Code_Sentiment_Analysis.ipynb

{
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  "metadata": {
    "colab": {
      "name": "NLP A3_Finall99.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXtjh_vCKVuz"
      },
      "source": [
        "## IMPORTS\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtP9JkcfLVqd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f81205a4-2bff-48f2-f85f-ec590aa6848f"
      },
      "source": [
        "# imports\n",
        "\n",
        "import sys\n",
        "import re\n",
        "import math\n",
        "import requests\n",
        "import xml.etree.ElementTree as ET\n",
        "import pandas as pd\n",
        "import operator\n",
        "from math import sqrt\n",
        "from statistics import mean\n",
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "\n",
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "import nltk\n",
        "from nltk import stem  \n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.corpus import wordnet\n",
        "\n",
        "import collections\n",
        "from collections import Counter\n",
        "import itertools\n",
        "from itertools import islice\n",
        "from itertools import chain\n",
        "import pickle\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np \n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "\n",
        "import sklearn\n",
        "from sklearn import metrics\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Embedding\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing import text, sequence\n",
        "from keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation\n",
        "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
        "from tensorflow.keras.layers import Conv1D, GlobalMaxPooling1D, MaxPooling1D\n",
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "\n",
        "import random\n",
        "seed_value = 1\n",
        "random.seed(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "tf.random.set_seed(seed_value)\n",
        "\n",
        "# nltk downloads\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('averaged_perceptron_tagger')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[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 wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet 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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1Wy0BQ0KcuK"
      },
      "source": [
        "## LOAD AND EXTRACT THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0wU219k5G9T"
      },
      "source": [
        "# function to load data from xml file and return total no of reviews,review text,aspect term and aspect term polarity\n",
        "def processXML(file):\n",
        "\n",
        "  # get root\n",
        "  root = ET.parse(file).getroot()\n",
        "\n",
        "  id = []\n",
        "  term = {}     # Review wise aspect term\n",
        "  polarity = {} # Polarities for a review\n",
        "  review_text = []\n",
        "\n",
        "  for item in root.findall('sentence'):    \n",
        "    id_val = item.get('id')\n",
        "    id.append(id_val)\n",
        "\n",
        "  total_no_reviews = len(id) \n",
        " \n",
        "  #  <review text>\n",
        "  for i in range(0,total_no_reviews):\n",
        "    review_text.append(root[i][0].text)\n",
        "\n",
        "  #  <aspect term>\n",
        "  for i in range (0, total_no_reviews):\n",
        "    aspect_terms = root[i][1]\n",
        "\n",
        "    # get term and polarity\n",
        "    for item in aspect_terms.findall('aspectTerm'):\n",
        "          term_val = item.get('term')\n",
        "          polarity_val = item.get('polarity')\n",
        "        \n",
        "          if i not in term:\n",
        "            term[i] = [term_val]\n",
        "            polarity[i] = [polarity_val]\n",
        "            \n",
        "          else:\n",
        "            term[i].append( term_val)\n",
        "            polarity[i] .append(polarity_val)\n",
        "  \n",
        "  return total_no_reviews,review_text,term,polarity;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "by71fEsfwfZp",
        "outputId": "fb011c55-e238-4694-d524-4330c77f6085"
      },
      "source": [
        "# load and extract training data\n",
        "total_no_reviews,review_text,term,polarity = processXML('Restaurants_Train.xml')\n",
        "\n",
        "print(\"Total no, of reviews: \",total_no_reviews)\n",
        "print(\"Reviews containing aspect term: \", len(term))\n",
