sentiment_analysis_nlp.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sentiment_analysis_nlp.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "SENTIMENT ANALYSIS:\n",
        "\n",
        "Sentiment analysis is the process of detecting positive or negative sentiment in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.\n",
        "\n",
        "Based on a scoring mechanism, sentiment analysis monitors conversations and evaluates language and voice inflections to quantify attitudes, opinions, and emotions related to a business, product or service, or topic. Sentiment analysis is sometimes also referred to as opinion mining. As part of the overall speech analytics system, sentiment analysis is the integral component that determines a customer’s opinions or attitudes.\n",
        "\n",
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)\n"
      ],
      "metadata": {
        "id": "q8UTTmhrn24_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KERAS:\n",
        "\n",
        "Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.\n",
        "\n",
        "\n",
        "Imported the keras estimators such as Preprocessers, models,layers.\n",
        "\n",
        "GOOGLE'S TENSORFLOW also imported"
      ],
      "metadata": {
        "id": "jiwQY_MMpivH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "wpgOXJySnCj2"
      },
      "outputs": [],
      "source": [
        "import numpy\n",
        "import pandas \n",
        "import tensorflow\n",
        "from keras.preprocessing import sequence\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Embedding\n",
        "from keras.layers import LSTM\n",
        "from keras.datasets import imdb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading the kears IMDB dataset already pre-integrated with training and testing data. "
      ],
      "metadata": {
        "id": "mNfAfDvfrBZL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_features = 20000\n",
        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Is5vWYt0nUQq",
        "outputId": "3a7d710b-f1e8-41f5-93b3-5c286ba3ced0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 0s 0us/step\n",
            "17473536/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "htTcVHu5As-M",
        "outputId": "f933cebe-4e79-4b8b-c636-acdaba1b7f33"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[list([1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32])\n",
            " list([1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463, 4369, 5012, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 8163, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 6853, 5, 163, 11, 3215, 10156, 4, 1153, 9, 194, 775, 7, 8255, 11596, 349, 2637, 148, 605, 15358, 8003, 15, 123, 125, 68, 2, 6853, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 8255, 5, 2, 656, 245, 2350, 5, 4, 9837, 131, 152, 491, 18, 2, 32, 7464, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95])\n",
