Sentiment_Analysis_Model.ipynb

{
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      "cell_type": "code",
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        "id": "qEOQ9Y_1zTSQ",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
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        }
      ],
      "source": [
        "!pip install -q datasets\n",
        "!pip install -q tensorflow_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsHK9LawGHmH"
      },
      "source": [
        "#Import data from google drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rZ4gh2JCJfR6"
      },
      "outputs": [],
      "source": [
        "# Import PyDrive and associated libraries.\n",
        "# This only needs to be done once per notebook.\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once per notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "file_ids = [\"1yWbvg1gVbSYvlFXahJkeBjHtfNAfwfJG\", \n",
        "            \"1mxU7-H1ROaobkQ8cWqWBSewldisriUfk\",\n",
        "            \"1UjTKjVR4WcsPJzVdNYsdzDGl5wpDoPVE\"] # We will get these ids from Balancing_Dataset.ipynb\n",
        "\n",
        "\n",
        "for id in range(len(file_ids)):\n",
        "  downloaded = drive.CreateFile({'id': file_ids[id]})\n",
        "  if id == 0:\n",
        "    downloaded.GetContentFile('/content/train_balanced.csv')  \n",
        "  if id == 1:\n",
        "    downloaded.GetContentFile('/content/val_balanced.csv')  \n",
        "  if id == 2:\n",
        "    downloaded.GetContentFile('/content/test_balanced.csv')  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7RrKbECLZ76"
      },
      "source": [
        "# See Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jd0ug1WWLer6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "#Display all column\n",
        "pd.set_option('display.max_columns', None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhXZ-BLqG03H",
        "outputId": "f651e5f6-8a7c-427d-aec8-44c09646d8f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    6416\n",
              "1    5410\n",
              "2    6416\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data = pd.read_csv('train_balanced.csv', sep=',')\n",
        "df_train = pd.DataFrame(data)\n",
        "df_train.groupby('label').size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpSM6XfFLoHX",
        "outputId": "0024a3ec-9eda-46f6-e340-b15f3dc8bff8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    735\n",
              "1    735\n",
              "2    735\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data = pd.read_csv('val_balanced.csv', sep=',')\n",
        "df_val = pd.DataFrame(data)\n",
        "df_val.groupby('label').size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QYC83rSTSkc",
        "outputId": "7a726724-c854-49ae-ded1-543059f689f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label\n",
              "0    208\n",
              "1     88\n",
              "2    204\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data = pd.read_csv('test_balanced.csv', sep=',')\n",
        "df_test = pd.DataFrame(data)\n",
        "df_test.groupby('label').size()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZtTz2fYW-cQ"
      },
      "source": [
        "### Merge Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDaidpg9VO2H",
        "outputId": "8cdbef71-6a14-4748-c46d-4390b70b6834"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20947, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#Merge the training, validation, and test data\n",
        "merged = pd.concat([df_train, df_val, df_test])\n",
        "merged.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9te5KAvBWqpb",
        "outputId": "03e7a37f-3353-44bc-a6f9-285103eb19d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(15710, 3), (2618, 3), (2619, 3)\n"
          ]
        }
      ],
      "source": [
        "#Split the data\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "df_train, df_val = train_test_split(merged, train_size=0.75, stratify=merged['label'])\n",
        "df_val, df_test = train_test_split(df_val, test_size=0.5, stratify=df_val['label'])\n",
        "\n",
        "print(\"{}, {}, {}\".format(df_train.shape, df_val.shape, df_test.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "V74DMr5QN7Ir",
        "outputId": "c91ef1bd-912b-4d3f-cd3f-b8d0beeb3b47"
      },
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              "                                                    text  label  \\\n",
              "1771               serius free ong aku di jember soalnya      1   \n",
              "12752  Selamat siang wargauroboyo Beberapa waktu yang...      1   \n",
              "1355   restoran yang menyajikan hidangan akulturasi b...      0   \n",