        "print(\"Some reviews: \",review_text[0:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total no, of reviews:  3041\n",
            "Reviews containing aspect term:  2021\n",
            "Some reviews:  ['But the staff was so horrible to us.', \"To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora.\", \"The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.\", 'Where Gabriela personaly greets you and recommends you what to eat.', \"For those that go once and don't enjoy it, all I can say is that they just don't get it.\"]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv2-dchMwvs9",
        "outputId": "961e063c-7318-4a4e-ba06-93f9f88bc84e"
      },
      "source": [
        "# load and extract testing data\n",
        "total_no_reviews_test,review_text_test,term_test,polarity_test = processXML('Restaurants_Test_Gold.xml')\n",
        "\n",
        "print(\"Total no, of reviews: \",total_no_reviews_test)\n",
        "print(\"Reviews containing aspect term: \", len(term_test))\n",
        "print(\"Some reviews: \",review_text_test[0:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total no, of reviews:  800\n",
            "Reviews containing aspect term:  606\n",
            "Some reviews:  ['The bread is top notch as well.', 'I have to say they have one of the fastest delivery times in the city.', 'Food is always fresh and hot- ready to eat!', 'Did I mention that the coffee is OUTSTANDING?', 'Certainly not the best sushi in New York, however, it is always fresh, and the place is very clean, sterile.']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDbSJsHFcStG"
      },
      "source": [
        "# function to return only unique words in a sentence\n",
        "def unique_list(line):\n",
        "  \n",
        "    ulist = []\n",
        "    [ulist.append(x) for x in line if x not in ulist]\n",
        "    return ulist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6gqKaEiUfLa"
      },
      "source": [
        "# function to extract relevant info with dependency parsing and return a dict of text and polarity for a review\n",
        "def getDictData(term, review_text, polarity): \n",
        "\n",
        "  index_list = []    # store index of review \n",
        "  descript_and_polarity = {} # dict to store the final description and its polarity\n",
        "\n",
        "  for key,val in term.items():\n",
        "    index_list.append(key)\n",
        "\n",
        "  for i in index_list: # for each index\n",
        "\n",
        "    review = review_text[i]\n",
        "    aspect_term_list = term[i]\n",
        "    polar_list = polarity[i]\n",
        "    doc = nlp(review)        # spacy\n",
        "    \n",
        "    for k,item in enumerate(aspect_term_list):  \n",
        "      single_term = len(item.split())      # get a single aspect terms in a review\n",
        "\n",
        "      if  single_term > 1:                 # if aspect term contains more than one word, keep the first word\n",
        "        item_ = item.split(' ', 1)[0]\n",
        "        aspect_term_list[k] = item_       # change the orginal term in the list\n",
        "\n",
        "    j = 0\n",
        "    word_type = []\n",
        "    description = ' '\n",
        "    descript_dict = {}\n",
        "\n",
        "    for token in doc:                       # for each word in review\n",
        "        if token.text in aspect_term_list:  # if word is an aspect term\n",
        "\n",
        "            aspect_item = token.text\n",
        "            head = token.head\n",
        "\n",
        "            head_child = head.children     # get all child text of head\n",
        "            token_child = token.children   # get all child text of token\n",
        "\n",
        "            for child_ in  token.children:\n",
        "              description = description + \" \" + str(child_) # add child text of token to description\n",
        "            \n",
        "            for child in head_child:\n",
        "              child = str(child)\n",
        "\n",
        "              if j not in  descript_dict:\n",
        "                 descript_dict[j] = [child] # store child text in a dict as list\n",
        "              else:\n",
        "                 descript_dict[j].append(child)\n",
        "\n",
        "            descript_dict[j].append(description)  # store child text of both token and head for an aspect term\n",
        "\n",
        "            j+=1 \n",