            " list([1, 14, 47, 8, 30, 31, 7, 4, 249, 108, 7, 4, 5974, 54, 61, 369, 13, 71, 149, 14, 22, 112, 4, 2401, 311, 12, 16, 3711, 33, 75, 43, 1829, 296, 4, 86, 320, 35, 534, 19, 263, 4821, 1301, 4, 1873, 33, 89, 78, 12, 66, 16, 4, 360, 7, 4, 58, 316, 334, 11, 4, 1716, 43, 645, 662, 8, 257, 85, 1200, 42, 1228, 2578, 83, 68, 3912, 15, 36, 165, 1539, 278, 36, 69, 2, 780, 8, 106, 14, 6905, 1338, 18, 6, 22, 12, 215, 28, 610, 40, 6, 87, 326, 23, 2300, 21, 23, 22, 12, 272, 40, 57, 31, 11, 4, 22, 47, 6, 2307, 51, 9, 170, 23, 595, 116, 595, 1352, 13, 191, 79, 638, 89, 2, 14, 9, 8, 106, 607, 624, 35, 534, 6, 227, 7, 129, 113])\n",
            " ...\n",
            " list([1, 11, 6, 230, 245, 6401, 9, 6, 1225, 446, 2, 45, 2174, 84, 8322, 4007, 21, 4, 912, 84, 14532, 325, 725, 134, 15271, 1715, 84, 5, 36, 28, 57, 1099, 21, 8, 140, 8, 703, 5, 11656, 84, 56, 18, 1644, 14, 9, 31, 7, 4, 9406, 1209, 2295, 2, 1008, 18, 6, 20, 207, 110, 563, 12, 8, 2901, 17793, 8, 97, 6, 20, 53, 4767, 74, 4, 460, 364, 1273, 29, 270, 11, 960, 108, 45, 40, 29, 2961, 395, 11, 6, 4065, 500, 7, 14492, 89, 364, 70, 29, 140, 4, 64, 4780, 11, 4, 2678, 26, 178, 4, 529, 443, 17793, 5, 27, 710, 117, 2, 8123, 165, 47, 84, 37, 131, 818, 14, 595, 10, 10, 61, 1242, 1209, 10, 10, 288, 2260, 1702, 34, 2901, 17793, 4, 65, 496, 4, 231, 7, 790, 5, 6, 320, 234, 2766, 234, 1119, 1574, 7, 496, 4, 139, 929, 2901, 17793, 7750, 5, 4241, 18, 4, 8497, 13164, 250, 11, 1818, 7561, 4, 4217, 5408, 747, 1115, 372, 1890, 1006, 541, 9303, 7, 4, 59, 11027, 4, 3586, 2])\n",
            " list([1, 1446, 7079, 69, 72, 3305, 13, 610, 930, 8, 12, 582, 23, 5, 16, 484, 685, 54, 349, 11, 4120, 2959, 45, 58, 1466, 13, 197, 12, 16, 43, 23, 2, 5, 62, 30, 145, 402, 11, 4131, 51, 575, 32, 61, 369, 71, 66, 770, 12, 1054, 75, 100, 2198, 8, 4, 105, 37, 69, 147, 712, 75, 3543, 44, 257, 390, 5, 69, 263, 514, 105, 50, 286, 1814, 23, 4, 123, 13, 161, 40, 5, 421, 4, 116, 16, 897, 13, 2, 40, 319, 5872, 112, 6700, 11, 4803, 121, 25, 70, 3468, 4, 719, 3798, 13, 18, 31, 62, 40, 8, 7200, 4, 2, 7, 14, 123, 5, 942, 25, 8, 721, 12, 145, 5, 202, 12, 160, 580, 202, 12, 6, 52, 58, 11418, 92, 401, 728, 12, 39, 14, 251, 8, 15, 251, 5, 2, 12, 38, 84, 80, 124, 12, 9, 23])\n",
            " list([1, 17, 6, 194, 337, 7, 4, 204, 22, 45, 254, 8, 106, 14, 123, 4, 12815, 270, 14437, 5, 16923, 12255, 732, 2098, 101, 405, 39, 14, 1034, 4, 1310, 9, 115, 50, 305, 12, 47, 4, 168, 5, 235, 7, 38, 111, 699, 102, 7, 4, 4039, 9245, 9, 24, 6, 78, 1099, 17, 2345, 16553, 21, 27, 9685, 6139, 5, 2, 1603, 92, 1183, 4, 1310, 7, 4, 204, 42, 97, 90, 35, 221, 109, 29, 127, 27, 118, 8, 97, 12, 157, 21, 6789, 2, 9, 6, 66, 78, 1099, 4, 631, 1191, 5, 2642, 272, 191, 1070, 6, 7585, 8, 2197, 2, 10755, 544, 5, 383, 1271, 848, 1468, 12183, 497, 16876, 8, 1597, 8778, 19280, 21, 60, 27, 239, 9, 43, 8368, 209, 405, 10, 10, 12, 764, 40, 4, 248, 20, 12, 16, 5, 174, 1791, 72, 7, 51, 6, 1739, 22, 4, 204, 131, 9])] [1 0 0 ... 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqYK6UA8rx9Q",
