Sentimental_Analysis_of_Amazon_Review_Dataset_Balanced.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sentimental Analysis of Amazon Review Dataset- Balanced.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "eAIyoB_-fG9g"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "ZrT1FaZREZ03",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Install Packages"
      ]
    },
    {
      "metadata": {
        "id": "vG9S4vusYNAr",
        "colab_type": "code",
        "outputId": "627464ce-094e-4189-b239-64c0d034d178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install -U -q PyDrive\n",
        "!pip install -q glove_python\n",
        "import numpy as np\n",
        "import pandas as pd\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",
        "import nltk\n",
        "from nltk.tokenize import sent_tokenize,word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "import string\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('maxent_ne_chunker')\n",
        "nltk.download('words')\n",
        "nltk.download('universal_tagset')\n",
        "nltk.download('wordnet')\n",
        "lemmatizer = nltk.WordNetLemmatizer()\n",
        "\n",
        "from bs4 import BeautifulSoup  \n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords \n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from nltk.stem import SnowballStemmer, WordNetLemmatizer\n",
        "from nltk import sent_tokenize, word_tokenize, pos_tag\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K    3% |█▎                              | 10kB 18.8MB/s eta 0:00:01\r\u001b[K    7% |██▌                             | 20kB 2.3MB/s eta 0:00:01\r\u001b[K    11% |███▊                            | 30kB 3.3MB/s eta 0:00:01\r\u001b[K    15% |█████                           | 40kB 2.1MB/s eta 0:00:01\r\u001b[K    19% |██████▎                         | 51kB 2.6MB/s eta 0:00:01\r\u001b[K    23% |███████▌                        | 61kB 3.1MB/s eta 0:00:01\r\u001b[K    27% |████████▊                       | 71kB 3.6MB/s eta 0:00:01\r\u001b[K    31% |██████████                      | 81kB 4.0MB/s eta 0:00:01\r\u001b[K    35% |███████████▏                    | 92kB 4.5MB/s eta 0:00:01\r\u001b[K    38% |████████████▌                   | 102kB 3.5MB/s eta 0:00:01\r\u001b[K    42% |█████████████▊                  | 112kB 3.5MB/s eta 0:00:01\r\u001b[K    46% |███████████████                 | 122kB 4.9MB/s eta 0:00:01\r\u001b[K    50% |████████████████▏               | 133kB 4.9MB/s eta 0:00:01\r\u001b[K    54% |█████████████████▍              | 143kB 8.9MB/s eta 0:00:01\r\u001b[K    58% |██████████████████▊             | 153kB 9.1MB/s eta 0:00:01\r\u001b[K    62% |████████████████████            | 163kB 9.2MB/s eta 0:00:01\r\u001b[K    66% |█████████████████████▏          | 174kB 9.2MB/s eta 0:00:01\r\u001b[K    70% |██████████████████████▍         | 184kB 9.3MB/s eta 0:00:01\r\u001b[K    73% |███████████████████████▋        | 194kB 9.3MB/s eta 0:00:01\r\u001b[K    77% |█████████████████████████       | 204kB 37.1MB/s eta 0:00:01\r\u001b[K    81% |██████████████████████████▏     | 215kB 10.5MB/s eta 0:00:01\r\u001b[K    85% |███████████████████████████▍    | 225kB 10.4MB/s eta 0:00:01\r\u001b[K    89% |████████████████████████████▋   | 235kB 10.5MB/s eta 0:00:01\r\u001b[K    93% |█████████████████████████████▉  | 245kB 10.5MB/s eta 0:00:01\r\u001b[K    97% |███████████████████████████████▏| 256kB 10.5MB/s eta 0:00:01\r\u001b[K    100% |████████████████████████████████| 266kB 8.4MB/s \n",
            "\u001b[?25h  Building wheel for glove-python (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package maxent_ne_chunker to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package maxent_ne_chunker is already up-to-date!\n",
            "[nltk_data] Downloading package words to /root/nltk_data...\n",
            "[nltk_data]   Package words is already up-to-date!\n",
            "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
            "[nltk_data]   Package universal_tagset is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "x6geO8DrWm46",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Install Libraries"
      ]
    },
    {
      "metadata": {
        "id": "uzDf-6GqDCUG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from itertools import *\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from nltk import word_tokenize          \n",
        "from nltk.stem import WordNetLemmatizer \n",
        "\n",
        "from nltk.corpus import wordnet\n",
        "\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "%matplotlib inline\n",
        " \n",
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "from scipy.sparse import csr_matrix\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import precision_score, recall_score, make_scorer\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import tree\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "import seaborn as sns\n",
        "\n",
        "from wordcloud import WordCloud\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from imblearn.under_sampling import ClusterCentroids\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "# from sklearn.grid_search import GridSearchCV\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from imblearn.under_sampling import NearMiss, RandomUnderSampler\n",
        "\n",
        "from keras.preprocessing import sequence\n",
        "from keras.utils import np_utils\n",
        "from keras.layers.core import Dense, Dropout, Activation, Lambda\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from collections import defaultdict\n",
        "from keras.layers.convolutional import Convolution1D\n",
        "from keras import backend as K\n",
        "from keras.models import Sequential\n",
        "from keras.layers.recurrent import LSTM, SimpleRNN, GRU\n",
        "\n",
        "import logging\n",
        "from gensim.models import word2vec\n",
        "from gensim.models import Word2Vec\n",
        "from gensim.models.keyedvectors import KeyedVectors\n",
        "#importing the glove library\n",
        "from glove import Corpus, Glove\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cEw-5famWt8i",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Pre-Processing"
      ]
    },
    {
      "metadata": {
        "id": "w6xJUYU6YhBk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "1. Balance Positive and Negative Reviews (Split Equally the Distribution into Positive and Negative Reviews)\n",
        "Metric: \n",
        "1,2 Rating - Negative Review\n",
        "4,5 Rating - Good Review\n",
        "Ignore 3 Rating as its Neutral"
      ]
    },
    {
      "metadata": {
        "id": "zQsQlxKexN_G",
        "colab_type": "code",
        "outputId": "eb40eab7-ff32-4896-d80a-5ee4c0f70579",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        }
      },
      "cell_type": "code",
      "source": [
        "link='https://drive.google.com/open?id=1Z_y9nw9nQCmuQUJyFUF2DkVw_7KX346b'\n",
        "fluff, id = link.split('=')\n",
        "print (id) # Verify that you have everything after '='\n",
        "downloaded = drive.CreateFile({'id':id}) \n",
        "downloaded.GetContentFile('reviews_Baby_5.json')  \n",
        "df = pd.read_json('reviews_Baby_5.json', orient='columns',lines=True)\n",
        "# Dataset is now stored in a Pandas Dataframe\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1Z_y9nw9nQCmuQUJyFUF2DkVw_7KX346b\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>asin</th>\n",
              "      <th>helpful</th>\n",
              "      <th>overall</th>\n",
              "      <th>reviewText</th>\n",
              "      <th>reviewTime</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>reviewerName</th>\n",
              "      <th>summary</th>\n",
              "      <th>unixReviewTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>097293751X</td>\n",
              "      <td>[0, 0]</td>\n",
              "      <td>5</td>\n",
              "      <td>Perfect for new parents. We were able to keep ...</td>\n",
              "      <td>07 16, 2013</td>\n",
              "      <td>A1HK2FQW6KXQB2</td>\n",
              "      <td>Amanda Johnsen \"Amanda E. Johnsen\"</td>\n",
              "      <td>Awesine</td>\n",
              "      <td>1373932800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>097293751X</td>\n",
              "      <td>[0, 0]</td>\n",
              "      <td>5</td>\n",
              "      <td>This book is such a life saver.  It has been s...</td>\n",
              "      <td>06 29, 2013</td>\n",
              "      <td>A19K65VY14D13R</td>\n",
              "      <td>angela</td>\n",
              "      <td>Should be required for all new parents!</td>\n",
              "      <td>1372464000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>097293751X</td>\n",
              "      <td>[0, 0]</td>\n",
              "      <td>5</td>\n",
              "      <td>Helps me know exactly how my babies day has go...</td>\n",
              "      <td>03 19, 2014</td>\n",
              "      <td>A2LL1TGG90977E</td>\n",
              "      <td>Carter</td>\n",
              "      <td>Grandmother watching baby</td>\n",
              "      <td>1395187200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>097293751X</td>\n",
              "      <td>[0, 0]</td>\n",
              "      <td>5</td>\n",
              "      <td>I bought this a few times for my older son and...</td>\n",
              "      <td>08 17, 2013</td>\n",
              "      <td>A5G19RYX8599E</td>\n",
              "      <td>cfpurplerose</td>\n",
              "      <td>repeat buyer</td>\n",
              "      <td>1376697600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>097293751X</td>\n",
              "      <td>[0, 0]</td>\n",
              "      <td>4</td>\n",
              "      <td>I wanted an alternative to printing out daily ...</td>\n",
              "      <td>04 1, 2014</td>\n",
              "      <td>A2496A4EWMLQ7</td>\n",
              "      <td>C. Jeter</td>\n",
              "      <td>Great</td>\n",
              "      <td>1396310400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         asin helpful  overall  \\\n",
              "0  097293751X  [0, 0]        5   \n",
              "1  097293751X  [0, 0]        5   \n",
              "2  097293751X  [0, 0]        5   \n",
              "3  097293751X  [0, 0]        5   \n",
              "4  097293751X  [0, 0]        4   \n",
              "\n",
              "                                          reviewText   reviewTime  \\\n",
              "0  Perfect for new parents. We were able to keep ...  07 16, 2013   \n",
              "1  This book is such a life saver.  It has been s...  06 29, 2013   \n",
              "2  Helps me know exactly how my babies day has go...  03 19, 2014   \n",
              "3  I bought this a few times for my older son and...  08 17, 2013   \n",
              "4  I wanted an alternative to printing out daily ...   04 1, 2014   \n",
              "\n",
              "       reviewerID                        reviewerName  \\\n",
              "0  A1HK2FQW6KXQB2  Amanda Johnsen \"Amanda E. Johnsen\"   \n",
              "1  A19K65VY14D13R                              angela   \n",
              "2  A2LL1TGG90977E                              Carter   \n",
              "3   A5G19RYX8599E                        cfpurplerose   \n",
              "4   A2496A4EWMLQ7                            C. Jeter   \n",
              "\n",
              "                                   summary  unixReviewTime  \n",
              "0                                  Awesine      1373932800  \n",
              "1  Should be required for all new parents!      1372464000  \n",
              "2                Grandmother watching baby      1395187200  \n",
              "3                             repeat buyer      1376697600  \n",