        "            description = ' '   \n",
        "\n",
        "    descript_joined = []\n",
        "\n",
        "    for key,val in  descript_dict.items(): # combine the words stored in list to form a single text sentence\n",
        "      value = ' '.join(val)\n",
        "      value = ' '.join(unique_list(value.split())) # remove repeated words\n",
        "      descript_joined.append(value) \n",
        "\n",
        "    for key,val in zip(polar_list, descript_joined): \n",
        "       descript_and_polarity[val] = key   # store description and polarity in a dict\n",
        "\n",
        "  return descript_and_polarity"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCp4oKsIFB49",
        "outputId": "fc7866f2-788b-426b-9323-2d55a750c6ec"
      },
      "source": [
        "all_data_train = getDictData(term,review_text,polarity)\n",
        "print(len(all_data_train)) \n",
        "\n",
        "all_data_test = getDictData(term_test,review_text_test,polarity_test)\n",
        "len(all_data_test) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3327\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1057"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kj1pKgYAX--E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e5cc5e4-da47-41f9-b534-023e78d2b27c"
      },
      "source": [
        "# training dataframe\n",
        "df_train = pd.DataFrame(list(all_data_train.items()),columns = ['text','sentiment']) \n",
        "print(\"Training data shape: \",df_train.shape) \n",
        "\n",
        "# testing dataframe\n",
        "df_test = pd.DataFrame(list(all_data_test.items()),columns = ['text','sentiment']) \n",
        "print(\"Testing data shape: \", df_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data shape:  (3327, 2)\n",
            "Testing data shape:  (1057, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bno8SKgPpowy",
        "outputId": "b5a07919-1000-41b2-8fd7-7583d8e7b547"
      },
      "source": [
        "df_train = df_train[df_train.sentiment != \"conflict\"]\n",
        "df_test = df_test[df_test.sentiment != \"conflict\"]\n",
        "\n",
        "print(\"Training data shape: \",df_train.shape) \n",
        "print(\"Testing data shape: \", df_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data shape:  (3239, 2)\n",
            "Testing data shape:  (1044, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "aPU93z3WUqXH",
        "outputId": "54c450af-2370-46c1-f4d4-f5fe27b6e163"
      },
      "source": [
        "df_train[0:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>But staff horrible . the</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>be , factor food but make . the was</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>food exceptional , with 's . The</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>kitchen a capable whip</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>menu the</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Not food , but were the outstanding</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>perks great . little '</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>favorite orrechiete the with</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>( usually waiters enough the</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>to dish in the</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  text sentiment\n",
              "0             But staff horrible . the  negative\n",
              "1  be , factor food but make . the was  positive\n",
              "2     food exceptional , with 's . The  positive\n",
              "3               kitchen a capable whip  positive\n",
              "4                             menu the   neutral\n",
              "5  Not food , but were the outstanding  positive\n",
              "6               perks great . little '  positive\n",
              "7         favorite orrechiete the with  positive\n",
              "8         ( usually waiters enough the  positive\n",
              "9                       to dish in the   neutral"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "Y-7QM2yWU5aU",
        "outputId": "753cf01b-05ca-4715-f5e3-4d57f703ffbe"
      },
      "source": [