        "outputId": "3cc45d5a-e826-4b7c-ac68-bd476ba708e3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[list([1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 10698, 5, 4, 360, 7, 4, 177, 5760, 394, 354, 4, 123, 9, 1035, 1035, 1035, 10, 10, 13, 92, 124, 89, 488, 7944, 100, 28, 1668, 14, 31, 23, 27, 7479, 29, 220, 468, 8, 124, 14, 286, 170, 8, 157, 46, 5, 27, 239, 16, 179, 15387, 38, 32, 25, 7944, 451, 202, 14, 6, 717])\n",
            " list([1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 2679, 23, 1310, 5, 109, 943, 4, 114, 9, 55, 606, 5, 111, 7, 4, 139, 193, 273, 23, 4, 172, 270, 11, 7216, 10626, 4, 8463, 2801, 109, 1603, 21, 4, 22, 3861, 8, 6, 1193, 1330, 10, 10, 4, 105, 987, 35, 841, 16873, 19, 861, 1074, 5, 1987, 17975, 45, 55, 221, 15, 670, 5304, 526, 14, 1069, 4, 405, 5, 2438, 7, 27, 85, 108, 131, 4, 5045, 5304, 3884, 405, 9, 3523, 133, 5, 50, 13, 104, 51, 66, 166, 14, 22, 157, 9, 4, 530, 239, 34, 8463, 2801, 45, 407, 31, 7, 41, 3778, 105, 21, 59, 299, 12, 38, 950, 5, 4521, 15, 45, 629, 488, 2733, 127, 6, 52, 292, 17, 4, 6936, 185, 132, 1988, 5304, 1799, 488, 2693, 47, 6, 392, 173, 4, 2, 4378, 270, 2352, 4, 1500, 7, 4, 65, 55, 73, 11, 346, 14, 20, 9, 6, 976, 2078, 7, 5293, 861, 12746, 5, 4182, 30, 3127, 2, 56, 4, 841, 5, 990, 692, 8, 4, 1669, 398, 229, 10, 10, 13, 2822, 670, 5304, 14, 9, 31, 7, 27, 111, 108, 15, 2033, 19, 7836, 1429, 875, 551, 14, 22, 9, 1193, 21, 45, 4829, 5, 45, 252, 8, 12508, 6, 565, 921, 3639, 39, 4, 529, 48, 25, 181, 8, 67, 35, 1732, 22, 49, 238, 60, 135, 1162, 14, 9, 290, 4, 58, 10, 10, 472, 45, 55, 878, 8, 169, 11, 374, 5687, 25, 203, 28, 8, 818, 12, 125, 4, 3077])\n",
            " list([1, 111, 748, 4368, 1133, 2, 2, 4, 87, 1551, 1262, 7, 31, 318, 9459, 7, 4, 498, 5076, 748, 63, 29, 5161, 220, 686, 10941, 5, 17, 12, 575, 220, 2507, 17, 6, 185, 132, 2, 16, 53, 928, 11, 2, 74, 4, 438, 21, 27, 10044, 589, 8, 22, 107, 2, 19550, 997, 1638, 8, 35, 2076, 9019, 11, 22, 231, 54, 29, 1706, 29, 100, 18995, 2425, 34, 12998, 8738, 2, 5, 19353, 98, 31, 2122, 33, 6, 58, 14, 3808, 1638, 8, 4, 365, 7, 2789, 3761, 356, 346, 4, 2, 1060, 63, 29, 93, 11, 5421, 11, 15236, 33, 6, 58, 54, 1270, 431, 748, 7, 32, 2580, 16, 11, 94, 19469, 10, 10, 4, 993, 2, 7, 4, 1766, 2634, 2164, 2, 8, 847, 8, 1450, 121, 31, 7, 27, 86, 2663, 10760, 16, 6, 465, 993, 2006, 2, 573, 17, 2, 42, 4, 17345, 37, 473, 6, 711, 6, 8869, 7, 328, 212, 70, 30, 258, 11, 220, 32, 7, 108, 21, 133, 12, 9, 55, 465, 849, 3711, 53, 33, 2071, 1969, 37, 70, 1144, 4, 5940, 1409, 74, 476, 37, 62, 91, 1329, 169, 4, 1330, 10104, 146, 655, 2212, 5, 258, 12, 184, 10104, 546, 5, 849, 10333, 7, 4, 22, 1436, 18, 631, 1386, 797, 7, 4, 8712, 71, 348, 425, 4320, 1061, 19, 10288, 5, 12141, 11, 661, 8, 339, 17863, 4, 2455, 11434, 7, 4, 1962, 10, 10, 263, 787, 9, 270, 11, 6, 9466, 4, 2, 2, 121, 4, 5437, 26, 4434, 19, 68, 1372, 5, 28, 446, 6, 318, 7149, 8, 67, 51, 36, 70, 81, 8, 4392, 2294, 36, 1197, 8, 2, 2, 18, 6, 711, 4, 9909, 26, 10296, 1125, 11, 14, 636, 720, 12, 426, 28, 77, 776, 8, 97, 38, 111, 7489, 6175, 168, 1239, 5189, 137, 2, 18, 27, 173, 9, 2399, 17, 6, 12397, 428, 14657, 232, 11, 4, 8014, 37, 272, 40, 2708, 247, 30, 656, 6, 13182, 54, 2, 3292, 98, 6, 2840, 40, 558, 37, 6093, 98, 4, 17345, 1197, 15, 14, 9, 57, 4893, 5, 4659, 6, 275, 711, 7937, 