              "4                                    Great      1396310400  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "moDBRsFD_S0I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Remove any 'neutral' ratings equal to 3\n",
        "df = df[df['overall'] != 3]\n",
        "\n",
        "# # df = pd.concat(f1)\n",
        "# df = df.sample(frac=0.7, random_state=0) #uncomment to use full set of data\n",
        "# # Drop missing values\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "# Encode 4s and 5s as 1 (positive sentiment) and 1s and 2s as 0 (negative sentiment)\n",
        "df['Sentiment'] = np.where(df['overall'] > 3, 1, 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-wLY1ly41R0m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = df.sort_values(by=['overall'])\n",
        "# # df = df.head(98000)\n",
        "dfnegative = df.head(16932)\n",
        "dfpositive = df.iloc[16933:,:]\n",
        "dfpositive4 = dfpositive.head(8466)\n",
        "dfpositive5 = dfpositive.tail(8466)\n",
        "\n",
        "f = [dfpositive4,dfpositive5]\n",
        "\n",
        "dfpositives = pd.concat(f)\n",
        "\n",
        "f1 = [dfnegative,dfpositives]\n",
        "\n",
        "df = pd.concat(f1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9aocVpo2t99B",
        "colab_type": "code",
        "outputId": "ac4d3c53-9386-4c28-9e59-cb2692c6e859",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"The Number of Reviews less than rating 3\")\n",
        "df[df['overall'] < 3].shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Number of Reviews less than rating 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16932, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "NneE8PLIt6Pb",
        "colab_type": "code",
        "outputId": "f926e692-4fe6-485c-d597-cc35b7e85e9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"The Number of Reviews greater than 3\")\n",
        "df[df['overall'] > 3].shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Number of Reviews greater than 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16932, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "PiZ3Ue_YBzDl",
        "colab_type": "code",
        "outputId": "e092db1d-bf99-4cd7-a498-5d52e5a13dba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"The Size of Dataset\",df.shape)\n",
        "print('Distribution of Positive and Negative Reviews, Three being the threshold')\n",
        "df.hist('overall')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Size of Dataset (33864, 10)\n",
            "Distribution of Positive and Negative Reviews, Three being the threshold\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b29bfb2e8>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yw27fu3cvy5Ytm/dxt+/aM9L+K49hzvM10zGPYlzzNaoe6xrlHDsSz69Rn1OjOGn5kjl/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\n1CkDQJI6ZQBIUqcMAEnqlAEgSZ36P+rwaGnZuVepAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4tWQufRmCGg0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = df['reviewText']\n",
        "y = df['Sentiment']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "009IjM2SZHC5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Pre-Process the Review Text"
      ]
    },
    {
      "metadata": {
        "id": "afMlpExZZP-d",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Stemming, Remove Punctuation, Remove Non-Characters, HTML Tags,Remove Stop Words"
      ]
    },
    {
      "metadata": {
        "id": "UsoSUlSm-dOw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cleanText(raw_text, remove_stopwords=True, stemming=False, split_text=False, \\\n",
        "             ):\n",
        "    '''\n",
        "    Convert a raw review to a cleaned review\n",
        "    '''\n",
        "    text = BeautifulSoup(raw_text, 'lxml').get_text()  #remove html\n",
        "    letters_only = re.sub(\"[^a-zA-Z]\", \" \", text)  # remove non-character\n",
        "    words = letters_only.lower().split() # convert to lower case \n",
        "    \n",
        "    if remove_stopwords: # remove stopword\n",
        "        stops = set(stopwords.words(\"english\"))\n",
        "        stops.remove('not')\n",
        "        stops.remove('no')\n",
        "        words = [w for w in words if not w in stops]\n",
        "    if stemming==True: # stemming\n",
        "        stemmer = PorterStemmer()\n",
        "        stemmer = SnowballStemmer('english') \n",
        "        words = [stemmer.stem(w) for w in words]\n",
        "        \n",
        "    if split_text==True:  # split text\n",
        "        return (words)\n",
        "    \n",
        "    return( \" \".join(words))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NmrBUUPKDIwx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_cleaned = []\n",
        "    \n",
        "for d in X:\n",
        "    X_cleaned.append(cleanText(d))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KpoCYlsSCXiH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def grid_search(X_train, y_train,X_test,y_test):\n",
        "    classifiers = ['Adaboost','KNN','SVM','MNB','LR']\n",
        "    pipeline1 = Pipeline([(\n",
        "    ('clf', AdaBoostClassifier())\n",
        "    )])\n",
        "\n",
        "    pipeline2 = Pipeline([(\n",
        "    ('clf', KNeighborsClassifier())\n",
        "    )])\n",
        "\n",
        "    pipeline3 = Pipeline([(\n",
        "    ('clf', SVC())\n",
        "    )])\n",
        "\n",
        "    pipeline4 = Pipeline([(\n",
        "    ('clf', MultinomialNB())\n",
        "    )])\n",
        "    \n",
        "    pipeline5 = Pipeline([(\n",
        "    ('clf', LogisticRegression())\n",
        "    )])\n",
        "\n",
        "    parameters1 = {\n",
        "    'clf__n_estimators': [50,100,150],\n",
        "        'clf__learning_rate': [0.1,1.0],\n",
        "\n",
        "    }\n",
        "\n",
        "    parameters2 = {\n",
        "    'clf__n_neighbors': [3, 5, 7, 10],\n",
        "    'clf__weights': ['uniform', 'distance'],\n",
        "    }\n",
        "\n",
        "    parameters3 = {\n",
        "    'clf__C': [0.01, 0.1, 1.0],\n",
        "    'clf__kernel': ['linear'],\n",
        "    'clf__gamma': [0.01, 0.1, 1.0],\n",
        "\n",
        "    }\n",
        "    parameters4 = {\n",
        "    'clf__alpha': [0.01, 0.1, 1.0],\n",
        "    }\n",
        "    \n",
        "    parameters5 = {\n",
        "        'clf__C': [0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0], \n",
        "        \"clf__penalty\":[\"l1\",\"l2\"]\n",
        "    }\n",
        "\n",
        "    pars = [parameters3]\n",
        "    pips = [pipeline3]\n",
        "\n",
        "    print (\"Starting Gridsearch..\")\n",
        "    for i in range(len(pars)):\n",
        "        gs = GridSearchCV(pips[i], pars[i], verbose=2, n_jobs=-1)\n",
        "        gs = gs.fit(X_train, y_train)\n",
        "        print (\"Finished Gridsearch For\",classifiers[i])\n",
        "        print('The Best Model Achieved for {} with Score {} and the best parameters are {}'.format(classifiers[i],gs.best_score_,gs.best_params_))\n",
        "        y_pred = gs.predict(X_test)\n",
        "        print(\"Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
        "        print(classification_report(y_test, y_pred, target_names=target_names))\n",
        "        print(\"Confusion Matrix\",confusion_matrix(y_test, y_pred))    \n",
        "    return"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eAIyoB_-fG9g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Classification using CountVectorizer Bag of Words"
      ]
    },
    {
      "metadata": {
        "id": "G6MI86B8A1cF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "26e687a7-8f5f-4ed6-ff85-7c348db90821"
      },
      "cell_type": "code",
      "source": [
        "countVect = CountVectorizer(min_df = 50, ngram_range = (1,2),strip_accents='unicode', binary=True)\n",
        "X_all_countVect = countVect.fit_transform(X_cleaned)\n",
        "\n",
        "print(\"Number of features : %d \\n\" %len(countVect.get_feature_names())) #1722\n",
        "print(\"Show some feature names : \\n\", countVect.get_feature_names()[::1000])\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of features : 4663 \n",
            "\n",
            "Show some feature names : \n",
            " ['ability', 'device', 'latch', 'pregnant', 'theme']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bU_F1HiGCJW9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1646
        },
        "outputId": "23d0ce99-a43e-4268-9f88-db84a3b55a72"
      },
      "cell_type": "code",
      "source": [
        "knn = []\n",
        "svm = []\n",
        "dt =[]\n",
        "nb =[]\n",
        "lr =[]\n",
        "rus = RandomUnderSampler(random_state=777)\n",
        "X_RUS, y_RUS = rus.fit_sample(X_all_countVect, y)\n",
        "target_names = ['Positive','Negative']\n",
        "skf = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
        "for train_index, test_index in skf.split(X_RUS, y_RUS):\n",
        "  X_train_countVect = X_RUS[train_index]\n",
        "  y_train = y_RUS[train_index]\n",
        "  X_test_countVect = X_RUS[test_index]\n",
        "  y_test = y_RUS[test_index]\n",
        "  \n",
        "  \n",
        "  knn_mean = knn_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  knn.append(knn_mean)\n",
        "  dt_mean = dt_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  dt.append(dt_mean)\n",
        "  nb_mean = nb_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  nb.append(nb_mean)\n",
        "  lr_mean = lr_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  lr.append(lr_mean)\n",
        "  svm_mean = svc_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  svm.append(svm_mean)\n",
        "  \n",
        "  \n",
        "  print('The Accuracy for KNN:',sum(knn)/len(knn))\n",
        "  print('The Accuracy for SVM:',sum(svm)/len(svm))\n",
        "  print('The Accuracy for DT:',sum(dt)/len(dt))\n",
        "  print('The Accuracy for MNB:',sum(nb)/len(nb))\n",
        "  print('The Accuracy for LR:',sum(lr)/len(lr))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNN Results:\n",
            "KNN Accuracy: 0.629317980513729\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.72      0.66      3387\n",
            "           1       0.66      0.54      0.59      3387\n",
            "\n",
            "   micro avg       0.63      0.63      0.63      6774\n",
            "   macro avg       0.63      0.63      0.63      6774\n",
            "weighted avg       0.63      0.63      0.63      6774\n",
            "\n",
            "Confusion Matrix [[2427  960]\n",
            " [1551 1836]]\n",
            "KNN Train Accuracy: 0.7705057216685124\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.82      0.78     13545\n",
            "           1       0.80      0.72      0.76     13545\n",
            "\n",
            "   micro avg       0.77      0.77      0.77     27090\n",
            "   macro avg       0.77      0.77      0.77     27090\n",
            "weighted avg       0.77      0.77      0.77     27090\n",
            "\n",
            "Adaboosting Results:\n",
            "Adaboosting DT Accuracy: 0.8813108945969885\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Positive       0.87      0.90      0.88      3387\n",
            "    Negative       0.90      0.86      0.88      3387\n",
            "\n",
            "   micro avg       0.88      0.88      0.88      6774\n",
            "   macro avg       0.88      0.88      0.88      6774\n",
            "weighted avg       0.88      0.88      0.88      6774\n",
            "\n",
            "Confusion Matrix [[3056  331]\n",
            " [ 473 2914]]\n",