              "13922  2nd giveaway aku mau adain giveaway dalam rang...      1   \n",
              "5083   bu susi karena pengalaman usaha perikanan jadi...      0   \n",
              "...                                                  ...    ...   \n",
              "10244  padahal baru beli paket 9 gb dan masih 3 gb , ...      2   \n",
              "6236   terima kasih pak untuk kinerja bapak yang begi...      0   \n",
              "1689                                  ini kamu dom mana       1   \n",
              "10114  amin . salut buat para dokter yang telah membe...      0   \n",
              "5471   masakan padang ini menurut saya rasanya sama s...      0   \n",
              "\n",
              "                                              text_clean  \n",
              "1771                  serius free ong aku di jember soal  \n",
              "12752  selamat siang wargauroboyo beberapa waktu yang...  \n",
              "1355   restoran yang saji hidang akulturasi belanda d...  \n",
              "13922  2nd giveaway aku mau adain giveaway dalam rang...  \n",
              "5083   bu sus karena alam usaha ikan jadi tahu masala...  \n",
              "...                                                  ...  \n",
              "10244  padahal baru beli paket 9 gb dan masih 3 gb ko...  \n",
              "6236   terima kasih pak untuk kerja bapak yang begitu...  \n",
              "1689                                   ini kamu dom mana  \n",
              "10114  amin salut buat para dokter yang telah beri ba...  \n",
              "5471   masakan padang ini turut saya rasa sama sepert...  \n",
              "\n",
              "[15710 rows x 3 columns]"
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              "      <td>serius free ong aku di jember soal</td>\n",
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              "    <tr>\n",
              "      <th>6236</th>\n",
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              "      <td>0</td>\n",
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              "      <td>ini kamu dom mana</td>\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUDBzjqXVRoQ"
      },
      "source": [
        "# NNLM Transfer Learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GbATv50tLFj8"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "53lIbwYv35bZ"
      },
      "outputs": [],
      "source": [
        "#Fill the missing value\n",
        "\n",
        "x_train = df_train['text_clean']\n",
        "# x_train = df_train['text']\n",
        "y_train = df_train['label']\n",
        "\n",
        "# x_val = df_val['text_clean']\n",
        "x_val = df_val['text']\n",
        "y_val = df_val['label']\n",
        "\n",
        "# x_test = df_test['text_clean']\n",
        "x_test = df_test['text']\n",
        "y_test = df_test['label']\n",
        "\n",
        "x_train.fillna(value='', inplace=True)\n",
        "x_val.fillna(value='', inplace=True)\n",
        "x_test.fillna(value='', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhv5U_a5J2QC",
        "outputId": "06d4a15f-50fa-4f2b-b465-098e39e238b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((15710,), (15710,))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "#See the datatrain shape\n",
        "(x_train.shape, y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nOgoxFXbCJLA"
      },
      "outputs": [],
      "source": [
        "batch_size = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2xr1Wv7bVEkP"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "#Even up the data length\n",
        "\n",
        "train_dataset = (\n",
        "    tf.data.Dataset\n",
        "    .from_tensor_slices((x_train, y_train))\n",
        "    .shuffle(1000)\n",
        "    .batch(batch_size).prefetch(tf.data.AUTOTUNE)\n",
        ")\n",
        "\n",
        "val_dataset = (\n",
        "    tf.data.Dataset\n",
        "    .from_tensor_slices((x_val, y_val))\n",
        "    .shuffle(1000)\n",
        "    .batch(batch_size).prefetch(tf.data.AUTOTUNE)\n",
        ")\n",
        "\n",
        "test_dataset = (\n",
        "    tf.data.Dataset\n",
        "    .from_tensor_slices((x_test, y_test))\n",
        "    # .shuffle(1000)\n",
        "    .batch(batch_size).prefetch(tf.data.AUTOTUNE)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lE7_BltOw_xl",
        "outputId": "0d78bc62-7b4c-4c07-d36a-8190d12ec26f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x[0].ndim:  1\n",
            "x[0].shape:  (64,)\n",
            "x[1].shape:  (64,)\n",
            "x[0].ndim:  1\n",
            "x[0].shape:  (64,)\n",
            "x[1].shape:  (64,)\n"
          ]
        }
      ],
      "source": [
        "#See Dataset Length\n",
        "\n",
        "for i,x in enumerate(train_dataset):\n",
        "  print(\"x[0].ndim: \", x[0].ndim)\n",