2, 3292, 98, 6, 2, 10, 10, 6639, 19, 14, 10241, 267, 162, 711, 37, 5900, 752, 98, 4, 17345, 2378, 90, 19, 6, 2, 7, 2, 1810, 2, 4, 4770, 3183, 930, 8, 508, 90, 4, 1317, 8, 4, 2, 17, 15454, 3965, 1853, 4, 1494, 8, 4468, 189, 4, 2, 6287, 5774, 4, 4770, 5, 95, 271, 23, 6, 7742, 6063, 2, 5437, 33, 1526, 6, 425, 3155, 2, 4535, 1636, 7, 4, 4669, 11966, 469, 4, 4552, 54, 4, 150, 5664, 17345, 280, 53, 2, 2, 18, 339, 29, 1978, 27, 7885, 5, 17303, 68, 1830, 19, 6571, 14605, 4, 1515, 7, 263, 65, 2132, 34, 6, 5680, 7489, 43, 159, 29, 9, 4706, 9, 387, 73, 195, 584, 10, 10, 1069, 4, 58, 810, 54, 14, 6078, 117, 22, 16, 93, 5, 1069, 4, 192, 15, 12, 16, 93, 34, 6, 1766, 2, 33, 4, 5673, 7, 15, 18760, 9252, 3286, 325, 12, 62, 30, 776, 8, 67, 14, 17, 6, 12214, 44, 148, 687, 2, 203, 42, 203, 24, 28, 69, 2, 6676, 11, 330, 54, 29, 93, 2, 21, 845, 14148, 27, 1099, 7, 819, 4, 22, 1407, 17, 6, 14967, 787, 7, 2460, 19569, 2, 100, 30, 4, 3737, 3617, 3169, 2321, 42, 1898, 11, 4, 3814, 42, 101, 704, 7, 101, 999, 15, 1625, 94, 2926, 180, 5, 9, 9101, 34, 15205, 45, 6, 1429, 22, 60, 6, 1220, 31, 11, 94, 6408, 96, 21, 94, 749, 9, 57, 975])\n",
            " ...\n",
            " list([1, 13, 1408, 15, 8, 135, 14, 9, 35, 32, 46, 394, 20, 62, 30, 5093, 21, 45, 184, 78, 4, 1492, 910, 769, 2290, 2515, 395, 4257, 5, 1454, 11, 119, 16946, 89, 1036, 4, 116, 218, 78, 21, 407, 100, 30, 128, 262, 15, 7, 185, 2280, 284, 1842, 2, 37, 315, 4, 226, 20, 272, 2942, 40, 29, 152, 60, 181, 8, 30, 50, 553, 362, 80, 119, 12, 21, 846, 5518])\n",
            " list([1, 11, 119, 241, 9, 4, 840, 20, 12, 468, 15, 94, 3684, 562, 791, 39, 4, 86, 107, 8, 97, 14, 31, 33, 4, 2960, 7, 743, 46, 1028, 9, 3531, 5, 4, 768, 47, 8, 79, 90, 145, 164, 162, 50, 6, 501, 119, 7, 9, 4, 78, 232, 15, 16, 224, 11, 4, 333, 20, 4, 985, 200, 5, 2, 5, 9, 1861, 8, 79, 357, 4, 20, 47, 220, 57, 206, 139, 11, 12, 5, 55, 117, 212, 13, 1276, 92, 124, 51, 45, 1188, 71, 536, 13, 520, 14, 20, 6, 2302, 7, 470])\n",
            " list([1, 6, 52, 7465, 430, 22, 9, 220, 2594, 8, 28, 2, 519, 3227, 6, 769, 15, 47, 6, 3482, 4067, 8, 114, 5, 33, 222, 31, 55, 184, 704, 5586, 18020, 19, 346, 3153, 5, 6, 364, 350, 4, 184, 5586, 9, 133, 1810, 11, 5417, 13226, 21, 4, 7298, 2, 570, 50, 2005, 2643, 9, 6, 1249, 17, 6, 2, 2, 21, 17, 6, 1211, 232, 1138, 2249, 29, 266, 56, 96, 346, 194, 308, 9, 194, 21, 29, 218, 1078, 19, 4, 78, 173, 7, 27, 2, 5698, 3406, 718, 2, 9, 6, 6907, 17, 210, 5, 3281, 5677, 47, 77, 395, 14, 172, 173, 18, 2740, 2931, 4517, 82, 127, 27, 173, 11, 6, 392, 217, 21, 50, 9, 57, 65, 12, 14274, 53, 40, 35, 390, 7, 11, 4, 3567, 7, 4, 314, 74, 6, 792, 22, 16261, 19, 714, 727, 5205, 382, 4, 91, 6533, 439, 19, 14, 20, 9, 1441, 5805, 1118, 4, 756, 25, 124, 4, 31, 12, 16, 93, 804, 34, 2005, 2643])] [0 1 1 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(x_train), 'train sequences')\n",
        "print(len(x_test), 'test sequences')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vM8yw2upnWLv",
        "outputId": "cb1ced40-5691-40ce-af89-1d893f276a55"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25000 train sequences\n",