        "df_test[0:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bread notch well . The</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the fastest delivery in</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Food always fresh and hot- !</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>that coffee OUTSTANDING the</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>place clean , sterile . the</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>I people at the</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>decent Japanese</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>BEST spicy tuna , salad .</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>great asian</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>the rose ( on )</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            text sentiment\n",
              "0         bread notch well . The  positive\n",
              "1        the fastest delivery in  positive\n",
              "2   Food always fresh and hot- !  positive\n",
              "3    that coffee OUTSTANDING the  positive\n",
              "5    place clean , sterile . the  positive\n",
              "6                I people at the  positive\n",
              "7                decent Japanese  positive\n",
              "8      BEST spicy tuna , salad .  positive\n",
              "9                    great asian  positive\n",
              "10               the rose ( on )  positive"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-CC7chW9oEk"
      },
      "source": [
        "## PREPROCESS THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sjk99fcpbJy6"
      },
      "source": [
        "# function to remove puntuation\n",
        "def remove_puntuation(text):\n",
        "    text = [word for word in text if word.isalpha()] #  remove all puntuation and numbers\n",
        "    return text \n",
        "\n",
        "# function to remove stop word from text\n",
        "def remove_stopwords(text):\n",
        "    stop_words = list(stopwords.words('english')) # store nltk stopwords\n",
        "    text = [word for word in text if not word in stop_words] \n",
        "    return text \n",
        "\n",
        "# function to perform lemmatization\n",
        "def lemmatization (text):\n",
        "    lemmatizer = stem.WordNetLemmatizer() \n",
        "    text = [lemmatizer.lemmatize(token) for token in text]\n",
        "    return text \n",
        "\n",
        "\n",
        "# function to perform text preprocessing (lemmatization and no stemming)\n",
        "def clean_corpus(text):\n",
        "    \n",
        "    text = text.lower()                # lower case  \n",
        "    token = word_tokenize(text)         # tokenise\n",
        "  \n",
        "    token = remove_stopwords(token)    # remove stopwords\n",
        "    token = remove_puntuation(token)   # remove puntuation \n",
        "    token = lemmatization(token)       # lemmatize\n",
        "\n",
        "    all_tokens = \" \".join(token)       # join all tokens\n",
        "\n",
        "    return all_tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QO38Fw2b3fK"
      },
      "source": [
        "# preprocess training data\n",
        "df_train['processed_text'] = df_train.text.apply(lambda x: clean_corpus(x))\n",
        "\n",
        "# preprocess test data\n",
        "df_test['processed_text'] = df_test.text.apply(lambda x: clean_corpus(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4axzjWrq-mA",
        "outputId": "155ffd2e-d27b-4829-c7f1-34e0de82ea22"
      },
      "source": [
        "# remove duplicated sentences\n",
        "df_train = df_train.drop_duplicates(subset = 'processed_text' , keep = \"first\")\n",
        "df_test = df_test.drop_duplicates(subset = 'processed_text' , keep = \"first\")\n",
        "\n",
        "print(\"Training data shape: \",df_train.shape) \n",
        "print(\"Testing data shape: \", df_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data shape:  (2531, 3)\n",
            "Testing data shape:  (882, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvCg4u578ukX",
        "outputId": "5f73a27c-1415-480b-889d-1f53ce72ac5b"
      },
      "source": [
        "# remove sentences with only one word, as they do not have any sentiment description term\n",
        "length_text = []\n",
        "length_text_t = []\n",
        "\n",
        "for text in df_train['processed_text']:\n",
        "  length_text.append(len(text.split()))\n",
        "\n",
        "for text in df_test['processed_text']:\n",
        "  length_text_t.append(len(text.split()))\n",
        "\n",
        "df_train['len_text'] = length_text\n",
        "df_test['len_text'] = length_text_t\n",
        "\n",
        "df_train = df_train[df_train.len_text != 1]\n",
        "df_test = df_test[df_test.len_text != 1]\n",