            "Adaboosting DT Train Accuracy: 0.861203396087117\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Positive       0.86      0.86      0.86     13545\n",
            "    Negative       0.86      0.86      0.86     13545\n",
            "\n",
            "   micro avg       0.86      0.86      0.86     27090\n",
            "   macro avg       0.86      0.86      0.86     27090\n",
            "weighted avg       0.86      0.86      0.86     27090\n",
            "\n",
            "NB Results:\n",
            "MNB Accuracy: 0.8990256864481843\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Positive       0.88      0.93      0.90      3387\n",
            "    Negative       0.92      0.87      0.90      3387\n",
            "\n",
            "   micro avg       0.90      0.90      0.90      6774\n",
            "   macro avg       0.90      0.90      0.90      6774\n",
            "weighted avg       0.90      0.90      0.90      6774\n",
            "\n",
            "Confusion Matrix [[3145  242]\n",
            " [ 442 2945]]\n",
            "MNB Train Accuracy: 0.8693983019564415\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Positive       0.87      0.86      0.87     13545\n",
            "    Negative       0.87      0.87      0.87     13545\n",
            "\n",
            "   micro avg       0.87      0.87      0.87     27090\n",
            "   macro avg       0.87      0.87      0.87     27090\n",
            "weighted avg       0.87      0.87      0.87     27090\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "LR Results:\n",
            "LR Accuracy: 0.8940064954236788\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Positive       0.88      0.92      0.90      3387\n",
            "    Negative       0.91      0.87      0.89      3387\n",
            "\n",
            "   micro avg       0.89      0.89      0.89      6774\n",
            "   macro avg       0.89      0.89      0.89      6774\n",
            "weighted avg       0.89      0.89      0.89      6774\n",
            "\n",
            "Confusion Matrix [[3109  278]\n",
            " [ 440 2947]]\n",
            "LR Train Accuracy: 0.9347729789590254\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Positive       0.93      0.94      0.93     13545\n",
            "    Negative       0.93      0.93      0.93     13545\n",
            "\n",
            "   micro avg       0.93      0.93      0.93     27090\n",
            "   macro avg       0.93      0.93      0.93     27090\n",
            "weighted avg       0.93      0.93      0.93     27090\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a-BTLkPfBFdO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Classification Using Tf-IDF BoW Model"
      ]
    },
    {
      "metadata": {
        "id": "5LsiPiM3BEK0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "6a969a3f-40ac-45b7-9082-a4b09fec21f6"
      },
      "cell_type": "code",
      "source": [
        "# Fit and transform the training data to a document-term matrix using TfidfVectorizer \n",
        "tfidf = TfidfVectorizer(min_df = 5, ngram_range = (1,2),strip_accents='unicode', binary=True,max_features=5000) #minimum document frequency of 5\n",
        "X_all_tfidf = tfidf.fit_transform(X_cleaned)\n",
        "print(\"Number of features : %d \\n\" %len(tfidf.get_feature_names())) #1722\n",
        "print(\"Show some feature names : \\n\", tfidf.get_feature_names()[::1000])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of features : 5000 \n",
            "\n",
            "Show some feature names : \n",
            " ['ability', 'day one', 'individual', 'pack play', 'squeeze']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4z_ImC_AN8F-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "knn = []\n",
        "svm = []\n",
        "dt =[]\n",
        "nb =[]\n",
        "lr =[]\n",
        "rus = RandomUnderSampler(random_state=777)\n",
        "X_RUS, y_RUS = rus.fit_sample(X_all_tfidf, y)\n",
        "target_names = ['Positive','Negative']\n",
        "skf = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
        "for train_index, test_index in skf.split(X_RUS, y_RUS):\n",
        "  X_train_countVect = X_RUS[train_index]\n",
        "  y_train = y_RUS[train_index]\n",
        "  X_test_countVect = X_RUS[test_index]\n",
        "  y_test = y_RUS[test_index]\n",
        "  \n",
        "  \n",
        "  knn_mean = knn_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  knn.append(knn_mean)\n",
        "  dt_mean = dt_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  dt.append(dt_mean)\n",
        "  nb_mean = nb_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  nb.append(nb_mean)\n",
        "  lr_mean = lr_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  lr.append(lr_mean)\n",
        "  svm_mean = svc_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names)\n",
        "  svm.append(svm_mean)\n",
        "  \n",
        "  \n",
        "  print('The Accuracy for KNN:',sum(knn)/len(knn))\n",
        "  print('The Accuracy for SVM:',sum(svm)/len(svm))\n",
        "  print('The Accuracy for DT:',sum(dt)/len(dt))\n",
        "  print('The Accuracy for MNB:',sum(nb)/len(nb))\n",
        "  print('The Accuracy for LR:',sum(lr)/len(lr))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "9gEceKB2FytS"
      },
      "cell_type": "markdown",
      "source": [
        "# **K-Nearest Neighbours**"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "tB-DTx6WFytP",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def knn_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target): \n",
        "    classifier=KNeighborsClassifier(n_neighbors=5)\n",
        "    classifier.fit(X_train_countVect,y_train)\n",
        "\n",
        "    y_pred=classifier.predict(X_test_countVect)\n",
        "\n",
        "    y_pred_train = classifier.predict(X_train_countVect)\n",
        "    print('KNN Results:')\n",
        "    print(\"KNN Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
        "    print(classification_report(y_test, y_pred))\n",
        "    print(\"Confusion Matrix\",confusion_matrix(y_test, y_pred))    \n",
        "    print(\"KNN Train Accuracy:\",metrics.accuracy_score(y_train,y_pred_train))\n",
        "    print(classification_report(y_train, y_pred_train))\n",
        "    \n",
        "    return metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Mu5w-hFSFytO"
      },
      "cell_type": "markdown",
      "source": [
        "# SVM"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "6j8v5PNAFytL",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#SVM\n",
        "def svc_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names): \n",
        "  from sklearn import svm\n",
        "  clf=svm.SVC(kernel='linear')\n",
        "  clf.fit(X_train_countVect,y_train)\n",
        "\n",
        "  y_pred=clf.predict(X_test_countVect)\n",
        "  \n",
        "  y_pred_train =clf.predict(X_train_countVect)\n",
        "\n",
        "# scores = cross_val_score(clf, iris.data, iris.target, cv=5)\n",
        "# print(\"scores\",scores.avg)\n",
        "  print('SVM Results:')\n",
        "  print(\"SVM Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
        "  print(classification_report(y_test, y_pred, target_names=target_names))\n",
        "  print(\"Confusion Matrix\",confusion_matrix(y_test, y_pred))    \n",
        "  print(\"SVM Train Accuracy:\",metrics.accuracy_score(y_train,y_pred_train))\n",
        "  print(classification_report(y_train, y_pred_train, target_names=target_names))\n",
        "\n",
        "  return metrics.accuracy_score(y_test,y_pred)\n",
        "  \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Wb9f9xhVFytJ"
      },
      "cell_type": "markdown",
      "source": [
        "# MultiNomial NB"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "QAaFPSLBFytD",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Naive Bayes classifier\n",
        "def nb_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names): \n",
        "\n",
        "  clf = MultinomialNB()\n",
        "  clf.fit(X_train_countVect.toarray(),y_train)\n",
        "\n",
        "  y_pred=clf.predict(X_test_countVect)\n",
        "\n",
        "  y_pred_train =clf.predict(X_train_countVect)\n",
        "  print('NB Results:')\n",
        "#   y_pred_train =clf.predict(countVect.transform(X_test_cleaned))\n",
        "  print(\"MNB Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
        "  print(classification_report(y_test, y_pred, target_names=target_names))\n",
        "  print(\"Confusion Matrix\",confusion_matrix(y_test, y_pred))    \n",
        "  print(\"MNB Train Accuracy:\",metrics.accuracy_score(y_train,y_pred_train))\n",
        "  print(classification_report(y_train, y_pred_train, target_names=target_names))\n",
        "\n",
        "  return metrics.accuracy_score(y_test,y_pred)\n",
        "  \n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "2i4xUBtNFytB"
      },
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "w_k-BZGrFys0",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Logistic Regression\n",
        "def lr_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names): \n",
        "  lr = LogisticRegression()\n",
        "  lr.fit(X_train_countVect.toarray(), y_train)\n",
        "\n",
        "\n",
        "  y_pred=lr.predict(X_test_countVect)\n",
        "\n",
        "  y_pred_train =lr.predict(X_train_countVect)\n",
        "  print('LR Results:')\n",
        "  #   y_pred_train =clf.predict(countVect.transform(X_test_cleaned))\n",
        "  print(\"LR Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
        "  print(classification_report(y_test, y_pred, target_names=target_names))\n",
        "  print(\"Confusion Matrix\",confusion_matrix(y_test, y_pred))    \n",
        "  print(\"LR Train Accuracy:\",metrics.accuracy_score(y_train,y_pred_train))\n",
        "  print(classification_report(y_train, y_pred_train, target_names=target_names))\n",
        "  \n",
        "  return metrics.accuracy_score(y_test,y_pred)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "yERFIF_pFysg"
      },
      "cell_type": "markdown",
      "source": [
        "# Adaboosting (Decision Trees)"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "N28PY3zrFysQ",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# #   Decision Trees\n",
        "def dt_classifier(X_train_countVect,y_train,X_test_countVect,y_test,target_names): \n",
        "  clf = AdaBoostClassifier(n_estimators=400,learning_rate=1)\n",
        "  clf.fit(X_train_countVect,y_train)\n",
        "  \n",
        "  y_pred=clf.predict(X_test_countVect)\n",
        "  \n",
        "  y_pred_train =clf.predict(X_train_countVect)\n",
        "\n",
        "\n",
        "  print('Adaboosting Results:')\n",
        "  print(\"Adaboosting DT Accuracy:\",metrics.accuracy_score(y_test,y_pred))\n",
        "  print(classification_report(y_test, y_pred, target_names=target_names))\n",
        "  print(\"Confusion Matrix\",confusion_matrix(y_test, y_pred))    \n",
        "  print(\"Adaboosting DT Train Accuracy:\",metrics.accuracy_score(y_train,y_pred_train))\n",
        "  print(classification_report(y_train, y_pred_train, target_names=target_names))\n",
        "  \n",
        "  return metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_3NBtGgtUdai",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# LSTM with GLOVE\n"
      ]
    },
    {
      "metadata": {
        "id": "WPJPu_uHGNeX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Split review text into parsed sentences uisng NLTK's punkt tokenizer\n",
        "# nltk.download()\n",
        "tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
        "\n",
        "def parseSent(review, tokenizer, remove_stopwords=False):\n",
        "    '''\n",
        "    Parse text into sentences\n",
        "    '''\n",
        "    raw_sentences = tokenizer.tokenize(review.strip())\n",