        "  print(\"x[0].shape: \", x[0].shape)\n",
        "  print(\"x[1].shape: \", x[1].shape)\n",
        "  if i==0:\n",
        "    break\n",
        "\n",
        "for i,x in enumerate(test_dataset):\n",
        "  print(\"x[0].ndim: \", x[0].ndim)\n",
        "  print(\"x[0].shape: \", x[0].shape)\n",
        "  print(\"x[1].shape: \", x[1].shape)\n",
        "  if i==0:\n",
        "    break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z7WjbTq1VLYC"
      },
      "outputs": [],
      "source": [
        "import tensorflow_hub as hub\n",
        "from tensorflow.keras import Sequential, layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YRpS-PWOVQsp"
      },
      "outputs": [],
      "source": [
        "#Call the NNLM model\n",
        "nnlm = \"https://tfhub.dev/google/nnlm-id-dim128-with-normalization/2\"\n",
        "hub_layer = hub.KerasLayer(nnlm, input_shape=[],dtype=tf.string, trainable=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVgiRaAqcTa4"
      },
      "source": [
        "# Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmFtV0fjcTa4",
        "outputId": "075d1163-d4f3-4ae2-a9f5-eda45540574d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " keras_layer (KerasLayer)    (None, 128)               112461824 \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 99        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 112,499,075\n",
            "Trainable params: 112,499,075\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#Create the model\n",
        "model = Sequential([\n",
        "    hub_layer,\n",
        "    layers.Dropout(0.5),\n",
        "    layers.Dense(128),\n",
        "    layers.Dropout(0.5),\n",
        "    layers.Dense(128),\n",
        "    layers.Dropout(0.5),\n",
        "    layers.Dense(32),\n",
        "    layers.Dropout(0.5),\n",
        "\n",
        "    layers.Dense(3, activation='softmax'),\n",
        "])\n",
        "model.compile(optimizer=tf.optimizers.Adam(),loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDbQnN2l2mWw",
        "outputId": "4e72e39c-dae6-49b7-e625-55d0e35b8a65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "246/246 [==============================] - 45s 149ms/step - loss: 0.7826 - accuracy: 0.6480 - val_loss: 0.5176 - val_accuracy: 0.7922\n",
            "Epoch 2/10\n",
            "246/246 [==============================] - 16s 64ms/step - loss: 0.5331 - accuracy: 0.7875 - val_loss: 0.4767 - val_accuracy: 0.8052\n",
            "Epoch 3/10\n",
            "246/246 [==============================] - 13s 53ms/step - loss: 0.4244 - accuracy: 0.8376 - val_loss: 0.4956 - val_accuracy: 0.7960\n",
            "Epoch 4/10\n",
            "246/246 [==============================] - 12s 51ms/step - loss: 0.3526 - accuracy: 0.8691 - val_loss: 0.5139 - val_accuracy: 0.7991\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_dataset, epochs=10,validation_data=(val_dataset), callbacks=tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VziG9Q_G18UP",
        "outputId": "f520f1ae-189a-4ed8-9f8c-7d8b43f169f9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Visualize the Training & Validation's Accuracy and Loss\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_loss_acc(history):\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",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.plot(epochs, acc, label='Training accuracy')\n",
        "  plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "  plt.legend(loc='upper left')\n",
        "  plt.title('Training and validation accuracy')\n",
        "\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.plot(epochs, loss, label='Training Loss')\n",
        "  plt.plot(epochs, val_loss, label='Validation Loss')\n",
        "  plt.legend(loc='lower right')\n",
        "  plt.title('Training and validation loss')\n",
        " \n",
        "  plt.show()\n",
        "# Plot training results\n",
        "plot_loss_acc(history)\n",
        "     "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3MLEzWwVKmJ",
        "outputId": "0c27b244-c38c-479b-aec6-cf1f747c69aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41/41 - 0s - loss: 0.4596 - accuracy: 0.8209 - 259ms/epoch - 6ms/step\n",
            "Model accuracy:  0.8209240436553955\n"
          ]
        }
      ],
      "source": [
        "#Accuracy test\n",
        "test_loss, test_acc = model.evaluate(test_dataset, verbose=2)\n",
        "print('Model accuracy: ',test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RSuJRMwMC6ew"
      },
      "outputs": [],
      "source": [
        "sentiment = ['Positive','Neutral','Negative']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "TiG_Y7I524dm",