        "\n",
        "print(df_train.shape)\n",
        "print(df_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2136, 4)\n",
            "(691, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyNl-Vowkia1"
      },
      "source": [
        "df_train = df_train[['text', 'processed_text', 'len_text', 'sentiment']]\n",
        "df_test = df_test[['text', 'processed_text', 'len_text', 'sentiment']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "K95Lm1gGrTPk",
        "outputId": "91db0b42-00b7-4418-d13d-70dae44c46f7"
      },
      "source": [
        "df_train[0:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>text</th>\n",
              "      <th>processed_text</th>\n",
              "      <th>len_text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>But staff horrible . the</td>\n",
              "      <td>staff horrible</td>\n",
              "      <td>2</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>be , factor food but make . the was</td>\n",
              "      <td>factor food make</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>food exceptional , with 's . The</td>\n",
              "      <td>food exceptional</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>kitchen a capable whip</td>\n",
              "      <td>kitchen capable whip</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Not food , but were the outstanding</td>\n",
              "      <td>food outstanding</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>perks great . little '</td>\n",
              "      <td>perk great little</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>favorite orrechiete the with</td>\n",
              "      <td>favorite orrechiete</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>( usually waiters enough the</td>\n",
              "      <td>usually waiter enough</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Bagels taste , gummy . The</td>\n",
              "      <td>bagel taste gummy</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Nevertheless food good . the itself</td>\n",
              "      <td>nevertheless food good</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   text  ... sentiment\n",
              "0              But staff horrible . the  ...  negative\n",
              "1   be , factor food but make . the was  ...  positive\n",
              "2      food exceptional , with 's . The  ...  positive\n",
              "3                kitchen a capable whip  ...  positive\n",
              "5   Not food , but were the outstanding  ...  positive\n",
              "6                perks great . little '  ...  positive\n",
              "7          favorite orrechiete the with  ...  positive\n",
              "8          ( usually waiters enough the  ...  positive\n",
              "11           Bagels taste , gummy . The  ...  positive\n",
              "12  Nevertheless food good . the itself  ...  positive\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "vutR405s50Mo",
        "outputId": "7cea1a2c-5f06-4437-b94b-4135c84b1700"
      },
      "source": [
        "df_test[0:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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>text</th>\n",
              "      <th>processed_text</th>\n",
              "      <th>len_text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bread notch well . The</td>\n",
              "      <td>bread notch well</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the fastest delivery in</td>\n",
              "      <td>fastest delivery</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Food always fresh and hot- !</td>\n",
              "      <td>food always fresh</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>that coffee OUTSTANDING the</td>\n",
              "      <td>coffee outstanding</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>place clean , sterile . the</td>\n",
              "      <td>place clean sterile</td>\n",
              "      <td>3</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>decent Japanese</td>\n",
              "      <td>decent japanese</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>BEST spicy tuna , salad .</td>\n",
              "      <td>best spicy tuna salad</td>\n",
              "      <td>4</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>great asian</td>\n",
              "      <td>great asian</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>I drinks is the , martini</td>\n",