        "    sentences = []\n",
        "    for raw_sentence in raw_sentences:\n",
        "        if len(raw_sentence) > 0:\n",
        "            sentences.append(cleanText(raw_sentence, remove_stopwords, split_text=True))\n",
        "    return sentences\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L9EUSwHraAw3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_cleaned=np.array(X_cleaned)\n",
        "y=np.array(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3FYFtaMgXIPO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Parse each review in the training set into sentences\n",
        "sentences = []\n",
        "for review in X_cleaned:\n",
        "  sentences += parseSent(review, tokenizer)\n",
        "\n",
        "# creating a corpus object\n",
        "  corpus = Corpus() \n",
        "  #training the corpus to generate the co occurence matrix which is used in GloVe\n",
        "  corpus.fit(sentences, window=10)\n",
        "  #creating a Glove object which will use the matrix created in the above lines to create embeddings\n",
        "  #We can set the learning rate as it uses Gradient Descent and number of components\n",
        "  glove = Glove(no_components=300, learning_rate=0.05)\n",
        " \n",
        "  glove.fit(corpus.matrix, epochs=3, no_threads=4, verbose=True)\n",
        "  glove.add_dictionary(corpus.dictionary)\n",
        "  glove.save('glove.model') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9PkAF6J_X15b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        },
        "outputId": "6ce1e005-74f1-4f10-dd9f-4f195cba78ea"
      },
      "cell_type": "code",
      "source": [
        "w2v = glove.load(\"glove.model\")\n",
        "w2v1 = glove.load(\"glove.model\")\n",
        "print(\"Vector Representation for the word Perfect in Glove:\")\n",
        "np.array(w2v.word_vectors[w2v.dictionary['perfect']]).reshape(1,300)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vector Representation for the word Perfect in Glove:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.08495787, -0.11356045, -0.08846308,  0.08354033, -0.0115258 ,\n",
              "        -0.03018097,  0.07990233, -0.00188552, -0.11281774,  0.03257014,\n",
              "         0.07057219, -0.00595591,  0.11524554,  0.02520369, -0.07093215,\n",
              "         0.14729772, -0.01412138, -0.00369628,  0.01922959, -0.03325729,\n",
              "        -0.03140391, -0.0572636 ,  0.04783262,  0.01076807, -0.15400567,\n",
              "        -0.02406117,  0.06578213,  0.00753086, -0.00344713,  0.04389354,\n",
              "        -0.06150736,  0.0067762 ,  0.08667346, -0.11764054,  0.14076806,\n",
              "         0.09384767,  0.086179  , -0.00122824, -0.02913382, -0.05792346,\n",
              "        -0.04421396, -0.04560012, -0.08427738, -0.09541765, -0.06020362,\n",
              "         0.08060084,  0.06123696, -0.09709566,  0.02772153,  0.03982536,\n",
              "        -0.00617725, -0.00512047,  0.00824899,  0.16338191, -0.05168399,\n",
              "         0.0427997 ,  0.01913232, -0.01063455, -0.11044303, -0.0476534 ,\n",
              "        -0.08202095,  0.00708921,  0.0191874 ,  0.01301286, -0.05817096,\n",
              "        -0.08810799,  0.10116979, -0.0576546 , -0.10864431, -0.00602952,\n",
              "         0.07691038, -0.12199521, -0.09729996,  0.07328579,  0.1341358 ,\n",
              "         0.05436529,  0.05382075, -0.07966734,  0.02242786,  0.07823436,\n",
              "         0.05374293, -0.06072122,  0.09274616,  0.10131296,  0.05983077,\n",
              "        -0.07543787, -0.17400801, -0.1144765 , -0.01202818,  0.05589571,\n",
              "        -0.0351766 , -0.1199603 ,  0.01549555,  0.12668262,  0.09208219,\n",
              "         0.05812892, -0.03081628, -0.06132571, -0.00094242,  0.01493759,\n",
              "        -0.06430814,  0.09506181,  0.10426063, -0.04141519, -0.02535273,\n",
              "        -0.00385614, -0.02184896, -0.02679685,  0.06454064,  0.00009834,\n",
              "         0.07281791,  0.0340921 , -0.01923966,  0.00568722,  0.01422407,\n",
              "         0.14567906, -0.12814568,  0.04154506,  0.07646618, -0.06227118,\n",
              "         0.05941745, -0.15750522,  0.13452201, -0.00064719, -0.07732756,\n",
              "        -0.00608321, -0.16453516,  0.11381702,  0.02114348, -0.08491701,\n",
              "         0.08164106, -0.03867339, -0.04188666,  0.07881261, -0.07883699,\n",
              "         0.05557289,  0.07738791,  0.07459964,  0.05942873,  0.09041101,\n",
              "         0.10872776, -0.08741317, -0.07637907, -0.03598521, -0.01674722,\n",
              "        -0.02552683, -0.06106009, -0.00965149,  0.1640455 , -0.0281448 ,\n",
              "         0.07253194, -0.1017698 ,  0.00410215,  0.00481094,  0.01371653,\n",
              "         0.0604265 , -0.08814858, -0.01094417, -0.10097548,  0.04457728,\n",
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              "        -0.00996899, -0.12790395, -0.01296001,  0.04672574,  0.02574743,\n",
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              "        -0.12635211, -0.15470971, -0.09325192, -0.050905  , -0.01119344,\n",
              "         0.15984427,  0.05278545, -0.07894599, -0.06872066, -0.00563943,\n",
              "        -0.02022958,  0.06758478,  0.10502334,  0.06027486, -0.01074046,\n",
              "         0.10475947,  0.04214049, -0.14107825, -0.04429248, -0.06558572,\n",
              "         0.07609347, -0.06139643,  0.03436532,  0.00026942, -0.05287346,\n",
              "         0.02973242,  0.02644807, -0.06657326, -0.01336452,  0.03701419,\n",
              "        -0.10869403, -0.06396486,  0.05534331,  0.1343886 ,  0.05528033,\n",
              "         0.09826719,  0.05764429, -0.14399412, -0.01542028,  0.06540829,\n",
              "        -0.13146957, -0.08686295,  0.06495803,  0.02145579,  0.04146674,\n",
              "        -0.04546519,  0.14025407,  0.01750976, -0.00893816, -0.0182151 ,\n",
              "        -0.12115845, -0.0436103 , -0.03192356,  0.14549059, -0.07622021,\n",
              "        -0.05716625, -0.03864489,  0.02265301,  0.01628527, -0.13765797,\n",
              "        -0.1271794 , -0.04576446, -0.00029078,  0.08307643,  0.05060436,\n",
              "         0.04728554, -0.01457376, -0.11414284, -0.12640233, -0.07808301,\n",
              "        -0.19262051, -0.1317653 , -0.06311553, -0.00940945,  0.06749092,\n",
              "         0.10846131, -0.00951523, -0.0922785 ,  0.11222186, -0.00390419,\n",
              "        -0.00126781, -0.08687281,  0.01719663, -0.11827332, -0.01934179,\n",
              "         0.09029586,  0.04854163, -0.04733829, -0.06569705,  0.07467471,\n",
              "         0.1342368 ,  0.07928167, -0.02913346,  0.11253748,  0.04803264,\n",
              "         0.07721818, -0.04215057,  0.01230715, -0.07222262,  0.16490328,\n",
              "         0.04620197,  0.06282905,  0.09862075, -0.03423917,  0.08692986,\n",
              "        -0.02149758, -0.01044102, -0.03533524, -0.01120436,  0.09859021]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "gDoKLWLcTiD-",
        "colab_type": "code",
        "outputId": "49ce0f60-9ca0-40f8-ffe0-02921d1cc469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1142
        }
      },
      "cell_type": "code",
      "source": [
        "skf = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
        "\n",
        "\n",
        "maxlen = 100\n",
        "batch_size = 64\n",
        "nb_classes = 2\n",
        "nb_epoch = 3\n",
        "\n",
        "\n",
        "num_features = 300  #embedding dimension                     \n",
        "min_word_count = 10                \n",
        "num_workers = 4       \n",
        "context = 10                                                                                          \n",
        "downsampling = 1e-3 \n",
        "\n",
        "\n",
        "\n",
        "for train_index, test_index in skf.split(X_cleaned,y):\n",
        "  X_train = X_cleaned[train_index]\n",
        "  y_train = y[train_index]\n",
        "  X_test = X_cleaned[test_index]\n",
        "  y_test = y[test_index]\n",
        "  \n",
        "  # Split review text into parsed sentences uisng NLTK's punkt tokenizer\n",
        "  tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
        "\n",
        "\n",
        "  # Parse each review in the training set into sentences\n",
        "  sentences = []\n",
        "  for review in X_train:\n",
        "    sentences += parseSent(review, tokenizer)\n",
        " \n",
        "\n",
        "  print(\"Training glove model ...\\n\")\n",
        "  # creating a corpus object\n",
        "  corpus = Corpus() \n",
        "  #training the corpus to generate the co occurence matrix which is used in GloVe\n",
        "  corpus.fit(sentences, window=10)\n",
        "  #creating a Glove object which will use the matrix created in the above lines to create embeddings\n",
        "  #We can set the learning rate as it uses Gradient Descent and number of components\n",
        "  glove = Glove(no_components=300, learning_rate=0.05)\n",
        " \n",
        "  glove.fit(corpus.matrix, epochs=5, no_threads=4, verbose=True)\n",
        "  glove.add_dictionary(corpus.dictionary)\n",
        "  glove.save('glove.model')\n",
        "  w2v = glove.load(\"glove.model\")\n",
        "\n",
        "\n",
        "#   # Get glove embedding matrix\n",
        "  embedding_matrix = w2v.word_vectors  # embedding matrix, type = numpy.ndarray \n",
        "  top_words = w2v.word_vectors.shape[0] #4016\n",
        "\n",
        "    \n",
        "#   # Vectorize X_train and X_test to 2D tensor\n",
        "  \n",
        "  tokenizer = Tokenizer(nb_words=top_words) #only consider top 20000 words in the corpse\n",
        "  tokenizer.fit_on_texts(X_train)\n",
        "  tokenizer.word_index #access word-to-index dictionary of trained tokenizer\n",
        "\n",
        "  sequences_train = tokenizer.texts_to_sequences(X_train)\n",
        "  sequences_test = tokenizer.texts_to_sequences(X_test)\n",
        "\n",
        "  X_train_seq = sequence.pad_sequences(sequences_train, maxlen=maxlen)\n",
        "  X_test_seq = sequence.pad_sequences(sequences_test, maxlen=maxlen)\n",
        "\n",
        "\n",
        "   # one-hot encoding of y_train and y_test\n",
        "  y_train_seq = np_utils.to_categorical(y_train, nb_classes)\n",
        "  y_test_seq = np_utils.to_categorical(y_test, nb_classes)\n",
        "\n",
        "  print('X_train shape:', X_train_seq.shape) #(27799, 100)\n",
        "  print('X_test shape:', X_test_seq.shape) #(3089, 100)\n",
        "  print('y_train shape:', y_train_seq.shape) #(27799, 2)\n",
        "  print('y_test shape:', y_test_seq.shape) #(3089, 2)\n",
        "\n",
        "  # Construct glove embedding layer\n",
        "  embedding_layer = Embedding(embedding_matrix.shape[0], #4016\n",
        "                            embedding_matrix.shape[1], #300\n",
        "                            weights=[embedding_matrix])\n",
        "  # Construct LSTM with Word2Vec embedding\n",
        "  model2 = Sequential()\n",
        "  model2.add(embedding_layer)\n",
        "  model2.add(LSTM(200, dropout_W=0.25, dropout_U=0.25))\n",
        "  model2.add(Dense(nb_classes))\n",
        "  model2.add(Activation('relu'))\n",
        "  model2.summary()\n",
        "\n",
        "  # Compile model\n",
        "  model2.compile(loss='binary_crossentropy',\n",
        "              optimizer='nadam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  model2.fit(X_train_seq, y_train_seq, batch_size=batch_size, nb_epoch=3, verbose=1)\n",
        "\n",
        "\n",
        "  # Model evaluation\n",