            "25000 test sequences\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxlen = 180\n",
        "batch_size = 32"
      ],
      "metadata": {
        "id": "mcQUJbgBnwjI"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\n",
        "x_test = sequence.pad_sequences(x_test, maxlen=maxlen)"
      ],
      "metadata": {
        "id": "veI3V65nn3BF"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('x_train shape:', x_train.shape)\n",
        "print('x_test shape:', x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKjCbh5qn7ic",
        "outputId": "0321825a-ad89-4cec-ccff-cc9a1fbbf29d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (25000, 180)\n",
            "x_test shape: (25000, 180)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To view the complete matrix"
      ],
      "metadata": {
        "id": "V92k_px0sQXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "with numpy.printoptions(threshold=numpy.inf):\n",
        "    print(x_train[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGRY1iYDBHMl",
        "outputId": "088c2ada-5be9-4289-a17c-8222f073012a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1463  4369  5012   134    26     4   715     8   118  1634    14   394\n",
            "    20    13   119   954   189   102     5   207   110  3103    21    14\n",
            "    69   188     8    30    23     7     4   249   126    93     4   114\n",
            "     9  2300  1523     5   647     4   116     9    35  8163     4   229\n",
            "     9   340  1322     4   118     9     4   130  4901    19     4  1002\n",
            "     5    89    29   952    46    37     4   455     9    45    43    38\n",
            "  1543  1905   398     4  1649    26  6853     5   163    11  3215 10156\n",
            "     4  1153     9   194   775     7  8255 11596   349  2637   148   605\n",
            " 15358  8003    15   123   125    68     2  6853    15   349   165  4362\n",
            "    98     5     4   228     9    43     2  1157    15   299   120     5\n",
            "   120   174    11   220   175   136    50     9  4373   228  8255     5\n",
            "     2   656   245  2350     5     4  9837   131   152   491    18     2\n",
            "    32  7464  1212    14     9     6   371    78    22   625    64  1382\n",
            "     9     8   168   145    23     4  1690    15    16     4  1355     5\n",
            "    28     6    52   154   462    33    89    78   285    16   145    95]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a \"tf.keras.Sequential\" model and start with an embedding layer which store one vector per word.It convert sequences of word indicies to sequences of vectors.These vectors are trainable.\n",
        "It followed by LSTM layer it is belongs to RNN.RNN has a drawback of Short term memory storage.If a sequence is long enough, they have a hard time carrying information from earlier time steps to later ones.So, adding an LSTM layer with dropout of 0.2, reccurrent_dropout of 0.2.Then a Dense layer with sigmiod function."