              "      <td>drink martini</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>esp lychee , and</td>\n",
              "      <td>esp lychee</td>\n",
              "      <td>2</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            text         processed_text  len_text sentiment\n",
              "0         bread notch well . The       bread notch well         3  positive\n",
              "1        the fastest delivery in       fastest delivery         2  positive\n",
              "2   Food always fresh and hot- !      food always fresh         3  positive\n",
              "3    that coffee OUTSTANDING the     coffee outstanding         2  positive\n",
              "5    place clean , sterile . the    place clean sterile         3  positive\n",
              "7                decent Japanese        decent japanese         2  positive\n",
              "8      BEST spicy tuna , salad .  best spicy tuna salad         4  positive\n",
              "9                    great asian            great asian         2  positive\n",
              "12     I drinks is the , martini          drink martini         2  positive\n",
              "13              esp lychee , and             esp lychee         2  positive"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkePbQ_xcIHM"
      },
      "source": [
        "# function to get word count of a dataset\n",
        "def word_count(dataset):\n",
        "    \n",
        "    unique_words = [] \n",
        "    \n",
        "    for row in dataset:\n",
        "        for word in row.split(\" \"):\n",
        "            if len(word) > 2 and word not in unique_words:\n",
        "                unique_words.append(word) # Add each unique word of length > 2 to the list\n",
        "            \n",
        "    unique_words.sort()\n",
        "    vocab = {j:i for i,j in enumerate(unique_words)} # Enumerate the list, i.e., give consecutive numbers to each item, store in a dict\n",
        "    return vocab"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6ytjIlAcWCG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3aa241d-73a9-4dd3-d57c-8cf0fa3b71b8"
      },
      "source": [
        "#total number of unique words in training set\n",
        "unique_words_ques2 = word_count(df_train['processed_text'])\n",
        "print(\"Number of unique words in processed training text: \",len(unique_words_ques2))  \n",
        "\n",
        "unique_words_ = word_count(df_train['text'])\n",
        "print(\"Number of unique words in original training text: \",len(unique_words_))   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of unique words in processed training text:  1521\n",
            "Number of unique words in original training text:  1931\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "jEY1TDBNv8-D",
        "outputId": "d997344e-dd7a-43e8-da59-b528a9151fe5"
      },
      "source": [
        "# plot distribution of label in training data\n",
        "df_train['sentiment'].hist(edgecolor = 'black',bins=5,facecolor = 'teal')\n",
        "plt.suptitle(\"Histogram for Labels\",size = 14)\n",
        "plt.xlabel(\"Label\")\n",
        "plt.ylabel(\"Number of reviews\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "U3WXhVs0wTD0",
        "outputId": "46351f2b-c803-48c0-aa34-8875a2c7376a"
      },
      "source": [
        "# plot distribution of label in test data\n",
        "df_test['sentiment'].hist(edgecolor = 'black',bins=5,facecolor = 'teal')\n",
        "plt.suptitle(\"Histogram for Labels\",size = 14)\n",
        "plt.xlabel(\"Label\")\n",
        "plt.ylabel(\"Number of reviews\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMChzCyu17ZA"
      },
      "source": [
        "## TFIDF AND SOME CLASSIFIERS (Logistic Regression, Multinomial Naive Bayes & Support Vector Classifier)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LksgE2ogiAL"
      },
      "source": [
        "y_test = df_test['sentiment']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1rsW6zF8n6f"
      },
      "source": [
        "- TFIDF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxjzTs_vcdU-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69998719-3131-4b03-bf72-89e5e76b3dcb"
      },
      "source": [
        "# TF-IDF transformation\n",
        "vectorizer = TfidfVectorizer()\n",
        "vectorizer.fit(df_train['processed_text'])\n",
        "\n",
        "x_train_tfidf = vectorizer.transform(df_train['processed_text'])\n",
        "x_test_tfidf = vectorizer.transform(df_test['processed_text'])\n",
