NLP_Text_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NLP_Text_Classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzIlNF7hECBk",
        "outputId": "4c4ab2ec-82e3-4ddf-93f6-c1c5e06acecf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/data-sets/IMDB_Dataset.csv')"
      ],
      "metadata": {
        "id": "jH4psu28FA0T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRUn9mJh9kWV"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/data-sets/IMDB_Dataset.csv\")\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idFQJm36Ibds",
        "outputId": "bfe822bd-0e9b-49fd-f8ac-c1baa590b51d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                              review sentiment\n",
            "0  One of the other reviewers has mentioned that ...  positive\n",
            "1  A wonderful little production. <br /><br />The...  positive\n",
            "2  I thought this was a wonderful way to spend ti...  positive\n",
            "3  Basically there's a family where a little boy ...  negative\n",
            "4  Petter Mattei's \"Love in the Time of Money\" is...  positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=df[\"sentiment\"])\n",
        "plt.show()\n",
        "\n",
        "X = df[\"review\"]\n",
        "Y = df[\"sentiment\"]\n",
        "\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "Fl00KBE1IwZ2",
        "outputId": "80354055-f193-471e-9387-42db36ba9df6"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "stream",
          "name": "stdout",
          "text": [
            "0        positive\n",
            "1        positive\n",
            "2        positive\n",
            "3        negative\n",
            "4        positive\n",
            "           ...   \n",
            "49995    positive\n",
            "49996    negative\n",
            "49997    negative\n",
            "49998    negative\n",
            "49999    negative\n",
            "Name: sentiment, Length: 50000, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "\n",
        "le = LabelEncoder()\n",
        "Y = le.fit_transform(Y)\n",
        "print(\"Vocab's labels vector: \", Y, Y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgoxxPeBIejO",
        "outputId": "ea7aaefe-136a-49cd-823a-9383adbee97c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocab's labels vector:  [1 1 1 ... 0 0 0] (50000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
      ],
      "metadata": {
        "id": "OOaZ4RXZIkZl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "\n",
        "max_words = 1000\n",
        "tkn = Tokenizer(num_words=max_words)\n",
        "tkn.fit_on_texts(X_train)\n",
        "seq = tkn.texts_to_sequences(X_train)"
      ],
      "metadata": {
        "id": "j0AdJCWhIm25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seq\n",
        "print(len(seq))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48PHL5TPJ-Pr",
        "outputId": "dc2c4f4d-db80-4430-b43a-1a566a0551d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "max_len = 200\n",
        "padded_docs = pad_sequences(seq, padding = 'pre', maxlen= max_len)\n",
        "print(padded_docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJa0_iz5IpDK",
        "outputId": "27e7539e-42a4-475f-e8ba-e32c2d6c47d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0   0   0 ...  12  27   5]\n",
            " [  0   0   0 ... 452   4  56]\n",
            " [  0   0   0 ... 142   8   3]\n",
            " ...\n",
            " [  2 262  24 ... 229  42   4]\n",
            " [  0   0   0 ...  48  11   6]\n",
            " [  0   0   0 ...  42   4 448]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
        "\n",
        "\n",
        "embedding_vector_features = 30\n",
        "model = Sequential()\n",
        "model.add(Embedding(max_words, embedding_vector_features, input_length=max_len))\n",
        "model.add(LSTM(256))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss = 'binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BcRQgHgIqqt",
        "outputId": "0801f3c6-36ad-42f8-d63d-a5e78c20b120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 200, 30)           30000     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 256)               293888    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 257       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 324,145\n",
            "Trainable params: 324,145\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(padded_docs,Y_train,batch_size=128,epochs=10, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUo5rl21KqPt",
        "outputId": "56c82235-f9bb-4edd-d869-444f28c000f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "250/250 [==============================] - 11s 31ms/step - loss: 0.5568 - accuracy: 0.7066 - val_loss: 0.3687 - val_accuracy: 0.8378\n",
            "Epoch 2/10\n",
            "250/250 [==============================] - 7s 27ms/step - loss: 0.3814 - accuracy: 0.8313 - val_loss: 0.3535 - val_accuracy: 0.8537\n",