        "  score = model2.evaluate(X_test_seq, y_test_seq, batch_size=batch_size)\n",
        "  nb.append(score)\n",
        "  print('Test accuracy : {:.4f}'.format(score[1]))\n",
        "\n",
        "  print('Test loss : {:.4f}'.format(score[0]))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training glove model ...\n",
            "\n",
            "Performing 5 training epochs with 4 threads\n",
            "Epoch 0\n",
            "Epoch 1\n",
            "Epoch 2\n",
            "Epoch 3\n",
            "Epoch 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/text.py:178: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
            "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_train shape: (27090, 100)\n",
            "X_test shape: (6774, 100)\n",
            "y_train shape: (27090, 2)\n",
            "y_test shape: (6774, 2)\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:83: UserWarning: Update your `LSTM` call to the Keras 2 API: `LSTM(200, dropout=0.25, recurrent_dropout=0.25)`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, None, 300)         7922400   \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 200)               400800    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 402       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 2)                 0         \n",
            "=================================================================\n",
            "Total params: 8,323,602\n",
            "Trainable params: 8,323,602\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:96: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "27090/27090 [==============================] - 353s 13ms/step - loss: 0.6219 - acc: 0.7311\n",
            "Epoch 2/3\n",
            " 1600/27090 [>.............................] - ETA: 5:26 - loss: 0.3531 - acc: 0.8738"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "EdZwiPLgsjcH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#LSTM with Word2vec\n"
      ]
    },
    {
      "metadata": {
        "id": "aY9Gp1KdIZgW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "13028f24-f5e3-4b61-ea90-18ef134e2b51"
      },
      "cell_type": "code",
      "source": [
        "import logging\n",
        "from gensim.models import word2vec\n",
        "from gensim.models import Word2Vec\n",
        "from gensim.models.keyedvectors import KeyedVectors\n",
        "\n",
        "\n",
        "# Fit parsed sentences to Word2Vec model \n",
        "# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',level=logging.INFO)\n",
        "\n",
        "num_features = 300  #embedding dimension                     \n",
        "min_word_count = 10                \n",
        "num_workers = 4       \n",
        "context = 10                                                                                          \n",
        "downsampling = 1e-3 \n",
        "\n",
        "print(\"Training Word2Vec model ...\\n\")\n",
        "w2v = Word2Vec(sentences, workers=num_workers, size=num_features, min_count = min_word_count,\\\n",
        "                 window = context, sample = downsampling)\n",
        "w2v.init_sims(replace=True)\n",
        "w2v.save(\"w2v_300features_10minwordcounts_10context\") #save trained word2vec model\n",
        "\n",
        "print(\"Number of words in the vocabulary list : %d \\n\" %len(w2v.wv.index2word)) #4016 \n",
        "print(\"Show first 10 words in the vocalbulary list  vocabulary list: \\n\", w2v.wv.index2word[0:10])"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Word2Vec model ...\n",
            "\n",
            "Number of words in the vocabulary list : 6760 \n",
            "\n",
            "Show first 10 words in the vocalbulary list  vocabulary list: \n",
            " ['not', 'baby', 'one', 'would', 'like', 'use', 'get', 'great', 'really', 'little']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wi6sipyl048u",
        "colab_type": "code",
        "outputId": "a4a9f935-5d3c-4853-83b6-2fe7ad238561",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "# Load trained Word2Vec model\n",
        "w2v = Word2Vec.load(\"w2v_300features_10minwordcounts_10context\")\n",
        "\n",
        "\n",
        "# Get Word2Vec embedding matrix\n",
        "embedding_matrix = w2v.wv.syn0  # embedding matrix, type = numpy.ndarray \n",
        "print(\"Shape of embedding matrix : \", embedding_matrix.shape) #(4016, 300) = (volcabulary size, embedding dimension)\n",
        "# w2v.wv.syn0[0] #feature vector of the first word in the volcabulary list\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of embedding matrix :  (6760, 300)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: DeprecationWarning: Call to deprecated `syn0` (Attribute will be removed in 4.0.0, use self.wv.vectors instead).\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "tz6ezIvQA-Ps",
        "colab_type": "code",
        "outputId": "0e5e343e-9488-4e9c-c0eb-7a02617638ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "cell_type": "code",
      "source": [
        "top_words = embedding_matrix.shape[0] #4016\n",
        "maxlen = 100  \n",
        "batch_size = 32\n",
        "nb_classes = 2\n",
        "nb_epoch = 3\n",
        "\n",
        "\n",
        "# Vectorize X_train and X_test to 2D tensor\n",
        "tokenizer = Tokenizer(nb_words=top_words) #only consider top 20000 words in the corpse\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "# tokenizer.word_index #access word-to-index dictionary of trained tokenizer\n",
        "\n",
        "sequences_train = tokenizer.texts_to_sequences(X_train)\n",
        "sequences_test = tokenizer.texts_to_sequences(X_test)\n",
        "\n",
        "X_train_seq = sequence.pad_sequences(sequences_train, maxlen=maxlen)\n",
        "X_test_seq = sequence.pad_sequences(sequences_test, maxlen=maxlen)\n",
        "\n",
        "\n",
        "# one-hot encoding of y_train and y_test\n",
        "y_train_seq = np_utils.to_categorical(y_train, nb_classes)\n",
        "y_test_seq = np_utils.to_categorical(y_test, nb_classes)\n",
        "\n",
        "print('X_train shape:', X_train_seq.shape) #(27799, 100)\n",
        "print('X_test shape:', X_test_seq.shape) #(3089, 100)\n",
        "print('y_train shape:', y_train_seq.shape) #(27799, 2)\n",
        "print('y_test shape:', y_test_seq.shape) #(3089, 2)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/text.py:178: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
            "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "X_train shape: (27090, 100)\n",
            "X_test shape: (6774, 100)\n",
            "y_train shape: (27090, 2)\n",
            "y_test shape: (6774, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FJ6zI9gg9hF9",
        "colab_type": "code",
        "outputId": "6bad595a-5d37-4037-cee1-6137abdf4765",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Construct Word2Vec embedding layer\n",
        "embedding_layer = Embedding(embedding_matrix.shape[0], #4016\n",
        "                            embedding_matrix.shape[1], #300\n",
        "                            weights=[embedding_matrix])\n",
        "\n",
        "\n",
        "# Construct LSTM with Word2Vec embedding\n",
        "model2 = Sequential()\n",
        "model2.add(embedding_layer)\n",
        "model2.add(LSTM(128, dropout_W=0.2, dropout_U=0.2)) \n",
        "model2.add(Dense(nb_classes))\n",
        "model2.add(Activation('softmax'))\n",
        "model2.summary()\n",
        "\n",
        "# Compile model\n",
        "model2.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model2.fit(X_train_seq, y_train_seq, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1)\n",
        "\n",
        "\n",
        "# Model evaluation\n",
        "score = model2.evaluate(X_test_seq, y_test_seq, batch_size=batch_size)\n",
        "print('Test loss : {:.4f}'.format(score[0]))\n",
        "print('Test accuracy : {:.4f}'.format(score[1]))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `LSTM` call to the Keras 2 API: `LSTM(128, dropout=0.2, recurrent_dropout=0.2)`\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, None, 300)         2028000   \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 128)               219648    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 2)                 258       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 2)                 0         \n",
            "=================================================================\n",
            "Total params: 2,247,906\n",
            "Trainable params: 2,247,906\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            " 5088/27090 [====>.........................] - ETA: 4:18 - loss: 0.5206 - acc: 0.7366"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-82a265b59a1d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m               metrics=['accuracy'])\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mmodel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_seq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnb_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "2C-852aV6ABq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ]
    },
    {
      "metadata": {
        "id": "sS0wsjfKEEFY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# T-SNE with Glove"
      ]
    },
    {
      "metadata": {
        "id": "BTzMQOdsG6lW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def tsnescatterplot(model, word, list_names):\n",
        "    \"\"\" Plot in seaborn the results from the t-SNE dimensionality reduction algorithm of the vectors of a query word,\n",
        "    its list of most similar words, and a list of words.\n",
        "    \"\"\"\n",
        "    arrays = np.empty((0, 300), dtype='f')\n",
        "    word_labels = [word]\n",
        "    color_list  = ['red']\n",
        "\n",
        "    # adds the vector of the query word\n",
        "    arrays = np.append(arrays, np.array(model.word_vectors[model.dictionary[word]]).reshape(1,300), axis=0)\n",
        "    \n",
        "    # gets list of most similar words\n",
        "    close_words = model.most_similar(word)\n",
        "    \n",
        "    # adds the vector for each of the closest words to the array\n",
        "    for wrd_score in close_words:\n",
        "        wrd_vector = np.array(model.word_vectors[model.dictionary[wrd_score[0]]]).reshape(1,300)\n",
        "        word_labels.append(wrd_score[0])\n",
        "        color_list.append('blue')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "    # adds the vector for each of the words from list_names to the array\n",
        "    for wrd in list_names:\n",
        "        wrd_vector = np.array(model.word_vectors[model.dictionary[wrd]]).reshape(1,300)\n",
        "        word_labels.append(wrd)\n",
        "        color_list.append('green')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "        \n",
        "    # Reduces the dimensionality from 300 to 50 dimensions with PCA\n",
        "    reduc = PCA(n_components=10).fit_transform(arrays)\n",
        "    \n",
        "    # Finds t-SNE coordinates for 2 dimensions\n",
        "    np.set_printoptions(suppress=True)\n",
        "    \n",
        "    Y = TSNE(n_components=2, random_state=0, perplexity=15).fit_transform(reduc)\n",
        "    \n",
        "    # Sets everything up to plot\n",
        "    df = pd.DataFrame({'x': [x for x in Y[:, 0]],\n",
        "                       'y': [y for y in Y[:, 1]],\n",
        "                       'words': word_labels,\n",
        "                       'color': color_list})\n",
        "    fig, _ = plt.subplots()\n",
        "    fig.set_size_inches(9, 9)\n",
        "    \n",
        "    # Basic plot\n",
        "    p1 = sns.regplot(data=df,\n",
        "                     x=\"x\",\n",
        "                     y=\"y\",\n",
        "                     fit_reg=False,\n",