        "\n",
        "print(vectorizer.get_feature_names())\n",
        "\n",
        "print(\"Training feature shape\", x_train_tfidf.shape)\n",
        "print(\"Testing feature shape\", x_test_tfidf .shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['aanother', 'absolutely', 'ac', 'accomodate', 'accomodating', 'accompaniment', 'actor', 'actually', 'add', 'added', 'addition', 'additional', 'adequate', 'adopted', 'adventurous', 'advice', 'aesthetic', 'affected', 'affordable', 'afortune', 'ago', 'agreable', 'ahead', 'air', 'ala', 'alain', 'allow', 'alluring', 'almost', 'alone', 'along', 'also', 'although', 'always', 'amanzing', 'amazin', 'amazing', 'ambiance', 'ambience', 'ambitious', 'american', 'americanized', 'amiable', 'amid', 'among', 'amount', 'amuse', 'angry', 'annoying', 'annual', 'another', 'antipasti', 'anything', 'anyway', 'anywhere', 'apart', 'apology', 'app', 'apparently', 'appealing', 'appetizer', 'appetizing', 'apple', 'appointment', 'apps', 'ar', 'architecture', 'area', 'around', 'arrived', 'as', 'asian', 'aside', 'ask', 'asked', 'assorted', 'assortment', 'ate', 'atlhough', 'atmosphere', 'atomosphere', 'atrocious', 'attend', 'attentive', 'attest', 'attitude', 'attract', 'attraction', 'attractive', 'au', 'aunthentic', 'authentic', 'authentically', 'available', 'average', 'avocado', 'away', 'aweful', 'awesome', 'awful', 'awsome', 'back', 'bacon', 'bacos', 'bad', 'bagel', 'bagles', 'baked', 'baluchi', 'bar', 'bartender', 'basic', 'basically', 'basil', 'basketful', 'basmati', 'bass', 'bathroom', 'bbq', 'bean', 'beat', 'beatiful', 'beautiful', 'beef', 'beer', 'beginning', 'behind', 'berate', 'besides', 'best', 'better', 'beverage', 'beyond', 'bhelpuri', 'big', 'bill', 'bindi', 'birthday', 'bistro', 'bit', 'bitter', 'black', 'blah', 'bland', 'blantently', 'blend', 'blond', 'blue', 'bombay', 'boring', 'botle', 'boton', 'bottle', 'box', 'boxed', 'boyfriend', 'brassiere', 'bread', 'breathe', 'brilliant', 'bring', 'brings', 'broth', 'brulee', 'brunch', 'bruscetta', 'bruschetta', 'bruschettas', 'buffet', 'building', 'bummer', 'bun', 'burger', 'burnt', 'business', 'bustling', 'busy', 'butter', 'buttery', 'ca', 'cabaret', 'cafe', 'cake', 'cakebread', 'calamari', 'call', 'called', 'calm', 'calzones', 'came', 'candle', 'candlevery', 'canned', 'cannoli', 'capable', 'caprese', 'care', 'cared', 'carte', 'case', 'caserole', 'casual', 'caviar', 'ceiling', 'certain', 'certainly', 'ceviche', 'chaat', 'chaats', 'chai', 'champagne', 'chance', 'changed', 'changing', 'character', 'charge', 'charged', 'charming', 'cheap', 'cheapest', 'check', 'checked', 'cheescake', 'cheese', 'cheeseburger', 'chef', 'chic', 'chicken', 'chilean', 'chill', 'chilled', 'chinese', 'chip', 'chocolate', 'choice', 'chole', 'chongqing', 'choose', 'chop', 'chopped', 'chosen', 'christmas', 'cigar', 'city', 'claimed', 'clam', 'class', 'classic', 'classical', 'classy', 'clean', 'cleaned', 'clear', 'clearly', 'client', 'closely', 'closing', 'club', 'clubhouse', 'clumsy', 'coat', 'cobb', 'cocktail', 'cod', 'coem', 'coffee', 'coke', 'cold', 'cole', 'colorful', 'combination', 'come', 'comfort', 'comfortable', 'comforting', 'coming', 'common', 'companion', 'company', 'comparable', 'compared', 'compensate', 'competent', 'complain', 'complementary', 'completely', 'complimentary', 'comprised', 'congee', 'consequently', 'consider', 'considering', 'consisent', 'consistant', 'consisted', 'consistent', 'consistently', 'consomme', 'container', 'convenient', 'conveniently', 'conversation', 'cook', 'cooked', 'cooking', 'cool', 'corned', 'corner', 'correct', 'corridor', 'cosmopolitan', 'cost', 'could', 'counter', 'couple', 'course', 'courteous', 'cover', 'cozy', 'crab', 'crackling', 'crammed', 'cramped', 'crappy', 'cream', 'creative', 'crew', 'crispy', 'crowd', 'crowded', 'crust', 'crusted', 'cube', 'cuisine', 'curry', 'customer', 'cute', 'dainty', 'dal', 'dance', 'dark', 'date', 'day', 'dazzling', 'de', 'deal', 'decent', 'decor', 'deep', 'deff', 'definitely', 'delcious', 'deli', 'delicate', 'delicious', 'delicous', 'delightful', 'deliver', 'delivered', 'delivery', 'descent', 'described', 'desert', 'deserve', 'deserves', 'design', 'despite', 'dessert', 'devine', 'dhal', 'dhosas', 'diamond', 'different', 'dim', 'dimsum', 'dine', 'diner', 'dining', 'dinner', 'dinosaur', 'disapointing', 'disappointed', 'disappointing', 'disappointment', 'dish', 'disney', 'display', 'dissapointing', 'diverse', 'dollar', 'done', 'donut', 'door', 'dosas', 'dough', 'doughy', 'downside', 'drafty', 'drawback', 'dress', 'dressed', 