        "outputId": "2154c7ab-4cff-4960-b847-828a8f55d32a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 194ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Negative'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "#Sentiment Prediction Test\n",
        "sequence = ['kerjanya bagus tapi lama banget bikin kesel']\n",
        "sentiment[model.predict(sequence).argmax(axis=1)[0]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxS3ct9hC57a",
        "outputId": "9967f5d1-2605-47c6-f50f-a3239044d6be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41/41 [==============================] - 0s 6ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(test_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCU2cD224s4m"
      },
      "source": [
        "Evaluate the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0H35R4F4vBm",
        "outputId": "9c12363d-08cd-4ee8-a896-93f4a6398351"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Positive       0.87      0.93      0.90       920\n",
            "     Neutral       0.79      0.76      0.78       779\n",
            "    Negative       0.77      0.75      0.76       920\n",
            "\n",
            "    accuracy                           0.82      2619\n",
            "   macro avg       0.81      0.81      0.81      2619\n",
            "weighted avg       0.81      0.82      0.81      2619\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, np.around(predictions, decimals=0).argmax(axis=1), target_names=['Positive', 'Neutral', 'Negative']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37p1yN134rTr"
      },
      "source": [
        "Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "7doBIS8LVPBO",
        "outputId": "75a6ad30-845c-436d-fbcb-e552046ee9bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-ed286f56bf55>:8: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 23.52222222222222, 'Prediction')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "\n",
        "matrix = confusion_matrix(y_test, np.around(predictions, decimals=0).argmax(axis=1))\n",
        "conf_matrix = pd.DataFrame(matrix, index = ['Positive','Neutral','Negative'],columns = ['Positive','Neutral','Negative'])\n",
        "#Normalizing\n",
        "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
        "ax = sns.heatmap(conf_matrix, annot=True, annot_kws={\"size\": 15}, cmap='Blues')\n",
        "ax.set_ylabel(\"True Label\", fontsize=14, labelpad=20)\n",
        "ax.set_xlabel(\"Prediction\", fontsize=14, labelpad=20)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "117-22IhRAkj"
      },
      "source": [
        "Saving Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Fhaerj4lez5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f5a4112-2d3d-44ef-90d9-949539e4f995"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/nlp_model.h5 (deflated 68%)\n"
          ]
        }
      ],
      "source": [
        "#Save model in Hierarchical Data Format 5 File.\n",
        "\n",
        "model.save(\"nlp_model.h5\")\n",
        "model.save(\"nlp_model\")\n",
        "\n",
        "!zip -r /content/nlp_model.zip /content/nlp_model.h5"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(\"nlp_model.zip\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "6pyNAWDqKyLR",
        "outputId": "dd673a78-4b86-42f9-b77b-3443e0f3f6d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_35ad32bc-29ee-49f5-b4ae-7ffdbd827c42\", \"nlp_model.zip\", 436210397)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UFVDUHd3mSKm",
        "outputId": "400532dd-6ee5-4de2-99ec-9405ab0e5a79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model file saved to Google Drive.\n"
          ]
        }
      ],
      "source": [
        "# Import the required libraries\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# Define the file ID and destination path\n",
        "file_id = \"1yWbvg1gVbSYvlFXahJkeBjHtfNAfwfJG\"\n",
        "destination_path = \"/content/nlp_model.h5\"\n",
        "\n",
        "# Download the file from Google Drive\n",
        "downloaded_file = drive.CreateFile({'id': file_id})\n",
        "downloaded_file.GetContentFile(destination_path)\n",
        "\n",
        "# Print a success message\n",
        "print(\"Model file saved to Google Drive.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2n2x-7jPnVKQ",
        "outputId": "91ca5380-7d4f-41d1-c171-4ed6003c976c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File saved successfully.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "# Check if the file exists in the destination path\n",
        "if os.path.isfile(destination_path):\n",
        "    print(\"File saved successfully.\")\n",
        "else:\n",
        "    print(\"File download failed.\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "107d7b120a5b68773a87d4067e673630b35702e55ebab3138143d08b1c4d7cbc"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}