            "Epoch 3/10\n",
            "250/250 [==============================] - 7s 27ms/step - loss: 0.3219 - accuracy: 0.8647 - val_loss: 0.3344 - val_accuracy: 0.8591\n",
            "Epoch 4/10\n",
            "250/250 [==============================] - 7s 28ms/step - loss: 0.3113 - accuracy: 0.8686 - val_loss: 0.3278 - val_accuracy: 0.8574\n",
            "Epoch 5/10\n",
            "250/250 [==============================] - 7s 28ms/step - loss: 0.3037 - accuracy: 0.8723 - val_loss: 0.3498 - val_accuracy: 0.8556\n",
            "Epoch 6/10\n",
            "250/250 [==============================] - 8s 31ms/step - loss: 0.2920 - accuracy: 0.8763 - val_loss: 0.3341 - val_accuracy: 0.8579\n",
            "Epoch 7/10\n",
            "250/250 [==============================] - 7s 28ms/step - loss: 0.2822 - accuracy: 0.8809 - val_loss: 0.3510 - val_accuracy: 0.8605\n",
            "Epoch 8/10\n",
            "250/250 [==============================] - 7s 28ms/step - loss: 0.2726 - accuracy: 0.8873 - val_loss: 0.3510 - val_accuracy: 0.8579\n",
            "Epoch 9/10\n",
            "250/250 [==============================] - 7s 28ms/step - loss: 0.2660 - accuracy: 0.8886 - val_loss: 0.3319 - val_accuracy: 0.8560\n",
            "Epoch 10/10\n",
            "250/250 [==============================] - 7s 28ms/step - loss: 0.2571 - accuracy: 0.8940 - val_loss: 0.3297 - val_accuracy: 0.8626\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test Set \n",
        "\n",
        "text_seq = tkn.texts_to_sequences(X_test)\n",
        "test_padded = pad_sequences(text_seq, maxlen = max_len)\n",
        "\n",
        "accr = model.evaluate(test_padded, Y_test)\n",
        "print(accr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtGdRmCWKtBy",
        "outputId": "ab1a77a6-d99d-47c1-e73a-1409790d7e11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 7ms/step - loss: 0.3093 - accuracy: 0.8668\n",
            "[0.30932125449180603, 0.8668000102043152]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 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', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "UOIHSKVtTwp4",
        "outputId": "8f2096b1-bde3-4c09-8c69-81adba981f16"
      },
      "execution_count": null,
      "outputs": [
        {
          "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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# AbNegative rating (3 of 10)\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "comment1 = \"\"\"I'm not sure what accomplished director/producer/cinematographer Joshua Caldwell was thinking taking on this project.\n",
        "This film has got to be the epitome of terrible writing and should be a classroom example of 'what not to do' when writing a screenplay. \n",
        "Why would Joshua take on (clearly) amateur writer Adam Gaines script is beyond me. \n",
        "Even his good directing and excellent cinematography could not save this disaster.\n",
        "Aside from the super obvious plot holes and very poor story overall, the dragged-out unnecessary dialogue made this film unbearable and extremely boring. \n",
        "The way too long 1h 39min film length felt like 4 hours and I found myself saying \"get on with it already, who cares!\" when the two leads would just ramble on about nothing relevant. \n",
        "This movie may have been interesting if it was a 30 min short film (which oddly enough is the only minimal writing experience Adam Gaines has).\n",
        "The acting was decent and Katia Winter was very easy on the eyes to look at, but her chemistry with Simon Quarterman was very unconvincing. \n",
        "Maybe it was the boring dialogue they had that made their chemistry absent.\n",
        "Even the maybe total of 10 minutes of action scenes were overly dragged out. \n",
        "The rest of the film was primarily useless garbage dialogue with absolutely no point to the story - start to finish.\n",
        "Don't waste your time with this one. See the trailer, and that's all the good and interesting parts you'll need to see.\n",
        "This gets a 3/10 strictly for the directing and cinematography.\"\"\"\n",
        "\n",
        "comment2 = \"This was the best movie I have ever seen\"\n",
        "\n",
        "\n",
        "text_seq = tkn.texts_to_sequences(comment2)\n",
        "test_padded = pad_sequences(text_seq, maxlen = max_len)\n",
        "\n",
        "single_sentence_probas = model.predict(test_padded)\n",
        "print(len(single_sentence_probas))\n",
        "predicted_class_label = np.argmax(single_sentence_probas)\n",
        "\n",
        "\n",
        "print(predicted_class_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XStnazZeLlZY",
        "outputId": "fcfd5fe7-cf0c-4e34-b9c9-b66973b02ad4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40\n",
            "3\n"
          ]
        }
      ]
    }
  ]
}