        "                     marker=\"o\",\n",
        "                     scatter_kws={'s': 40,\n",
        "                                  'facecolors': df['color']\n",
        "                                 }\n",
        "                    )\n",
        "    \n",
        "    # Adds annotations one by one with a loop\n",
        "    for line in range(0, df.shape[0]):\n",
        "         p1.text(df[\"x\"][line],\n",
        "                 df['y'][line],\n",
        "                 '  ' + df[\"words\"][line].title(),\n",
        "                 horizontalalignment='left',\n",
        "                 verticalalignment='bottom', size='medium',\n",
        "                 color=df['color'][line],\n",
        "                 weight='normal'\n",
        "                ).set_size(15)\n",
        "\n",
        "    \n",
        "    plt.xlim(Y[:, 0].min()-50, Y[:, 0].max()+50)\n",
        "    plt.ylim(Y[:, 1].min()-50, Y[:, 1].max()+50)\n",
        "            \n",
        "    plt.title('t-SNE visualization for {}'.format(word.title()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6KjtPIujG_Ax",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "d0885e4d-3c8b-414f-c6a5-acfab39c49a3"
      },
      "cell_type": "code",
      "source": [
        "print(\"Glove Model Represenation of Love:\")\n",
        "tsnescatterplot(w2v1, 'love', ['money', 'excellent', 'cheap', 'perfect', 'easy', 'affordable', 'experience'])"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Glove Model Represenation of Love:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iqxPoG0QpS5RrOVREij8FLhEpskauGc7INcMBMDC4rVo/hjV+BoDVB1cRExhL49JNsTls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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5V_MpSvLHbdm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "5fdfa537-7b35-4bf6-9567-8f6e822985c0"
      },
      "cell_type": "code",
      "source": [
        "print(\"Glove Model Represenation of Hate:\")\n",
        "tsnescatterplot(w2v1, 'hate', ['horrible','dislike','pathetic','poor','quality','disgusting','awful'])"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Glove Model Represenation of Hate:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wFvdczic3fM41lVsxauUjfPDbuy718dYZAs6FciTnMK+sH8++E3ud2htENWRxz+VUD6tx\n0c4tImVncXcBIiL/VTW0KkmVkgFoFd+Grce2Mv23afSvP8jNlZUuxDfUUbuIuJ9mcETEYzWMSnSa\nKXnux6dpM6s51afG0nBGHQZ/3Z+07DTH9iYf1SMjN4NX1o93XO7639kcq83KuJ+e5ar3a1D3g5qM\nXDmCPGue0zn3n9jHwGV9uHJaVapNjeG2BT3YcWw7AHuP76HNp80BuGneDY5zQMmXqKw2K6///CrN\nP2lE/DuRNJxRh2ErBl/4gRKRYjSDIyIea9/xvUQFRjs+H8k5zINNHqFSYCxHc48w+ZdJ3DyvKytv\nX4PJMDH9+k+4aV5Xul3RnTuvugeA2hXqOI5/e9ObtKzcmrc6vMsfR39n3E/PEB9SlWGNHgLgWG4G\n3eZ0IsK/Ai+3mUiAJYA3Nkzglvk38uOdG4gJqsTbHd5jyPIBjG/9Kg0iG561/ke/e5DPts7kgUYP\nkhLXkszcYyzcNf8ijJSI/JMCjoh4DJvdRqGtkNzCHBbtWsDCXfMY2OB+x/bX2092/Npqs5IUk0zD\nD+uw5uCPpMRdQ/2ohlhMZmKD4kq8XFQlpCqTrn0HgPZVO7D20E8s3jXfEXCmbHqL7IJT/Oe274nw\nrwBAcqXmNPm4Pv/+8yP61x9I3Yr1AKgdUeesl6S2H9vGJ39+yLiWL3JfgyGO9h4JN5/HCIlIWSng\niIjHGP39SEZ/PxIAA4PbavfmseQnHNtX7FnGq+tfYuuxLZzIP+5o35m5g5S4a0rtv22V9k6fa0fU\nYVP6Rsfn7/Z/S5sq7QjxDaXQVghAsG8IDaMS2XR4I674PnUlALfXudOl40TkwlDAERGPMTTxQbrX\nugl/SwDVQqsTYAlwbNuY9jN3f3U7XWp0Y3jjEUQGRGIYBp2/vJY8a26Z+g/zC3P67GPycTo2I/co\nP6etY+6O2cWObRXf1qXvciw3g0BLECG+oS4dJyIXhgKOiHiM+JB4EqMbl7ht8V8LqegfybvXTccw\nDIBit2qfrwi/CGpX78KIpMeLbQv2CXGtL/8KZBee4kT+cYUcETdQwBGRciG3MAcfk48j3AB8ue2z\nYvv5mHyL3RlVVq3i2zJvx2xqV7jKafbof/mafQBKnTVqVbkNAJ9tnVkubnMX8TYKOCJyURRabWAY\nWExG6TuXQZsq7ZiyeTJPfj+S66p3Zt2hNXyx7dNi+yVEXMnyPUtpX7UDQT5B1ApPINi3bLMvgxs+\nwBfbPqXnvK4MqD+I2KA4Duek88OB72kWm0LPhFupHFyFAEsAn275NyG+ofiYfEqcdaoVkcDddfvy\n9OrRHM45TErsNRzPz2LBzrlMvW76+Q6HiJRCAUdELqj9mTlM/fFPNqYewTCgebVKDGxem8hgv/Pq\nt0O1TjyVMpZpm6fw8R8zaFIpmU+6fEbzfzuHi6dTnmPUqke4c9GtZBdmM6f7Iq6p3KpM56gYUJGv\nbl7B82vG8tTqJziel0VMUCWSKzV33D3lb/Hn1bZv8Mq68fSY24UCWwHp9x8vsb+XWr9GlZAqfPzn\nh0zaMIHIgKhiC51F5OIw7Ha73d1FXEwFBVYyM7MJDw8kMzPb3eWUKxoz12i8IDO7gCFfruKgbTaW\ngD/AbsKak0h1/xuZfPM1BPiYnfbXmLlG4+U6jZnrPH3MoqLKNiOrJxmLyAWzbNshDlu/xyfwVwzD\nimEqwBK0jgO5G/h+11F3lycilxEFHBG5YH4/lE6heVex9lxjK1vSFXBE5NJRwBGRCyY+LBTDGlWs\n3cceS1yoa7dZi4icDwUcEblgOl8VT5i9Hdb80++PKsyrTBjNaZcQfZYjRUQuLN1FJSIXTHx4AP/X\nPoUJK/05eeogYCLCN46RnRpRIdDX3eWJyGVEAUdELqjkahF8dEdbdh7NxmwY1IwMxGRcmGfhiIiU\nlQKOiFxwFrOJ2tHB7i5DRC5jWoMjIiIiXkcBR0RERLyOLlGJiMglFR1d8iMD5s/Ppnlz6yWu5sz6\n9fMnI8Ng7twcd5ci50ABR0RELrkhQ/Lp1q3Aqa1OHZubqhFvpIAjIiKXXNWqNpKSFGjk4tEaHBER\n8TirVpm5/vpAqlQJpm7dIB5/3I+TJ09vnzXLQnR0iFMbQJMmQTz99Ok313foYKJfP3++/NJCcnIQ\nNWsGc/vtARw44PzogtRUg969A6haNZgmTYL4+GOfi/n15BLQDI6IiFxyNhsUFp7+bBhg/vtl81u2\nmLj99gDatLHy/vs5pKaa+Ne//Nizx8Snn7q+HmbDBjOHDpl49tk8cnNh9Gg/HnnEn5kzi/qy2+Ge\newLIyDCYMCEXPz94+WVfjh0zqFlTs0zllQKOiIhccqNH+zN6tL/jc3JyIQsXFgWO117zJT7ezkcf\n5fwdeqxERNi5774A1q0z0bSpa6HjxAmDf//7FOHhRZ/T0w2eesqfnBwICIAVK8z8+quZr746RZMm\nRX03bGj9e8bnQnxbcQcFHBERueSGDs2ne/fTi4yDg+2OX2/YYKZbt0LHjA5A166FWCx21qwxuxxw\nEhOtjnADcOWVRccfPGhQs6adDRvMREXZHOEGoEoVOw0bavamPFPAERGRSy4+3kZiYskBIj3dICrK\neZvZDBERdjIzXX/tR1iY3emz79+vRcvLMwA76ekGkZH2YsdFRtqLrfGR8kOLjEVExKNER9s5csQ5\nyFitcOyYQXh4URDx+3sdcYHznebnFIBKOh9QYpuUHwo4IiLiUZo0sbJ4sQ/W/3nm36JFFgoLDZo1\nK2qMjS0KOtu2nb6O9fPPJk6ccD2UNGpk5fBhEz//fPqPxP37DTZv1h+R5ZkuUYmIyHk5mVeIxWTg\n72MufecyePjhfK69NpB77w2gT598Dhww8dxzfrRrV+hYf9O4sZXYWBujR/sxalQex44ZvPmmLyEh\nxS81laZDBytXX21lwIAAnnoqD1/foruoSrpsJeWHAo6IiJyT7YdP8ubXqexMy8Mw2WlaM5j728UT\nGexX+sFnUaeOjZkzc3j+eT/69g0gJMTOTTcVMGZMnmMfX1+YPj2HkSP96d8/gCuusPHSS3ncf7//\nWXoumWHARx/l8Mgj/jz0kD+RkXYefDCf774zk5Ghy1TllWG32706ohYUWMnMzCY8PJDMzGx3l1Ou\naMxco/FyncbMNZ40XoeO5zL0w+0c/f4KLEcrgsmGNf4gNVoe4p276+Br8YzLO540ZuWFp49ZVFTJ\n7zL7J7f+BD7xxBOkpKTQtWtXR1tmZiZ9+/bluuuuo2/fvmRlZQFgt9v517/+RceOHenWrRu///67\nu8oWEbnszd90mIxNsfgcjcTAwLCZseyN5+COQNbsOebu8kTcG3B69uzJe++959Q2depUUlJSWLZs\nGSkpKUydOhWAlStXsnv3bpYtW8Zzzz3HM88844aKRUQEYGtqLmSEFWvP3h/GrsN6+7a4n1sDTtOm\nTQkLc/4fZMWKFfTo0QOAHj16sHz5cqd2wzBITEzk+PHjpKenX/KaRUQEqkb5Qmjxh8T4R58kLuL8\n1uCIXAget8j46NGjREdHAxAVFcXRo0cBSEtLo1KlSo79KlWqRFpammPfMzGbDcLDAzGbTYSHB168\nwr2Qxsw1Gi/Xacxc40njdWerKnz725+cOBGG5WQoduwURh6mUu3jdEm8giA/z/jjxZPGrLzwljHz\njJ/AMzAMA8M4vxXsVqtdi4zPkcbMNRov12nMXONJ4xXpa+KJHnG8EfAHWRkWMNu4ItbE411qUJCT\nT2ZOvrtLBDxrzMoLTx+zsi4y9riAU7FiRdLT04mOjiY9PZ0KFSoAEBMTw6FDhxz7HTp0iJiYGHeV\nKSJy2UuuFsGMAeHsO5aDj8UgLtT/vP9SKnKheMZ9fP+jffv2zJ07F4C5c+dy7bXXOrXb7XZ++eUX\nQkJCSr08JSIiF5fZZFC9YiCVwwIUbsSjuHUGZ8SIEaxdu5Zjx47RunVrhg0bxsCBA3nooYf44osv\niIuLY+LEiQC0adOG7777jo4dOxIQEMDzzz/vztJFRETEg+lBf3JGGjPXaLxcpzFzjcbLdRoz13n6\nmJWLB/2JiIiIXAwKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIXWFR0KP7TphRrN+3dQ1R0KL7LvnKpP795s/Gb9cmFKk9E5LKggCPi4fzm\nzcFfAUdExCUKOCIiIuJ1FHBE3Mjv038T3vU6Kl5ZlYoJVQm76QYsv2xwbA8ZNhi/hfPw/eF7oqJD\niYoOJfCl5x3bfb9aRHjHNkRWiaLi1bUIevYpKChwx1cREfEoFncXIOKNDJsNCgudG63WYvuZ9+0l\n97beWKvXwCjIx2/2F4TfeD0ZK9dgq16DUyMex5S6HyMri5MvvgqALa4yULQ2J2RQP3Lv6cup/xuD\nefdfBI17Fmw2Tj077qJ/RxERT6aAI3IRBI8eSfDokaXul/3oqNMfbDby27THsvFn/L/4lOxHR2Gr\nURN7eATYbBQmJZ/e124n6NmnyLutNydfmgBAAYCfH8GjHiH7wRHYK1S8sF9KRKQcUcARuQiyhz5I\nXvebnNpMaWmE3d3Lqc28bStB457FZ90aTEcOO9oLd+44a//mnTsw79/Hye43Oc0U5bdsjZGbi2XL\nnxS0aHkBvomISPmkgCNyEVjj4ylMbOzUZtq7x+mzcfIEYbf1wBYVzcmxz2ONrwr+foQ8PAwjL++s\n/RtHjwIQ1vuWErebUvefR/UiIuWfAo6Im1jWrcV8IJWsz+dhTbjS0W6cOF7qsfaICABOvPoGhfUb\nFNturVrtwhUqIlIOKeCIuImRmwuA3dfX0WZZuwbz3j0UNkh0tNl9fTDl5Toda62VgDU2DtO+vRTe\n3eeS1CsiUp4o4Ii4SUGTptiCggkZMZzsBx7EfPAAgS+/gDU2zmk/a60r8VuyGN/FC7HFxWGrFIut\nUiynnh1HyNCBmE4cJ//ajth9fDHv2Y3vVws5Pu0jCAx00zcTEXE/BRyRM7DZ7Xyz9TAr123jeHY+\nCdWi6dbsCqpEBFyQ/u3R0RyfNoPgZ54k7N7eWGtcwcmXJxDw5utO++X0vQ/Lr5sJeeh+TJmZnHp0\nFNmP/x95PW7GHhJC4MRX8Z/5MXaTGVu16uRd1wn+Z1ZIRORyZNjtdru7i7iYCgqsZGZmEx4eSGZm\ntrvLKVcu9zGbsXIbB5av4q7flxOdd4I1FWvwWdNuPHZHS6pVKD47crmP17nQmLlG4+U6jZnrPH3M\noqJCyrSfZnBESpB+Io/167cxdc1MAmxFTwa+8cBmLGtszK9RiWE3FF/YKyIinkOvahApwfbDp2h4\naIcj3PxXytGdbNlz+AxHiYiIp1DAESlBkK+ZI0FhxdqP+AYT7K+JTxERT6eAI1KCerEhHKpckzUR\n1R1t+YaZT65qR8smtdxXmIiIlIn+KipSAovZxLAeSbxht7No33aiTxzl57g6JDRIoHP9uNI7EBER\nt1LAETmDKyKDeKVfa35JbciJ3ELaRAVfsFvERUTk4lLAETkLH7OJplUj3F2GiIi4SGtwRERExOso\n4IiIiIjXUcARERERr6OAIyIiIl5HAUdERES8jgKOiIiIeB0FHBEREfE6HvscnPbt2xMUFITJZMJs\nNjN79mwyMzN5+OGHSU1NpXLlykycOJGwsOLvCxIREZHLm0fP4MyYMYN58+Yxe/ZsAKZOnUpKSgrL\nli0jJSWFqVOnurlCERER8UQeHXD+acWKFfTo0QOAHj16sHz5cjdXJCIiIp7IYy9RAfTv3x/DMOjV\nqxe9evXi6NGjREdHAxAVFcXRo0dL7cNsNggPD8RsNhEeHnixS/YqGjPXaLxcV97H7MMPDQYMMNG7\nt40ZM+wuH//55wY5OXDPPWVcAIsJAAAgAElEQVQ79lzGy9fXzMSJNu6/v+gcHTqYqFgRPv3U5nK9\n5VF5/xlzB28ZM48NODNnziQmJoajR4/St29fatas6bTdMAwMwyi1H6vVTmZmNuHhgWRmZl+scr2S\nxsw1Gi/Xlfcx+/jjAMDE/PkGBw+eIsDFd7HOnOlPRobBjTfmlGn/cxuvEHJy8snMLACgsDCAggI7\nmZm5LvZTPpX3nzF38PQxi4oKKdN+HnuJKiYmBoCKFSvSsWNHNm/eTMWKFUlPTwcgPT2dChUquLNE\nEbmMHT5ssGqVmVatCjl1ymDZMo/9+6LIZckjA052djYnT550/Hr16tUkJCTQvn175s6dC8DcuXO5\n9tpr3VmmiFzG5s+3YLUajB+fR2ysjdmzSw44H33kQ5s2gVSpEkzdukH06+fP8eMwbJg/Cxf68MMP\nFqKjQ4iODuGll3wBaNIkiKef9nPqZ9YsC76+Zv7+rZFTp2DUKD9SUoKoVi2YpKQgRo7048SJsn+H\nrVtNREeHsHq12an95EmoXj2YqVN9yt6ZiIfxyL9yHD16lKFDhwJgtVrp2rUrrVu3pn79+jz00EN8\n8cUXxMXFMXHiRDdXKiKXqzlzLNSvbyUhwUb37oV88IEPx49DaOjpfV57zZcXX/Slb98Cnn46j+xs\ng+XLzZw6ZTBiRB6pqQZZWQYvvlh0uSguruzreHJyDKxW+L//y6NiRTupqQYTJ/rSv38An31Wtkte\ntWvbaNLEyqxZPlxzjdXRvmCBhcJCuOWWgjLXI+JpPDLgVKlShfnz5xdrj4iIYMaMGW6oSETktP37\nDdatM/Pkk/kA9OxZwDvv+LJokYXevQsByMqC11/3ZeDAAp57Ls9xbNeuhY5fh4fbsdkgKcn1Bb+R\nkXZefvl0v4WFULWqnW7dAtm/3yA+vmxh6c47C3jyST9eeAGCg4vaZs704brrCtEqACnPPPISlYiI\nJ5szp+jSTY8eRTMciYk2atSwMXv26Us669ebyckx6N374s2CfPaZhfbtA6lePZi4uBC6dSu682Xn\nzrL/1v7f77BgQdHfd//6y2DNGvNFrVvkUlDAERFx0Zw5Fho0sBEaaicrq2i2plOnQr7/3kx6etHd\nnRkZRf+OiXH99vGyWLTIwgMPBJCUZGXatBy++uoU06cXXZrKyyvl4P8RHAzduxcyc2ZROPv0Ux+i\no+20b28t5UgRz+aRl6hERDzV9u0mfvutaFFuQkLx21UXLLDQv38BFSoUBZu0NIOKFV0LOX5+dgr+\nMYGSmen8WIwFCyw0aWLlpZdOp5kffij90RklufPOfLp1C2TXLoPPPvPhttsKMJtLP07EkyngiIi4\nYPZsC2aznY8+yin23JvRo/2YPduH/v0LSEqyEhBg59NPfXj22ZKnVHx9IS+veCiJjbWzbZvzBPu3\n3zr/dp2TA76+zsHpyy/P7bf05GQbtWrZePBBf/bvN3H77YWlHyTi4RRwROSyZrPb2X74FAVWG7Ui\ng/D3OfvUxZw5PrRpY6VDh+KXcHr1KuCZZ/zYt8+gShU7I0bk8/zzvhQUQIcOheTlFd1F9eij+cTG\n2qlVy8aSJRYWL7YQF2ejUiU7lSrZ6dKlkP/7Pz8mTvQlMdHKwoUWtm51Djxt2lgZNcqfCRN8adzY\nyvLlFlauPPff0u+4o4Bnn/UnKanozjCR8k5rcETksrU1/SR93/+DR987wBPvHuauqb/zn61Hzrj/\npk0mdu0yceutJS/A7dmzEJPp9CLkBx/M56WX8li50sw99wTw6KN+ZGUZBAcXzbz07VtAmzaFPPSQ\nP9ddF8SHHxYdd889BQwcWMC77/pw330B+PnBww/nO53r3nsLGDIkn3ff9aFv3wD27zd4552y3R5e\nks6di2Zt7rhDi4vFOxh2u/3irIDzEAUFVr2q4RxpzFyj8XKdO8fsZF4h/d7/k8P/uRJLZgQANv8c\nAlv/xsv3VKVOTNkeB38pXczxev99H8aO9ePXX08S4nlf/Zzp/0vXefqYlftXNYiIXEyrd2WQuT3C\nEW4ATLkBnNhchfkbS3+Rr7fYu9fgm2/MvP66L7ffXuBV4UYubwo4InJZOnKygNyM4m9MNrIDOHTs\n8rlM8/LLftx1VwAJCTZGjXLh/vLLTHq6wZNP+tG0aRDx8cEkJARz660BjucHiefRfxkRuSxdERVA\ncLVj5KdWxuD0nUxG1DHqVXPxteDl2KRJuUya5O4qPNuOHQY33RRIYCDcf38+tWvbOHECli+3MGSI\nPzVqZFOvnhZmexoFHBG5LCVVCafGlWn8eXg3xl/xGFYzhTHpxDRMo1uDK91dnniQIUMCiIiws2hR\nttMlvE6drPTpU0BY2PktZc3NBX//8yxSitElKhG5LFnMJl64uRY9bskl9Ib1+Hf9idY3H+bl22sR\nFexXegdyWfjxRzObNpkZPTqvxPVJV19tc7z3a906E3ffHUC9ekFUrx5Mu3aBfPGF8zzCrFlFb4/f\nsMFEjx4BVK0azFtvFb1FPjcXnn3Wj8TEostgbdsGsny5nrh4rjSDIyKXrVB/H4a3r8awdlWxAybj\n3J4ELN7rhx/MmM12Wrcu/dUV+/ebaNrUyr335uPnB2vXmnnwQX9Mplx69nR+eOKgQQH07ZvPo4/m\nO2aA+vcPYONGE489lk/16jbmz7dw990BLFuWTf36ugTmKgUcEbnsGcb/rsIROe3gwaJXbfzzqdUl\nuemm0yHGboeUFCsHDhh8/LFPsYBz3335DBx4ejH7ypVmvv7awty52bRoURSm2rWzsnOniYkTfZk2\nLffCfKHLiAKOiIjIWZR1Yi8zE156yY8lSywcPGhgtRYdGBtbfPalQwfnwLNypZnoaBvJyVYK/2dT\nq1ZWPv3UB3GdAo6IiMgZxMbaOXrUKNNC4OHD/Vm/3swjj+Rz5ZU2QkLsTJ/uw5Ilxf+ojY52Xpic\nkWGQnm4iLq74Qh+z2aufx3vRKOCIiIicwTXXWHnxRYNVq8x07HjmdTi5ubBsmYXx4/Po0+f0pSeb\nrWzTP+HhdmJjbcyYce6v2xBnCjgiIiJn0Ly5lYYNrYwb50dKSjbBwc7b//jDRFiYnZAQOzabgZ/f\n6dmWkydh6VILhlH6DEyrVlbeftuXoCD0stMLRAFHREQuGyfzCtmfmUOovw9xYWV7+Mzbb+dw002B\ndOwYxKBBpx/09803Fj7+2IevvsqmcmU7jRpZefVVP4KDwWSCSZN8CQmxc/Jk6edo29ZKu3ZWbr01\ngGHD/nsOg99+M5GXB08+mV96J+JEAUdERLye3W5n5rqDfPbTEaxZgRCQR93qvozsUo0Kgb5nPbZW\nLTvLl2fzxhu+vPmmL4cOGQQEQKNGVt5+O9fxFOO3387hscf8GTbMn4gIO/36FZCTU/Qi09IYBnzw\nQQ4TJ/oyZYovqakG4eF26tWzMWCAws250NvE5Yw0Zq7ReLlOY+YajZfr/jtmX285zKuzMilYexWm\nAl/s2LFW20/DDkeY0OtKDD0DycHTf870NnEREZG/ffbTEfI31cRUUDRbY2Bg3hPPjt12dh7x3D/M\n5