\", n_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vY_EGkdw4qz6",
        "outputId": "3b082d39-181c-495a-9c36-e3c8762c99c6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words: 4147\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Oversampling Data"
      ],
      "metadata": {
        "id": "cC7gs-Ha8ssc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of label yes:', len(df[df[\"label\"]=='yes']))\n",
        "print('Number of label no:', len(df[df[\"label\"]=='no']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKh7hUIhYUUK",
        "outputId": "a9363d6d-3dfe-412b-bd18-205460f3062e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of label yes: 6089\n",
            "Number of label no: 15512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ros = RandomOverSampler(random_state=0)\n",
        "X_resampled, y_resampled = ros.fit_resample(df[[\"text_a\"]], df[[\"label\"]])"
      ],
      "metadata": {
        "id": "KFjbV245Zxv2"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of label yes:', len(y_resampled[y_resampled[\"label\"]=='yes']))\n",
        "print('Number of label no:', len(y_resampled[y_resampled[\"label\"]=='no']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "91SKrYvpaFKy",
        "outputId": "41df1f3d-dc8f-4790-e099-85d37be10b9e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of label yes: 15512\n",
            "Number of label no: 15512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare training and testing data\n"
      ],
      "metadata": {
        "id": "p4Pyb0qT5eNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get max length of a sentence\n",
        "max_length = max(len(x) for x in text_a)\n",
        "max_length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sk9SZHAa58PU",
        "outputId": "4e6e810e-af34-4f54-a87e-0deed517a0c9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1065"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = np.array([0 if y_resampled.label[i] == 'no' else 1 for i in range(len(y_resampled))])\n",
        "y_dev = np.array([0 if df_dev.label[i] == 'no' else 1 for i in range(len(df_dev))])\n",
        "y_test = np.array([0 if df_test.label[i] == 'no' else 1 for i in range(len(df_test))])"
      ],
      "metadata": {
        "id": "rTLRIeJJHfzy"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_words = X_resampled.text_a.apply(gensim.utils.simple_preprocess)\n",
        "X_test_words = df_test.text_a.apply(gensim.utils.simple_preprocess)\n",
        "X_dev_words = df_dev.text_a.apply(gensim.utils.simple_preprocess)"
      ],
      "metadata": {
        "id": "VPxsm_ka--KS"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words=n_words)\n",
        "tokenizer.fit_on_texts(X_train_words.tolist())\n",
        "training_sequences = tokenizer.texts_to_sequences(X_train_words.tolist())\n",
        "train_data = pad_sequences(training_sequences, maxlen=max_length)"
      ],
      "metadata": {
        "id": "AInXKi_775t9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.fit_on_texts(X_test_words.tolist())\n",
        "testing_sequences = tokenizer.texts_to_sequences(X_test_words.tolist())\n",
        "test_data = pad_sequences(testing_sequences, maxlen=max_length)"
      ],
      "metadata": {
        "id": "xg2fDKgj_aYb"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.fit_on_texts(X_dev_words.tolist())\n",
        "dev_sequences = tokenizer.texts_to_sequences(X_dev_words.tolist())\n",
        "dev_data = pad_sequences(dev_sequences, maxlen=max_length)"
      ],
      "metadata": {
        "id": "u82-NxsxKrCs"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_data), len(y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhTa5UoMLOPa",
        "outputId": "e5f3fec6-72eb-4eea-adc0-4d0a68b9da6d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31024 31024\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Embedding Data"
      ],
      "metadata": {
        "id": "VHEetVTpAAzF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_index = tokenizer.word_index\n",
        "nb_words = len(word_index)\n",
        "\n",
        "embedding_matrix = np.zeros((nb_words, vec_length))\n",
        "for word, i in word_index.items():\n",
        "    if word in w2v_model.wv.vocab:\n",
        "        embedding_matrix[i] = w2v_model.wv.word_vec(word)"
      ],
      "metadata": {
        "id": "-sM_gRRtIb2p"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building CNN"
      ],
      "metadata": {
        "id": "mzOql3MT_piX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTHWWm6FXRNW",
        "outputId": "cd5afb69-518b-49f1-9ab3-0571dcbf2ce9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 1065, 100)         4783700   \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 106500)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                6816064   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11,602,389\n",
            "Trainable params: 6,818,689\n",
