Sentiment_Analysis_on_IMDB_movie_Review.ipynb

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    "colab": {
      "name": "Sentiment Analysis on IMDB movie Review.ipynb",
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      "authorship_tag": "ABX9TyMk+qGMIwEVU/nvUtjCqZTv",
      "include_colab_link": true
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      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpitaChatterjee/Sentiment-Analysis/blob/main/Sentiment_Analysis_on_IMDB_movie_Review.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSHPU1KRlcWs",
        "outputId": "87ad2e61-236f-462e-ec2c-649064006b86"
      },
      "source": [
        "%tensorflow_version 2.x  # this line is not required unless you are in a notebook\n",
        "from keras.datasets import imdb\n",
        "from keras.preprocessing import sequence\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import numpy as np\n",
        "\n",
        "VOCAB_SIZE = 88584\n",
        "\n",
        "MAXLEN = 250\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words = VOCAB_SIZE)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n",
            "You set: `2.x  # this line is not required unless you are in a notebook`. This will be interpreted as: `2.x`.\n",
            "\n",
            "\n",
            "TensorFlow 2.x selected.\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdSfEA-KzDHr",
        "outputId": "c0030e55-caad-423c-a2de-026f1ebca5d0"
      },
      "source": [
        "#load one dataset\n",
        "train_data[0]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {
            "tags": []
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          "execution_count": 2
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nmxe0saSzIDa"
      },
      "source": [
        "## More preprocessing the data\n",
        "train_data= sequence.pad_sequences(train_data, MAXLEN)\n",
        "test_data= sequence.pad_sequences(test_data, MAXLEN)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6idUiuJP8Pvr"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(VOCAB_SIZE, 32),\n",
        "    tf.keras.layers.LSTM(32),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSmeRX0U8WMg",
        "outputId": "19a6ed1a-3663-41d9-c61c-24e1bf50f49b"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 32)          2834688   \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 32)                8320      \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 2,843,041\n",
            "Trainable params: 2,843,041\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGVw5f999t4a"
      },
      "source": [
        "**Train & Evaluate the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDr7TkZT00Z-",
        "outputId": "b9aa051f-f529-4d3c-a3a7-ef99cf8cd664"
      },
      "source": [
        "model.compile(loss=\"binary_crossentropy\",optimizer=\"rmsprop\",metrics=['acc'])\n",
        "\n",
        "history = model.fit(train_data, train_labels, epochs=10, validation_split=0.2)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "625/625 [==============================] - 49s 76ms/step - loss: 0.5425 - acc: 0.7040 - val_loss: 0.3697 - val_acc: 0.8578\n",
            "Epoch 2/10\n",
            "625/625 [==============================] - 47s 75ms/step - loss: 0.2382 - acc: 0.9128 - val_loss: 0.2916 - val_acc: 0.8874\n",
            "Epoch 3/10\n",
            "625/625 [==============================] - 47s 75ms/step - loss: 0.1760 - acc: 0.9378 - val_loss: 0.2666 - val_acc: 0.8976\n",
            "Epoch 4/10\n",
            "625/625 [==============================] - 47s 75ms/step - loss: 0.1408 - acc: 0.9494 - val_loss: 0.6258 - val_acc: 0.8184\n",
            "Epoch 5/10\n",
            "625/625 [==============================] - 47s 74ms/step - loss: 0.1268 - acc: 0.9555 - val_loss: 0.3799 - val_acc: 0.8892\n",
            "Epoch 6/10\n",
            "625/625 [==============================] - 47s 74ms/step - loss: 0.0990 - acc: 0.9660 - val_loss: 0.3356 - val_acc: 0.8806\n",
            "Epoch 7/10\n",
            "625/625 [==============================] - 46s 74ms/step - loss: 0.0895 - acc: 0.9696 - val_loss: 0.3492 - val_acc: 0.8862\n",
            "Epoch 8/10\n",
            "625/625 [==============================] - 47s 75ms/step - loss: 0.0798 - acc: 0.9744 - val_loss: 0.3585 - val_acc: 0.8794\n",
            "Epoch 9/10\n",
            "625/625 [==============================] - 47s 75ms/step - loss: 0.0687 - acc: 0.9784 - val_loss: 0.4051 - val_acc: 0.8862\n",
            "Epoch 10/10\n",
            "625/625 [==============================] - 47s 75ms/step - loss: 0.0643 - acc: 0.9791 - val_loss: 0.3708 - val_acc: 0.8750\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UnfPbHZ8X6d",
        "outputId": "26ef1c83-5842-41ae-8a9a-c235ce616768"
      },
      "source": [
        "results = model.evaluate(test_data, test_labels)\n",
        "print(results)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "782/782 [==============================] - 14s 17ms/step - loss: 0.4289 - acc: 0.8525\n",
            "[0.4289276599884033, 0.8524799942970276]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xHiZ12x97tT"
      },
      "source": [
        "**Make prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qlk8I8979tIC",
        "outputId": "a4598d99-9b66-4ae8-d43e-27f9ee9e3f46"
      },
      "source": [
        "word_index = imdb.get_word_index()\n",
        "\n",
        "def encode_text(text):\n",
        "  tokens = keras.preprocessing.text.text_to_word_sequence(text)\n",
        "  tokens = [word_index[word] if word in word_index else 0 for word in tokens]\n",
        "  return sequence.pad_sequences([tokens], MAXLEN)[0]\n",
        "\n",
        "text = \"that movie was just amazing, so amazing\"\n",
        "encoded = encode_text(text)\n",
        "print(encoded)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1646592/1641221 [==============================] - 0s 0us/step\n",
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            "   0   0   0   0   0   0   0   0   0  12  17  13  40 477  35 477]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9KriMGD-FJ9",
        "outputId": "ff60528f-e9a2-4ef0-c2ca-10bb10910a33"
      },
      "source": [
        "# while were at it lets make a decode function\n",
        "\n",
        "reverse_word_index = {value: key for (key, value) in word_index.items()}\n",
        "\n",
        "def decode_integers(integers):\n",
        "    PAD = 0\n",
        "    text = \"\"\n",
        "    for num in integers:\n",
        "      if num != PAD:\n",
        "        text += reverse_word_index[num] + \" \"\n",
        "\n",
        "    return text[:-1]\n",
        "  \n",
        "print(decode_integers(encoded))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "that movie was just amazing so amazing\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atgcugsd-I9f",
        "outputId": "4272614b-ca58-4e57-8a0e-7b6a17fba80d"
      },
      "source": [
        "# now time to make a prediction\n",
        "\n",
        "def predict(text):\n",
        "  encoded_text = encode_text(text)\n",
        "  pred = np.zeros((1,250))\n",
        "  pred[0] = encoded_text\n",
        "  result = model.predict(pred) \n",
        "  print(result[0])\n",
        "\n",
        "positive_review = \"That movie was! really loved it and would great watch it again because it was amazingly great\"\n",
        "predict(positive_review)\n",
        "\n",
        "negative_review = \"that movie really sucked. I hated it and wouldn't watch it again. Was one of the worst things I've ever watched\"\n",
        "predict(negative_review)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.94702125]\n",
            "[0.24530876]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}