dwp4IiIiNc7fCIfU7bz2+MNDGzHAzl6SpeAvJECjoiIeL0aUQFYw7Kc2uyGDVOF41SJuHzeHn85\nUcARERGvd2/LGIKa7qQwLBM7dmy+eRgNttHy6qAy300l5YvuohIREa+XGB/GmNtsvBe9kwMZ+fj7\nGnRJrMBdzeLdXZpcJAo4IiJyWUiuFkHTe8LJK7ThazHp7fFeTgFHREQuG4Zh4O9jdncZcgloDY6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE6yjgiIiIiNdRwBERERGvo4AjIiIiXkcBR0RERLyOAo6I\niIh4HQUcERER8ToKOCIiIuJ1FHBERETE61jcXYCUX9GTQx2/9jf7UyOsJvfW60+fq/tjMpSdRUTE\nfRRw5LwMaTiMbld0J6cwh6/+WsiolY9gt9voX3+Qu0sTEZHLmAKOnJeqoVVJqpQMQKv4Nmw9tpXp\nv0276AHHarNitVvxNfte1POIiEj5pOsIckE1jEpk34m9js9Hc47ywIpB1J5WjWpTY+gxtwu/pG9w\nOsZqs/LS2udp9GFd4t+JpNXMZL7c9pnTPsNWDKbj521YvGshrWYmU2VqFBvS1l+S7yQiIuVPqQHn\no48+Iisr61LUIl5g3/G9RAVGOz7f+1Vvvtm7gmdajGPqddOx2W3cNK8ru7J2OvZ5ce04Jm54hbvr\n9uHDLrNIjm3OkOUDmL39c+e+T+xh7I9PMbzxCGbe8CVVQ6tdsu8lIiLlS6mXqI4cOcItt9xC3bp1\nufnmm2nVqhWGYVyK2qQcsNltFNoKyS3MYdGuBSzcNY+BDe4H4D97v2btoZ+Y230xLSq3BKBl5dYk\nfVSPtza+wattX+dYbgZTN0/m4SaPMSLpcQDaV+3AgZOpvLzuBXom3Oo4V0ZuBp/fOJ/6kQ0u/RcV\nEZFypdQZnIcffphly5Zxyy23MGfOHK677jpee+019u7dW9qhchkY/f1I4t6pQM33KjP8P0O45cpe\nPJb8BAAb0n4mMiDKEW4AgnyC6Fj9etYe/BGALRl/kl2YzY1X3OTUb/daPdmZuYMjOUccbbFBcQo3\nIiJSJmVaZGwYBlFRUURGRmI2m8nKymL48OG0aNGCxx9//GLXKB5saOKDdK91E/6WAKqFVifAEuDY\nlpadRlRAVLFjogKiOZZ3rGifU4eK2gKd94v++zJXZu4xIgMi/94nGhERkbIoNeDMmDGDefPmERER\nwS233MLjjz+Oj48PNpuN6667TgHnMhcfEk9idOMSt8UExnA453Cx9sM56UT4RRTtE1QJgCPZR6jg\nX9GxT3p2OgDh/hGONgNdGhURkbIp9RJVVlYWkyZNYtq0aXTu3BkfH5+iA00mpkyZctELlPKrcUwS\nR3IO8+OB1Y627IJslu9ZSnJsCgB1KlxFoCWQ+TvnOB07f8ccrgiv5Zi9ERERcUWpMzjDhw8/47Yr\nrrjighYj7mO12dmSdoLcQhsJUUGE+vucd5/tq3agaaVm3LesD082f4YK/hWY/MskcgpzGdqo6Ocq\nwr8CAxvcz4SfX8ZistAwqhGLdi1g+d5lTOn4/nnXICIilyc96E/YceQUz339M0fz9mLnJBZbDe5q\nfBX3ta1z3n3P6DyTp1f/H0+tfoK8wlwaxTRh9o0LqBl2OhyPTB6N2WRm+m/TOJyTTo2wmkzu8C43\nJdxy3ucXEZHLk2G32+3uLsIVK1euZNy4cdhsNm699VYGDhx41v0LCqxkZmYTHh5IZmb2Jaqy/Mgt\nsNJ31koOMR2L/24AbNZA/LLv4ZXuPahbMeDsHYiDfsZcpzFzjcbLdRoz13n6mEVFhZRpv3L1JGOr\n1crYsWN57733WLRoEQsXLmTHjh3uLqtcW78vk0zrH45wA2AyZ5Pts4xZP//pvsJERETOQ7kKOJs3\nb6ZatWpUqVIFX19fbrjhBlasWOHussq1zJwCcu0HirWbzFmkn/TcBC8iInI25SrgpKWlUalSJcfn\nmJgY0tLS3FhR+VezYhBB1Mdud74F255fg8bxsW6qSkRE5Px4/SJjs9kgPDwQs9lEeHigu8vxOClh\nATT59Sp+PNAVe8AqTKYc8nMSiLZ04a5mCYSH+bu7xHJDP2Ou05i5RuPlOo2Z67xlzMpVwImJieHQ\noUOOz2lpacTExJz1GKvVrkXGpXiibT1mbQxhydbG5ORbaREfyb1NaxMX5q8xc4F+xlynMXONxst1\nGjPXefqYlXWRcbkKOPXr12f37t3s27ePmJgYFi1axKuvvurusso9fx8zfZJr0ie5prtLERERuSDK\nVcCxWCyMGTOGAQMGYLVaufnmm0lISHB3WSIiIuJhylXAAWjTpg1t2rRxdxkiIiLiwcrVXVQiIiIi\nZaGAIyIiIl5HAUdEzmrWlk+InhzKkK8HXLA+X1k3ngYzauP3vA/DVgwu83E95nah35K7L1gdIuK9\nyt0aHBG5tGZv/xyAJbsXk1OYQ4Dl/N5P9kv6Bl5a9zyjmz1Np9od8LOW7ZZPERFXaAZHRM7ocPZh\nVu3/jlbxbTlVcJJlu7867z63H9sGQL/699E8PoUaYXo8gYhceAo4InJG83fOwWq3Mr7VK8QGxTF7\n+xdO219cO47mnzRyfD5VcIq4dypw7WetHG1Hc44SMzmMb/f9h2ErBjN0xUAArngvHt/nLaxOXeW4\nDHay4KRT/00+qsfTq0dfxG8oIt5KAUdEzmjO9i+oH9mQhIgr6V6rJ//Z+zXH87Ic25vHtmBX1k7S\ns9MBWHdoDRbDwu9Hf+VE/nEAfjr4AybDRFKlZEYkPc6IJo8BMLv7Qlbd+z0Nohpe+i8mIl5PAUdE\nSrT/xD7WHVpDj4SbAeiZcAt51jwW7Vrg2CepUjIWk4U1B38AisLMtdWuIyogmnWH1jja6kc2INgn\nmBphNan+9yWpxOjGNKvcnBDf0Ev8zUTkcqCAIyIlmrPjSwB61OoJFAWSGmE1HYuOAYJ8gmgQ2ZCf\nDvwdcA78QEpsC5rHtuCnAz862prFtbjE1YvI5U4BR0RKNGf7FzSISiTUN5SsvEyy8jLpVL0L36eu\ndFySAmgW24KfDv5IvjWfDWnraR7XguZxKfx08AdO5p/gtyObaR6rgCMil5ZuExeRYrYf28ZvRzYD\nkDCtarHtC3bOoX/9QQA0j2vBlM1vsWr/t/iYfakX2QCTYeaZH57k+9RVWO1WmsWmnPV8fmY/AAqs\n+eBzuj0zL/MCfSMRudwo4IhIMbO3f47ZMPNRl1kEWAKdto3+fiSzt39xOuDEpmC323lj4wSSKzXD\nZJioW/Fq/C0BvL1pEgnhVxIZEHnW88UGVwZg27FtNIttDsDPaescC5VFRFylgCNyGbDZ7ezOyMbA\noFqFAEyGcdb952z/gjZV2tGhWqdi23rVvoNnfhjNvhN7qRJSlQj/CtSuUIcfD6zmyebPAGAyTCRX\nasbXe5Zyd90+pdbXOLoJsUFxjP7+cUYlj+ZY7jHe3Pi6FiCLyDnTGhwRL7f5wHH6zFzJg/MXMXz+\nIvrNWsXvh06ccf9N6RvZlbWTW6+8vcTtPRNuwWSYmLP9S0dbs7/X2DSPvaZYW3Kl5qXW6Gv2Zfr1\nn2DCoP/Se3h705u81GYC4X7hZfqOIiL/ZNjtdru7i7iYCgqsZGZmEx4eSGZmtrvLKVc0Zq7xxPE6\ndDyXIbO/JdMyA4vfAex2sOZXoULhPUy5tS2RQb5urc8Tx8yTabxcpzFznaePWVRU2V7vohkcES+2\nbOtBsliFxe8AAIYBFr99ZPIDK7YdcnN1IiIXjwKOiBfbl5WFzXSwWLvVOMD+LC3gFRHvpYAj4sXq\nREVisV1RrN3XXovakRXdUJGIyKWhgCPixTpcGUOUOYXC7HrY7WbsdjMF2Q2I9kmmbUKUu8sTEblo\ndJu4iBcLC/Dh5W4pTF4dyuZDRzAwaBQXxZBr6hLsp//9RcR76Xc4ES8XHx7A8zckkVtgBcDfx+zm\nikRELj4FHJHLhIKN94uefPrBiP5mfyoGRNIwqhG9r7qLTtU7O+07bMVgtmT8yde3fnepyyzVN3tX\nsO3YFgY1HOrU7sk1i+dRwBER8SJDGg6j2xXdKbQVknpyP0t3L+aexbdze507eb39ZMd+I5IeJ7cw\n142Vntm3+/7Dwl3zigUcT65ZPI8CjoiIF6kaWpWkSsmOzzdfeRttq1zLQ98MJSXuGm6vcycANcJq\nuqvEc1Yeaxb30V1UIiJe7o6r7qZJTBIzfp/maBu2YjAdP2/j+JyVl8nD3zxA/elXUmVKFI0+rMuI\nb4Y59TN/xxyafZJI1SnR3DT3Bn49vInoyaHM2vKJY5/oyaFM+3WK03EvrX2eOu9XL9O5Xlr7PG9v\nmsS+E3uJnhxK9ORQhq0YXGLNs7Z8QvTkUP44+ju3zO9O9amVaPHvJizcOd/p/Ha7nfFrnqPuBzWp\n+W5lHvzP/czZ/gXRk0PZe3zPOY6qeDrN4IiIXAbaxLfjjY0TKLAW4GP2KbZ9zOr/Y92hNTx3zQtE\nB8aQenI/Px38wbH9l/QNDPy6L91q9uCFVi+z7dhW7lvW55xqOdu57qp7L7uydvJ96kqmX18UnCqW\n8jb6IV/35+66fRiaOJxpv05h0Nd9WRezmbi/31I/ZfNbTNzwKg81eZRmlVJYsnsRY38cc061S/mh\ngCMichmIDa5Moa2QY3nHiA6MLrZ9Y/rP9Kt3Hz0Sbna03Vr79AtXJ22cyJURtZl63QcYhkH7qh0p\nsBXy3DkEhbOdKy64MjGBlfAz+zldajubQQ2HcsdVdwPQMDqRqz+oxbLdS+hTrz9Wm5U3N77OvVf3\nY1TykwC0q3ote4/vIfXkfpdrl/JDl6hERC4Dpb1X+eqK9Xnrlzd4/7d32Zm5vdj2X9I3cF21zhiG\n4Wi7vnqXc6qltHO5qm2V9o5fV/CvSGRAFAdPpQKw7/g+0rPT6PSPWv/5WbyPAo6IyGXg0KkD+Jh8\niPCLKHH7+Nav0LnGDby67kVS/t2EZp8kMmf7F47t6dlpxS4VVQw4t9d9lHYuV4X6hTl99jX7kluY\nB0DaqaKXykYWq/3sl72k/FPAERG5DHy77z80iEoscf0NQJhfOM+3epnf++7gm9t+oHF0EkOWD2Br\nxhYAogNjOJpzxOmYozlHi/XjZ/Yj31rg1JaVl+nSuS6kmKBKABwpVvuRknYXL6KAIyLi5f7950ds\nSP+ZPlf3L9P+V0fW45kW/8Jmt7H92DYAEqMbs2zPV06XupbsXlzs2NigOLYf2+r4bLPbWJV65gfz\nlXSuohmYC/O8myqhVYgOjGHJX4uc2peWULt4Fy0yFhHxInuP72X9obUU2go5cCqVJX8tYt6OOdxR\n52561bnjjMd1nX0dXWp2o06FqzAw+PjPGQRagmgc0wSAYY0e4vov2zNwWV96X3Un245t5eM/pgNg\ncHpdTpea3fjgt3epF9WA6qHV+fiPDzmRf8Klc9UKT+BwTjqztnxCnQpXUcG/IlVDq53TeJhNZoYm\nPsizPz5JxYBIkis1Z+nuxfx59A8ATIb+nu+tFHBERDzMsex85v+Wypp9qYT6+9Gt7hW0qF7y2pl/\nenvTJN7eNAk/sx8V/SNJjG7Mh11mFXtVwz8lVUrm0y2fsPfEXsyGmfqRDZjV9UvHrdaJ0Y2Z0vF9\nxv30LEt2L6JhVCNeaj2BWxd0J8T39CsiHm06iiM5hxm/5jl8TL70rz+Q2hXq8MFv75b5XN1r9WT1\ngVWM/fEpjuQcoVftO5h07TuuDqPD4IZDyczLYPpv7/HOprfoVL0zDzZ5hJErRxDiG3LO/YpnM+yl\nLa0v5woKrGRmZhMeHkhmZra7yylXNGau0Xi5TmNW3NFT+Tw090dS81eA7xZstkACCtrTs24LHru+\ngUeN1+dbZzF0xUDW3bWZaqHV3V1Oic70M/bwNw/w3b5v2HDP726oyrN5+v+XUVFlC6WawRER8SBf\nbt7D/vylWIJXA2AGCnw/ZP4fEdzRPIEgN9b22HcP0ya+HeH+4Ww+vIkJP79Mx2qdPDbc/NefR/9g\n3o4vaVqpGYZh4j97v2bmlo95qvlYd5cmF5ECjoiIB/lhdyqG/x9ObYapgFzTr2za15oWVcLOcOTF\ndyw3g5ErR3AsL4MIvwp0v6InT7fw/JAQ6BPImoM/Me23d8kuOEV8SBWeaj6W+xOHlX6wlFsKOCIi\nHiTAx4I9z79Yu5lg/HzMbqjotPc6zXDr+c9VtdDqzOmxqPQdxato+biIiAe5oc4V+OS1wm4//duz\ntSCKIK6iWY0KbqxMpHzRDI6IiAe5/qoYNh1qxY97ozjBBnyNCoQbifxf+ySC/Sxk5uS7u0SRckEB\nR0TEg1jMJv7v2vpsS6/Jn2mtCPKzkFw1nLCAkp9ALCIlU8AREfEwhmFQOyaY2jHB7i5FpNzSGhwR\nEZFyIHpyKNN+nVKsfe/xPURPDmXZ7q8u6vlLOk+Tj+rx9OrRZz1udeoqoieHOp4efaloBkdERERK\nFRNUicU9l5MQcaW7SykTBRwRERHBarNitVuBwGLbcgtz8bf4k1Qp+dIXdo50iUpERMTLWG1WXlr7\nPI0+rEv8O5G0mpnMl9s+c9pn2IrBdPy8DYt3LaTVzGSqTI1iQ9p6Ptw8g+jJoWxIW0+PuV2oOiWa\nt355/ayXwl5d/yJXf1CL6lNjGfx1f47nZZ21PpvdxhsbXiP544bEvxNJ808aMWvLJxd0DDSDIyIi\nUk7Y7DYKbYVObUWzLs5eXDuON3+ZyKNJo0iMbsyiXfMZsnwAhmHQM+FWx377Tuxh7I9P8UjSSKID\nY6gaWo30jFQABn3dj7717uPRpqMI8z3zE7Tn7PiCGmE1ea3dJNJOHWLsj2N4+NthTOv04RmPeWLV\no3y6ZSaPNB1Jg8iGfLf/Gx76ZigV/CtwXSkvhi0rBRwREZFyYvT3Ixn9/ciz7nMsN4OpmyfzcJPH\nGJH0OADtq3bgwMlUXl73glPAycjN4PMb51M/ssHpDjKK/nVf/cEMbHi/o3nv8T0lni+3MIdPbvic\nYJ+iu/4CfQIZunwg2zK2cmWF2sX235W1k+m/TeP19pO5vc6dALSp0o60U4d4Zd14BRwREZHLzdDE\nB+le6yantrTsNO5e3MvxeUvGn2QXZnPjFc77da/Vk+H/GcKRnCNEBkQCEBsU5xxu/keH6p3KVFOb\n+PaOcAPQpUY37NjZmP5ziQFn1f7vMBkmbqjZzWk2qlV8G+bs+AKrzYrZdP6vJfn/9u48Oqr67uP4\nZ2aSyUoISxaWsAcDSt+dXQwAACAASURBVEBBFBTRQIgQYpBFBdRCcUGtLbWPbXFBBUTF9nHBpbgV\n9UGjLYJAEBREiQgiFAyoICiRREhCCAmGkGVm7vNHymBMAhmWzOTm/TqHc5jf3b7zPePw8f7u3EvA\nAQCgkWjfrL36RF5UbezXZ1byjuZKkiKCI6qNRwZHSpKKyg67A07Ef8dqExlU97Jfah3cutrrYP9g\nhfiHKq80r9b1C48dktNwqusr7Wtdnleaq7ah7ep17JMh4AAAYCJRIdGSpILSArUMbOUezy/NlySF\nB7Zwj1lkqXtHlpMs+4WC0oJqr0srS3W0skRRwVG1rh8e2EJ+Vj8tv/ZDWS01f+vUOiiilq08R8AB\nAMBE4lr2ULBfsJZ+v1j/0/Kv7vGlexara3g399mbs+XTnI9VUlninqZasXeZLLLUONN03KB2g+V0\nOXWk4oiujEk4q7X8EgEHAAAvMAxDhiRrPc+U1FeLwJa6Lf5OPbXlSflZ/dQ74kKl/7BMq/d9qPmJ\nr53VY0lSoF+QJqaP0119fq+80jw98vmDGtElRee1jKt1/W4tYvWb83+r2z+crLsunKY+kReq3FGm\nnYd36oeiPXrqqufOSl0EHAAAGtDRCofe+PIHffTdPlU4XerVpqVuvbSHurQKOWvH+Ev/+2Wz2rRg\nx6s6eCxfnZt30QtDX9a1sWPP2jGOG9VtjEL9QzVt7e9UWnlUSZ2Ga+7gp066zRNX/K+6hnfT/33z\nuuZuelTN7M3UvWWcJsTddNbqshiGYZy1vfmgykqniopKFR4erKKiUm+X06jQM8/QL8/RM8/QL8/5\nWs9chqE/L9usrYdWSUHrZbGWy3EsVq2MMXpm1CC1Dw/ydok+17Nfi4hoVq/1uJMxAAANZPv+I/r2\n0C5ZQj6S1VYqi8Up/+CdKtRKvbc9y9vlmQoBBwCABvJj4TEd1dc1fqBksf+or3MPeqcokyLgAADQ\nQFoE+yvQUvP+Ly5HC0U1O3vX4ICAAwBAg7m4Q7ha+vVUZVkXHb8C1uVophBHkkZf0M27xZkMv6IC\nAKCBBPrb9OjwSzR7tZ/yj/4kw3JMdqOdfnvJBerTvu4HWsJzBBwAABpQ51bBevm6y/V9wVEdq3Qp\nNiJEQf5n/uwlVEfAAQCggVktFsVGhJ56RZw2rsEBAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACm\nQ8ABAACmQ8ABAACm43MBZ968eRo0aJBSU1OVmpqqTz/91L1s/vz5SkxMVFJSkjIyMrxYJQAA8GU+\neaO/SZMmacqUKdXG9uzZo/T0dKWnpysvL0+TJ0/WqlWrZLNx90cAAFCdz53BqcuaNWuUnJwsu92u\nmJgYdezYUZmZmd4uCwAA+CCfDDgLFy5USkqKpk+fruLiYklSXl6eoqOj3etERUUpLy/PWyUCAAAf\n5pUpqkmTJqmgoKDG+LRp0zR+/HjdeeedslgseuaZZ/T444/rscceO+1j2WwWhYcHy2azKjw8+EzK\nbnLomWfol+fomWfol+fomefM0jOvBJwFCxbUa71x48Zp6tSpkqrO2OTm5rqX5eXlKSoq6pT7cDoN\nFRWVKjw8WEVFpadVb1NFzzxDvzxHzzxDvzxHzzzn6z2LiGhWr/V8booqPz/f/ffVq1crNjZWkpSQ\nkKD09HRVVFQoOztbWVlZio+P91aZAADAh/ncr6iefPJJ7dy5U5LUrl07zZw5U5IUGxur4cOHa8SI\nEbLZbJoxYwa/oAIAALWyGIZheLuIc6my0skU1WmiZ56hX56jZ56hX56jZ57z9Z412ikqAACAM0XA\nAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAA\npkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPA\nQZOSluanyMhmuuOOQG+XAgA4hwg4aFLee89fkrRypZ+OHfNyMQCAc4aAgybj4EGLMjJsGjTIoaNH\nLfrwQz9vlwQAOEcIOGgyli71k9Np0eOPl6tNG5fee69mwOnbN0QPPRSgf/zDX717hyg2NlS33Rao\n4uKq5U6n1KtXiObOtdfYduhQq37zm0CVlUnt24dq0aIT+589267IyGZaudLmHps+PUDJycHu17Nm\n2TV4cLA6dQpV794hmjo1UHl5FvfyRx4JUL9+ITKM6sdNS/NTu3ahKiiwCABQhYCDJmPxYj/16uVU\nbKxLqakOffyxn44cqbne0qV+ysjw09//XqYHHyzXhx/66dFHAyRJNpt0/fWVevdd/2pBIyvLoowM\nacKESgUGSn36OLVx44kws2GDnwIDDW3c6PeLMZsuvdThfl1QYNUf/lCht946ptmzy/Xjj1aNGRMk\nl6tq+YQJldq3z6rPPz+xX0l6+21/DRvmUOvWv0o+ANCEEXDQJOTkWPTllzaNGlUVKEaPrlR5uUXp\n6TXP4vj5Sa+/fkxDhzp1882VuvHGymrrHQ8a69efCBppaf6KjJSGDHFKki691KkvvqhaXlYmffWV\nVRMmVLrHioulnTutuvRSp3sfzzxTptGjHRo40KkRIxx67bVj+u47m3ub2FiX+vd36O23/d3bZGVZ\ntHGjTePHV56tVgGAKRBw0CQsXlwVCkaNqgoCffq41Lmzy33R8S9dfrlDfr/IPd27u1RQYFHlfzNE\nly6GBgxwKC2talvDkP71L39NnGi4txswwKldu6w6fFjassWmkBBp0qRKZWZaVVoqd2jp3/9EwFmz\nxqYRI4LVtWuo2rRppt69QyVJ339/4j/TiRMrtXy5n0pKql6npfkrIsJQQsKJ/QAACDhoIhYv9lN8\nvEthYYaKi6vOoCQlOfTZZzbl51e/diUsrPq2drshw7CovPzE2IQJJ4JGRoZN2dlW/eY3J6aILr7Y\nKYulKshs3GhT//5OnXde1fG3bKkai4tzqXnzqvW3brXqppuC1LatS88/f0wrVhzVBx8claRqx73m\nGoes1qppNMOQ3n3XX9ddV1ktkAEAJL4WYXq7d1u1Y8fxaZ5mNZYvW+anKVM8m+K55hqH7r+/Kmh8\n9pmf+vZ1qkcPqaioanlYmNSzp0sbN/ppxw6rrrrKIYul6ozNxo02bdjgV216asUKP7VqZejll8tk\n+W/eys6uedFwSIh07bWVSkvzV/v2hnJyrBo/3lFjPQBo6gg4ML333vOTzWbozTePKSio+rL77w/Q\ne+/5exxwgoKqgsZrr9m1Z49VjzxSLqn6dNeAAU599plNe/ZYdd995e6xpUv9lZlp1e23V7jXLSuz\nyN9f7nAjSYsW1Zw+k6rOHg0fHqInn5T69q26aBoAUB1TVGh0jlY49E3uz/qxsFTGr38zXYvFi/01\neLBTQ4c6ddll1f9cf32lNm+21nq25FQmTqxUZqZNhlEVdn7t0kudysy0yWKR4uNd7rHNm22qrLRU\nO4MzeLBDOTlWPfBAgNats+nvf7frnXdqDzh9+7oUF+fUF1/4cXExANSBgINGwzAMLfpPrm6c/43+\n8lKefvdKlv6Q9p1yj5TVuc1XX1n1ww9WjRtXexAYPbrqmpbjFyF7ok8fl9q0cWnECEeN63Yk6ZJL\nqgJMv35O9zUyvXq5FBJiqEMHl6KjT4SzoUOdevDBci1f7qebbw7S55/btHBhaZ3HHj7coaAgo9Zg\nBQCQLEZ9/he4EausdKqoqFTh4cEqKqr7HwzU5Gs9W/9DoWa/dVAVG3rKWhEgQ4YcbXIVO+Qnzb+5\nh6yWhr3R3a5dVg0aFKJ//7tUV1zhbNB+JSUFq2tXl154oe5w1xj42mfM19Evz9Ezz/l6zyIial5L\nWRvO4KDReHfjQZV91UnWiqqb7llkkf+BNtq/z1/b99dyx75zpLBQ2rTJqvvuC1CPHk4NGtRwP9He\nts2qefPs2rrVpltvrTj1BgDQRHGRMRqN/COVspYG1xivLAzRodKGm6r58EM//eEPgYqNden558vU\nkCeOhg0LUfPmhh54oFwXXsjFxQBQFwIOGo3Y6CAdaF4s68FI95ghQwHRRerYomOD1XHDDQ7dcENJ\ngx3vl/Lzf/bKcQGgsWGKCo3GxAFRan5xlhwtCmXIkMteLl2wWxeeZ1eXVjXP7AAAmi7O4KDROC8q\nVLOv76CXo37UD/k7Zfe36Or4lrr50s6yNPAFxgAA30bAQaPSq22Ynp0QpkqnSzarpcF/OQUAaBwI\nOGiU/G3MrgIA6sa/EgAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAahZyfszXt47sU//p5\nav+P1ur75gV6cP10FZUdPmfHnLtpjuJe6+R+vf6nDEW+EKZvD30jSapwVmjupjnaXpB5zmoAcHr4\nmTgAn7ez8Ftdu2SEWgdF6L5LZqhDs47aXfSdnt7yN63+cZXeH7VSkcGRp97RGYqP6K0Vo1erU/PO\nkqQKV4X+tvlxdQjrqF6t48/58QHUHwEHgE8zDEN3rr5VzQPCtWLMajWzh0mSBra7XMM6Xq0r3xmg\n+zLu1StJr5/zWprZw9Qvuv85Pw6AM8cUFQCftmH/eu0oyNQf+97rDjfHtQltq1vip2r5D+9rf8lP\nkqS0nQsV+UKYSiqrPxC175sX6KH197tff5S1UmOXpqrnP7uoy8vtNHxRgtbuW3PSWn49RdXl5baS\npN9/fIciXwhT5Ath2nfkRyX9+0rdvWZqje3vXjNVCe9e7nkTAHiMgAPAp204sF6SNKLLyFqXD+88\nUi7DpY0HPvdov/t+/lFJna7Wc0Ne0mtXv6l+0ZdofPoYfXFgY7338V7qcknSPX3v1YrRq7Vi9GpF\nhURrQo+btez796uFrJLKEi37/n1NiLvRozoBnB6mqAD4tAMlB9Q8ILzG2ZvjYprFSJL2l+z3aL9T\net3u/rvLcOnydldoV+G3euvbN3RJm0vrtY8+kRdJkjo171Jt6mp07Fg9tP4+LduzRON7VAWapXsW\ny+Gq1Oju4zyqE8DpIeAAMAWLPHvw6v6SnzTni5lal/OJ8o7mypAhSeofXb9wczLN7GEa2TVVabsW\nugNO2s6FSuo8Qi0DW53x/gGcGgEHgE9rE9pGxeVF+rniSK1ncbJ/zpYkRYVE1XufLsOlm1bcoJLK\nn/WXi+9X5+ZdFOwfrCc2PaqCYwVnpe6JPW7WqCUjlFW8V4YMbTzwud5O/vdZ2TeAUyPgAPBpA9tW\nXZS7cu8KjTvvhhrLV2WtkCT1i6qaIgqwBUiSKp0Vkv+J9YrKi9x/31v8vbYXfKW0kYuU0CHRPV7m\nKDtrdQ9oe5m6hHdV2q6FkmEoOqSNrowZctb2D+DkuMgYgE+7tM1AXdA6Xv+7ZW6NX0blHc3Vy5kv\namDby933pmkT2k6S9N3h79zrbcn7Uj9XHHG/PvbfIGP/bxiSpOyf92lTbv0vMJYku9Uuqe5gND7u\nJr278229uytN13UfL5vV5tH+AZw+Ag6ABuUyDP1UfEz5P5fXa32LxaLnh7ykorLDGrFoiN7Z+ZY2\n7F+vN77+p4YvGiKHy6mnrnrOvf5FkX3VJqSt7v/sz1r94yr9a1ea7ln7+2rTW7EtuqttSDs9tP5+\nfZS1Uot3/1vXLRulNiFtPXovdptdHcI6aen3i7XxwAZty/+PKpwV7uXXx01QbukB5ZRka3yPiR7t\nG8CZYYoKQIP56qdiPbVumw4dOyTJpu6RbfSHy3qpQ4ugk27Xo1VPfTRunf725eOavfFhHTyWL5fh\nUrfwWC0ZtUIdwjq617Xb7Fpw9UL9Zd09mrLqZnUNj9XcwU/pztW3uNcJsAXon1f/n/6a8SdNWXWz\n2oS21R/73qv1P2VoZ+G3Hr2nJ694Sg9//oDGLb1G5c5ybb5xu7ueqOAoXRTZT5LUNTzWo/0CODMW\nwzAMbxdxLlVWOlVUVKrw8GAVFZV6u5xGhZ55hn6dXPbhY7p7yVoV+78pm32/JItcFT3URtfp5euu\nULDds+mbx7+YpXlbn9a7KUt0WbtB56boM3S4rFC9X4/TY4P+pok9bz7j/fEZ8xw985yv9ywiolm9\n1mOKCkCDWPb1jyq2rJZfwH5ZLJLFYsgevFMFldu0fu8hj/f3l/4PaGjHJE1eOVF7Du8+BxWfvpKK\nn7Ul70tNz/gfhdpDdW3sWG+XBDQ5TFEBaBB7DxdJfrk1xssse5VTVCLJs4dlWiwWvT78rbNU3dn1\n1cFtuvb9ZMU066DnhsxXsH+wt0sCmhzO4ABoEN1bt5Ic7WuMBxmx6tiifqecG4vL2g1S/p1HtOWm\nHdV+ht7UHX9eV+QLYeowP1IXvtFTkz6YqFVZH9RY9+41U5X4r8H13vfcTXMU91on9+vjzw3bkb+j\n2vFf3T7/jN4DGg/O4ABoECPPj9HKXUN0qCxffgFZkqyqPNpL7QN7a2Dnll6uDg3ljt53K6Vrqhwu\nh34qydGqrBW6ecUNuiFuop5JeMG93j39/nxG9yWKj+itFaNXq2uLrio/aupLTVEHAg6ABtEmLFCP\nDh+oeZ+Fal/RYUlW9e3QVlMviVOgP/eHaSo6hHWo9tyuMd2v05UxQzRt7V0a0PYy3RBX9XP6zs27\nnNFxmtnD1C+6v4L8g1Qu371gFucOU1QAGkxcVDM9N3qg3hg/TG9NHKanxw1UdFigt8uCl03ocZP6\nRvXT61+/6h779RRVcXmR/rj2d+q1oLti5kfowjd66p61d9e5z9qmqH7t20Pf6Px/dtOdq2+V0+WU\nVPXLtz998nv1/GdXxcyP0IhFQ7Ul78uz8C7R0DiDA6BBWSwWtQi2e7sM+JjB7a/Ss1ufUqWzUv42\n/xrLZ6y/T1/mfqFZlz2myOAo/VSSo40HPj/t420/+JXGLUtVcpdr9OTgp2W1WFXuLNfYpakqrijW\nQwNmKSI4Qgt2vKqxS1O1ceJWRQXX/3ln8D4CDgDA69qEtpPD5dDh8sOKDK75i7qt+Vv02wtu1ajY\nMe6x2p5NVh9b8r7UDcvHaFz36/Xo5XNlsVQ9if7fu97RzsJvlHHDF+oS3k2SdEX7qzTgrYv04rZ5\nenjg7NM6HryDgAMA8LpT3XP2/Fa99Py2Z2W12jS4/ZWnfWfoTQc26tGNMzXpgimaMWBmtWXrctYq\nPqKPOoR1ksPlcI8PbHu5vsrfelrHg/cQcAAAXpd7dL/8rf5qEdCi1uWPX/E3PbHpUf39yyf013V/\nUufmXfTX/g94fBPFT7I/ltNw6LrzxtdYdqisUFvyvlTbf9T8VV+nsM4eHQfe55WLjD/44AMlJycr\nLi5O27dvr7Zs/vz5SkxMVFJSkjIyMtzj69atU1JSkhITE/XSSy81dMkAgHPok+yPFR/Rp9brbySp\neUC45gx6Ul9P3qO1132uiyL76Y7Vt2hX4U6PjvPHfvfqsraDdN2yUcoq3lttWYuAFuoTcaE+HPtJ\njT8LfPSmkqibVwJO9+7dNW/ePF188cXVxvfs2aP09HSlp6frlVde0SOPPCKn0ymn06mZM2fqlVde\nUXp6upYvX649e/Z4o3QAwFn21rdv6j/5WzTp/Cn1Wv/81hfo4YGz5TJc2n34O4+O5W/116tXv6mu\nzbtp7NJrdKBkv3vZoPaDtffIXrULjVGfyIuq/enZ6nyPjgPv88oUVdeuXWsdX7NmjZKTk2W32xUT\nE6OOHTsqMzNTktSxY0fFxMRIkpKTk7VmzRp169atwWoGAJy5fUf2aXPuJjlcDu0/+pNW7k3X+3sW\na0LcTbo+bkKd2418b5hGdElRXMsessii//v2dQX7heiiqL4e1xDkF6Q3k9/RuKXXaOzSa/T+tSvV\nOqi1rjtvvF7/+jVd+/4I3dnn9+oY1kmFZYXamr9FkcGRmtr7d2fy1tHAfOoanLy8PPXu3dv9Oioq\nSnl5eZKk6OjoauPHgw8AoGEdOFKmt//zgzbn5CrE7qeUnt00omcb+Vktp9z2xa/m6cWv5inAFqBW\nga3VJ/IivTEiTUmdhp90u37R/fXOzoXa9/M+2Sw29Wodr7SRi9Q2tN1pvYdQ/1C9nbxI174/Utcv\nu1aLU5crLKC5Fqcu1xObHtXcTXN08Fi+WgdF6MLIvkrqNOK0jgPvOWcBZ9KkSSooKKgxPm3aNA0d\nOvRcHbYGm82i8PBg2WxWhYfzwDtP0DPP0C/P0TPP+EK/DhQf05+WbVCuc5n8Avco3xWkFzZfqR+K\nrtLDIy866bYV9zlOuvyX3hzzRrXXT4/435OuP2fYbM0ZduJn3MnhSao43yGbzSqn01Xr8cMVrK23\nb60x9nzKc3pez9W7VrPxhc/Z2XDOAs6CBQs83iYqKkq5uSeeNpyXl6eoqKobK9U1fipOp6GiolKF\nhwerqIjbdXuCnnmGfnmOnnnGF/r16mc7daByhfxCtsiQZLEVyxH8L63+Lkqp33dQ51a+9Q+jL/Ss\nsfH1nkVE1O/hvD71qIaEhASlp6eroqJC2dnZysrKUnx8vHr16qWsrCxlZ2eroqJC6enpSkhI8Ha5\nANDkbMnJlSVgd7Uxi8WpMst27cov8VJVQE1euQbno48+0qxZs1RYWKjbb79dPXr00KuvvqrY2FgN\nHz5cI0aMkM1m04wZM2SzVT2Eb8aMGbrlllvkdDo1ZswYxcae3k2eAACnr3lggIySUMnvcLVxf0sr\nhQbw0FT4DotxqttHNnKVlU6mqE4TPfMM/fIcPfOML/Rr7e6DeiJjuRwhC2WxVkqSHOXtFOG6RW+M\nH+xzT4b3hZ41Nr7es/pOUfnUr6gAAL5tcLfW2l1wuZZ901oVtp2yqbla2WL1UFI/nws3aNoIOACA\nerNaLLptQKyuOT9GO/MGKNhuU592zWX386lLOgECDgDAc9FhgYoOC/R2GUCdiNwAAMB0CDgAAMB0\nCDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgA\nAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0\nCDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgA\nAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0\nCDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgA\nAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0\nCDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0CDgAAMB0vBJwPvjgAyUnJysuLk7bt293j+fk\n5Cg+Pl6pqalKTU3VjBkz3Mt27NihlJQUJSYmavbs2TIMwxulAwCARsDPGwft3r275s2bp4ceeqjG\nsg4dOuj999+vMf7www9r1qxZ6t27t2699VatW7dOgwcPbohyAQBAI+OVMzhdu3ZVly5d6r1+fn6+\nSkpK1KdPH1ksFo0aNUpr1qw5hxUCAIDGzOeuwcnJydGoUaN04403avPmzZKkvLw8RUdHu9eJjo5W\nXl6et0oEAAA+7pxNUU2aNEkFBQU1xqdNm6ahQ4fWuk1kZKTWrl2rFi1aaMeOHbrrrruUnp5+RnXY\nbBaFhwfLZrMqPDz4jPbV1NAzz9Avz9Ezz9Avz9Ezz5mlZ+cs4CxYsMDjbex2u+x2uyTpggsuUIcO\nHbR3715FRUUpNzfXvV5ubq6ioqLqtU+n01BRUanCw4NVVFTqcU1NGT3zDP3yHD3zDP3yHD3znK/3\nLCKiWb3W86kpqsLCQjmdTklSdna2srKyFBMTo8jISIWGhmrbtm0yDENLlizRkCFDvFwtAADwVRbD\nC7+3/uijjzRr1iwVFhYqLCxMPXr00KuvvqpVq1bp2WeflZ+fn6xWq+6++24lJCRIkrZv367p06er\nrKxMV1xxhR588EFZLJaGLh0AADQCXgk4AAAA55JPTVEBAACcDQQcAABgOgQcAABgOgQcAABgOgQc\nAABgOqYLODyp3HN19UyS5s+fr8TERCUlJSkjI8M9vm7dOiUlJSkxMVEvvfRSQ5fsU+bNm6dBgwa5\nP1uffvqpe1ld/Wvq+PzUT0JCglJSUpSamqrRo0dLkoqKijR58mQNGzZMkydPVnFxsZer9K7p06dr\nwIABGjlypHusrh4ZhqHZs2crMTFRKSkp+vrrr71VttfU1i/TfocZJrNnzx7j+++/N2688UYjMzPT\nPZ6dnW0kJyfXus2YMWOMrVu3Gi6Xy5gyZYrxySefNFS5PqGunu3evdtISUkxysvLjX379hlDhgwx\nHA6H4XA4jCFDhhj79u0zysvLjZSUFGP37t1efAfe9eyzzxqvvPJKjfG6+tfU8fmpv6uuuso4dOhQ\ntbEnnnjCmD9/vmEYhjF//nxj7ty53ijNZ2zatMnYsWNHte/3unr0ySefGFOmTDFcLpexdetWY+zY\nsV6p2Ztq65dZv8NMdwaHJ5V7rq6erVmzRsnJybLb7YqJiVHHjh2VmZmpzMxMdezYUTExMbLb7UpO\nTm5yPauPuvrX1PH5OTNr1qzRqFGjJEmjRo3S6tWrvVyRd1188cVq3rx5tbG6enR83GKxqE+fPjpy\n5Ijy8/MbvGZvqq1fdWns32GmCzgnw5PKPfPr3kRFRSkvL6/O8aZs4cKFSklJ0fTp092nw+lT7eiL\nZ6ZMmaLRo0frnXfekSQdOnRIkZGRkqSIiAgdOnTIm+X5pLp6xPd93cz4HXbOHrZ5LvnKk8obk9Pp\nGU44Wf/Gjx+vO++8UxaLRc8884wef/xxPfbYY16oEmbz9ttvKyoqSocOHdLkyZNrnGm1WCw8suYU\n6NGpmfU7rFEGAJVQJAAAAqNJREFUHF95Unljcjo9+3Vv8vLy3L2pa9ys6tu/cePGaerUqZJO3r+m\njL7U3/G+tGrVSomJicrMzFSrVq2Un5+vyMhI5efnq2XLll6u0vfU1aOm8n3vqdatW7v/bqbvsCYz\nRcWTyj2XkJCg9PR0VVRUuHsWHx+vXr16KSsrS9nZ2aqoqFB6err7oahN0S/n8FevXq3Y2FhJdfev\nqePzUz+lpaUqKSlx/339+vWKjY1VQkKClixZIkl8X9Whrh4dHzcMQ9u2bVOzZs3cU1lNmVm/w0z3\nsE2eVO65unomSS+++KIWLVokm82m++67T4MHD5Ykffrpp5ozZ46cTqfGjBmjO+64w5tvwavuvfde\n7dy5U5LUrl07zZw50/2lWVf/mjo+P6eWnZ2tu+66S5LkdDo1cuRI3XHHHTp8+LCmTZumAwcOqG3b\ntnr66acVHh7u5Wq955577tGmTZt0+PBhtWrVSnfffbeGDh1aa48Mw9DMmTOVkZGhoKAgzZkzR716\n9fL2W2hQtfVr06ZNpvwOM13AAQAAaDJTVAAAoOkg4AAAANMh4AAAANMh4AAAANMh4AAAANMh4AAA\nANMh4AAAANMh4ABotDIzM5WSkqLy8nKVlpYqOTlZ3333nbfLAuADuNEfgEbtqaeeUkVFhcrKyhQd\nHa3bb7/d2yUB8AEEHACNWkVFhcaOHauAgAClpaXJZrN5uyQAPoApKgCNWlFRkUpLS3X06FGVl5d7\nuxwAPoIzOAAatalTpyo5OVk5OTk6ePCgZsyY4e2SAPgAzuAAaLSWLFkif39/paSk6LbbbtP27du1\nYcMGb5cFwAdwBgcAAJgOZ3AAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDp\nEHAAAIDp/D+/GS0S8o9DzwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dv1FhX-QsoW1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# WordCloud"
      ]
    },
    {
      "metadata": {
        "id": "BmpBL_BMUFQg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Most Commonly Used Words in Negative Reviews\n",
        "#Most Commonly Used Words in Positive Reviews\n",
        "\n",
        "def create_word_cloud(sentiment):\n",
        "    try: \n",
        "#         df_brand = df.loc[df['Brand Name'].isin([brand])]\n",
        "        df_brand_sample = df.sample(frac=0.1)\n",
        "        word_cloud_collection = ''\n",
        "        \n",
        "        if sentiment == 1:\n",
        "            df_reviews = df_brand_sample[df_brand_sample[\"Sentiment\"]==1][\"reviewText\"]\n",
        "            \n",
        "        if sentiment == 0:\n",
        "            df_reviews = df_brand_sample[df_brand_sample[\"Sentiment\"]==0][\"reviewText\"]\n",
        "            \n",
        "        for val in df_reviews.str.lower():\n",
        "            tokens = nltk.word_tokenize(val)\n",
        "            tokens = [word for word in tokens if word not in stopwords.words('english')]\n",
        "            for words in tokens:\n",
        "                word_cloud_collection = word_cloud_collection + words + ' '\n",
        "\n",
        "        wordcloud = WordCloud(max_font_size=50, width=500, height=300).generate(word_cloud_collection)\n",
        "        plt.figure(figsize=(20,20))\n",
        "        plt.imshow(wordcloud)\n",
        "        plt.axis(\"off\")\n",
        "        plt.show()\n",
        "    except: \n",
        "        pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xbKVbZyn9ASx",
        "colab_type": "code",
        "outputId": "239b3407-acc2-4cc1-9f5c-918c380cffed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Word Cloud from Positive Reviews\")\n",
        "create_word_cloud(1)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Word Cloud from Positive Reviews\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L+o4dUupK97jJTBkTacV9drTpXeJ+XdRZa+fQ/O4LAIDgAN3X5c0hS4Ypn/yymqbumb/q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\nAwCCPZEqNkMWqQZ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MwzAMwzAMwzAMwzATjBNSISTelh1wb1bXaZej0dC/P+a2Hj+N5m/s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\n7eD18IQ11Opzs3g5DYDffDmx6oyMdaeyPkbq9tC08ykV5QAAT+3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vsrHvFckfyGeohZSH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SChZGTzO2Xn15jLKpdcFHd9VBifOk9qf6H7nMj+hLnD14wwp9FQIUyjmQcrzryu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\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d2Ebq7nBIrzITxqWL17wPAODOp6Ia4aBUhR3c/nfD9SdCXhGprhpWkCrdbJG/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\njyetx2i2AACarr8NAODtUo7Nw/TcFBwdTLvszPJzwZEnfjo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Ix3wR9OQ3U50gMpE4I+Fc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\nAAAAgJoREAIAAACoGQEhAAAAgJr5Fwc26urXVX3NAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JwYpWSOaKLDG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "7fde8d62-8fd9-4128-e027-795193df9176"
      },
      "cell_type": "code",
      "source": [
        "print(\"Word Cloud from Negative Reviews\")\n",
        "create_word_cloud(0)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Word Cloud from Negative Reviews\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0/c8+LE3phcJIqH6EokrEauN7xh7tCDVM2TevU8d1Kr1nZiedK0XXXwAACOza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\nbSHtI7XVtl9fS2XBRcn1aAqfLfXvAZDKIG3boFLvzJBgxjQqS9vZSeU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qpE0HnleXTSu9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\nFCMu+Jvf5EigVaUuArB1G8t/8gnfcnaJDUlQHNrL/6LTeDOqHNg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KIfTC0/o5+IJz+Ax/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AiAvrf5Yu8B7sf7f6vv6//7C4YX3mKYS3/9Sobnf7HJ/+/+hRFCEm1nO0+aq+9Fudm+Qwm/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\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WkfG01SlNC4VCqPLcawEAzWulQWWqTL7oZnW+RDm2An83paXu2rlOXebrbqUZZfRZpOgt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\nAACUjIAQAAAAQMn8H4RErQIgBinpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XGf3nMDrK6dS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}