            "Non-trainable params: 4,783,700\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(nb_words,\n",
        "        vec_length,\n",
        "        weights=[embedding_matrix],\n",
        "        input_length=max_length,\n",
        "        trainable=False))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, 'relu'))\n",
        "model.add(Dense(32, 'relu'))\n",
        "model.add(Dense(16, 'relu'))\n",
        "model.add(Dense(1, 'sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "        optimizer='nadam',\n",
        "        metrics=['acc'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRrjBkD1podE",
        "outputId": "b48ffa22-5251-4bdd-84c7-e85b94357828"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "3103/3103 - 167s - loss: 0.5401 - acc: 0.7273 - val_loss: 0.7105 - val_acc: 0.6307 - 167s/epoch - 54ms/step\n",
            "Epoch 2/10\n",
            "3103/3103 - 160s - loss: 0.3825 - acc: 0.8338 - val_loss: 0.7776 - val_acc: 0.6482 - 160s/epoch - 52ms/step\n",
            "Epoch 3/10\n",
            "3103/3103 - 165s - loss: 0.2889 - acc: 0.8829 - val_loss: 1.0919 - val_acc: 0.5796 - 165s/epoch - 53ms/step\n",
            "Epoch 4/10\n",
            "3103/3103 - 165s - loss: 0.2324 - acc: 0.9112 - val_loss: 1.1305 - val_acc: 0.6111 - 165s/epoch - 53ms/step\n",
            "Epoch 5/10\n",
            "3103/3103 - 163s - loss: 0.1940 - acc: 0.9271 - val_loss: 1.2669 - val_acc: 0.6193 - 163s/epoch - 53ms/step\n",
            "Epoch 6/10\n",
            "3103/3103 - 166s - loss: 0.1669 - acc: 0.9387 - val_loss: 1.4342 - val_acc: 0.5979 - 166s/epoch - 54ms/step\n",
            "Epoch 7/10\n",
            "3103/3103 - 163s - loss: 0.1516 - acc: 0.9439 - val_loss: 1.6788 - val_acc: 0.5782 - 163s/epoch - 52ms/step\n",
            "Epoch 8/10\n",
            "3103/3103 - 166s - loss: 0.1357 - acc: 0.9514 - val_loss: 1.6480 - val_acc: 0.6025 - 166s/epoch - 54ms/step\n",
            "Epoch 9/10\n",
            "3103/3103 - 162s - loss: 0.1212 - acc: 0.9571 - val_loss: 1.6101 - val_acc: 0.6204 - 162s/epoch - 52ms/step\n",
            "Epoch 10/10\n",
            "3103/3103 - 167s - loss: 0.1139 - acc: 0.9602 - val_loss: 1.7726 - val_acc: 0.6111 - 167s/epoch - 54ms/step\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(x=train_data, y=y_train, batch_size=10, epochs=10, shuffle=True, verbose=2, validation_data=(dev_data,y_dev))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(train_data, y_train, verbose=False)\n",
        "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
        "loss, accuracy = model.evaluate(dev_data, y_dev, verbose=False)\n",
        "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JP-QTd4uTr4A",
        "outputId": "2ac6db8c-3b45-4519-ad54-d148c641b7eb"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.9683\n",
            "Testing Accuracy:  0.6111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bDhMpDw-w4cs",
        "outputId": "2762ea80-9c1e-4551-e757-5c5818da07bd"
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-b0a201a2-2c94-41a5-95be-a68b1ebf6f11 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b0a201a2-2c94-41a5-95be-a68b1ebf6f11');\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",
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          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "pred = model.predict(test_data)\n",
        "\n",
        "for i in range(len(pred)):\n",
        "  pred[i] = round(pred[i][0])"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FepA39gTSg_9",
        "outputId": "cb853e1f-a56b-4b9f-b56b-e6b95f1f146a"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1534,  559],\n",
              "       [ 399,  308]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'dev'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'dev'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "BQowxVlPT9o2",
        "outputId": "810b26c6-4273-455e-bb70-9bcabc72aad0"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
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            "needs_background": "light"
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    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(confusion_matrix(y_test, pred))"
      ],
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          "height": 112
        },
        "id": "ke523t_YUpqy",
        "outputId": "85928812-92ee-457b-864c-a1792a4956ea"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGisTYntV-fW",
        "outputId": "7b49854f-234c-4508-e250-4b6083621cec"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.73      0.76      2093\n",
            "           1       0.36      0.44      0.39       707\n",
            "\n",
            "    accuracy                           0.66      2800\n",
            "   macro avg       0.57      0.58      0.58      2800\n",
            "weighted avg       0.68      0.66      0.67      2800\n",
            "\n"
          ]
        }
      ]
    }
  ],
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}