      ],
      "metadata": {
        "id": "pBbK_rSYASDF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(max_features, 128))\n",
        "model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "FyRxJNwSn-pD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a binary classification problem and the model outputs logits (a single-unit layer with a linear activation), we'll use the binary_crossentropy loss function. It is better for dealing with probabilities—it measures the \"distance\" between probability distributions, or in our |case, between the ground-truth distribution and the predictions"
      ],
      "metadata": {
        "id": "ejfuWS_AEPFI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "eOdgrXg1oQBg"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL FIT/TEST SCORE"
      ],
      "metadata": {
        "id": "i9aY3tbwB4qb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=8,\n",
        "          validation_data=(x_test, y_test))\n",
        "score, acc = model.evaluate(x_test, y_test,\n",
        "                            batch_size=batch_size)\n",
        "print('Test score:', score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BELw1PQToVGJ",
        "outputId": "733cab77-365c-44fa-f878-3ef1669b15fa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "782/782 [==============================] - 496s 634ms/step - loss: 0.4290 - accuracy: 0.7986 - val_loss: 0.3182 - val_accuracy: 0.8651\n",
            "Epoch 2/8\n",
            "782/782 [==============================] - 494s 632ms/step - loss: 0.2481 - accuracy: 0.9045 - val_loss: 0.4956 - val_accuracy: 0.8338\n",
            "Epoch 3/8\n",
            "782/782 [==============================] - 485s 620ms/step - loss: 0.1569 - accuracy: 0.9423 - val_loss: 0.3618 - val_accuracy: 0.8560\n",
            "Epoch 4/8\n",
            "782/782 [==============================] - 489s 626ms/step - loss: 0.1222 - accuracy: 0.9572 - val_loss: 0.4114 - val_accuracy: 0.8567\n",
            "Epoch 5/8\n",
            "782/782 [==============================] - 489s 625ms/step - loss: 0.0799 - accuracy: 0.9722 - val_loss: 0.5466 - val_accuracy: 0.8520\n",
            "Epoch 6/8\n",
            "782/782 [==============================] - 491s 627ms/step - loss: 0.0559 - accuracy: 0.9818 - val_loss: 0.5893 - val_accuracy: 0.8504\n",
            "Epoch 7/8\n",
            "782/782 [==============================] - 490s 627ms/step - loss: 0.0451 - accuracy: 0.9851 - val_loss: 0.5659 - val_accuracy: 0.8510\n",
            "Epoch 8/8\n",
            "782/782 [==============================] - 490s 626ms/step - loss: 0.0347 - accuracy: 0.9895 - val_loss: 0.7645 - val_accuracy: 0.8379\n",
            "782/782 [==============================] - 51s 65ms/step - loss: 0.7645 - accuracy: 0.8379\n",
            "Test score: 0.7645239233970642\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using kears, A function to input text encode and decode"
      ],
      "metadata": {
        "id": "TgX5k7XzEsH_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8S26xsmFZChi",
        "outputId": "c6eb6df4-5cfd-4551-b109-a93b3afbc1df"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1646592/1641221 [==============================] - 0s 0us/step\n",
            "1654784/1641221 [==============================] - 0s 0us/step\n",
            "[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "   0   0   0   0   0   0   0   0   0   0   0  12  17  13  40 477  35 477]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ENCODE"
      ],
      "metadata": {
        "id": "tfHw6y10FBZp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuqjaD1gZP7l",
        "outputId": "9e532afd-b2b2-4b96-d2ed-cb2204c2369d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "that movie was just amazing so amazing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DECODE"
      ],
      "metadata": {
        "id": "P5BIcdgcFDuL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def predict(text):\n",
        "  encoded_text = encode_text(text)\n",
        "  pred = np.zeros((1,180))\n",
        "  pred[0] = encoded_text\n",
        "  result = model.predict(pred) \n",
        "  print(result[0])"
      ],
      "metadata": {
        "id": "enoOdrxlZUeu"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "PREDICT AN REVIEW"
      ],
      "metadata": {
        "id": "WAtMHGKsFGQD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "positive_review = \"That movie not loved\"\n",
        "x = predict(positive_review)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7Ev_25zZaF-",
        "outputId": "95916a64-dead-4148-fd62-827882ead0d5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.185637]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "predictions = predict(negative_review)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-lkrlreZncy",
        "outputId": "47548511-1494-44df-c30f-59b4dd6b32ac"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.03698999]\n"
          ]
        }
      ]
    }
  ]
}