'drink', 'drinking', 'drunken', 'dry', 'duck', 'dulce', 'dumpling', 'early', 'ease', 'easily', 'east', 'easy', 'eat', 'eaten', 'eating', 'eats', 'eel', 'efficiently', 'egg', 'eggplant', 'either', 'elegant', 'embracing', 'employee', 'empty', 'encouraged', 'ended', 'england', 'english', 'enjoy', 'enjoyable', 'enjoyed', 'enjoying', 'enough', 'entertainment', 'enthusiastic', 'entire', 'entree', 'environment', 'escargot', 'esp', 'especially', 'espresso', 'establishment', 'etc', 'ethnic', 'even', 'ever', 'every', 'everything', 'everywhere', 'evil', 'ew', 'example', 'excellent', 'except', 'exceptional', 'exciting', 'existant', 'exorbitant', 'exotic', 'expanded', 'expect', 'expectation', 'expected', 'expecting', 'expensive', 'experiance', 'experience', 'explained', 'expresso', 'extensive', 'extra', 'extraordinary', 'fabulous', 'faced', 'fact', 'factor', 'fair', 'faired', 'fairly', 'fake', 'fallback', 'family', 'famous', 'fan', 'fancy', 'fantastic', 'far', 'fare', 'fast', 'fastest', 'fatty', 'favorite', 'favourite', 'feat', 'feature', 'featuring', 'feel', 'feeling', 'felt', 'fiance', 'filet', 'fill', 'filled', 'fillet', 'filling', 'finally', 'find', 'fine', 'finish', 'finished', 'first', 'fish', 'five', 'fixe', 'flashy', 'flavor', 'flavorless', 'flour', 'flower', 'flowing', 'fondue', 'food', 'forever', 'forget', 'forgettable', 'forgiven', 'forgot', 'four', 'francois', 'free', 'french', 'frequently', 'fresh', 'fresher', 'freshest', 'freshness', 'freshwater', 'fried', 'friend', 'friendlier', 'friendliest', 'friendly', 'frites', 'fromager', 'frozen', 'fruit', 'fry', 'full', 'fully', 'fun', 'funky', 'furthermore', 'fusion', 'garden', 'garlic', 'gaskin', 'gave', 'general', 'generation', 'generic', 'genuinely', 'get', 'getting', 'ghetto', 'gigantic', 'gigondas', 'gimmick', 'gin', 'ginger', 'girl', 'give', 'given', 'glass', 'glazed', 'glorified', 'gnochi', 'go', 'goat', 'godmother', 'going', 'good', 'gorgeous', 'got', 'grab', 'grace', 'gracious', 'grade', 'grape', 'grass', 'great', 'greatest', 'greatly', 'green', 'greet', 'greeted', 'gret', 'grilled', 'group', 'growing', 'guest', 'guided', 'guizhou', 'gulab', 'gummy', 'guy', 'half', 'halibut', 'hall', 'hang', 'hangout', 'happy', 'hardcore', 'haru', 'healthy', 'heaped', 'heart', 'hearty', 'heated', 'heavy', 'held', 'help', 'helpful', 'herb', 'hey', 'hidden', 'high', 'higher', 'highlight', 'highlighting', 'hip', 'hissed', 'hit', 'hold', 'holding', 'homemade', 'honestly', 'hoped', 'horedevous', 'horrendous', 'horrible', 'horrific', 'hospitable', 'host', 'hostess', 'hosting', 'hot', 'hottest', 'hour', 'house', 'however', 'huge', 'humble', 'hurt', 'husband', 'ice', 'icing', 'icy', 'idea', 'ie', 'ignore', 'ignored', 'imagination', 'immediately', 'impecable', 'impeccable', 'impecible', 'important', 'importantly', 'imported', 'imposing', 'impress', 'impressive', 'include', 'included', 'including', 'incompetent', 'inconsistency', 'inconsistent', 'incredible', 'indian', 'indoor', 'inedible', 'inexpensive', 'infused', 'ingrediants', 'ingredient', 'innovative', 'inside', 'inspired', 'instead', 'integral', 'interesting', 'interior', 'intimate', 'intitally', 'inventive', 'ish', 'italian', 'item', 'itsuperb', 'izakaya', 'japanese', 'jason', 'jazz', 'jelly', 'juice', 'juicy', 'jukebox', 'kalbi', 'kamasutra', 'kamikaze', 'kebab', 'keep', 'keeping', 'kenny', 'kielbasa', 'kind', 'kinda', 'kitchen', 'knish', 'know', 'knowledge', 'knowledgeable', 'known', 'korma', 'kruno', 'lack', 'lacking', 'lad', 'lady', 'lamb', 'lambchop', 'large', 'larger', 'largest', 'lassi', 'late', 'lava', 'lawn', 'laying', 'le', 'leaf', 'leaping', 'least', 'leave', 'leche', 'left', 'leftover', 'lemon', 'let', 'lettuce', 'ley', 'lie', 'life', 'light', 'like', 'lime', 'limited', 'line', 'list', 'lit', 'little', 'live', 'loaded', 'lobby', 'lobster', 'local', 'location', 'loin', 'long', 'look', 'looked', 'looking', 'lot', 'lotus', 'loud', 'lousy', 'love', 'lovely', 'lover', 'low', 'lox', 'lt', 'lunch', 'lyle', 'made', 'magnificant', 'mahi', 'mai', 'main', 'make', 'makhani', 'mamoun', 'man', 'management', 'manager', 'manna', 'mannered', 'many', 'margarita', 'margarite', 'margheritta', 'martini', 'masaman', 'mashed', 'match', 'matter', 'may', 'maybe', 'mayo', 'mayonaisse', 'mayonnaise', 'maze', 'meal', 'meanwhile', 'measure', 'meat', 'meatball', 'mediocre', 'mediterranean', 'medium', 'mein', 'melted', 'member', 'memorable', 'mention', 'mentioned', 'menu', 'mesclun', 'metrazur', 'might', 'mignon', 'mild', 'mill', 'mimosa', 'mind', 'mini', 'minimal', 'minimalist', 'minute', 'mix', 'mixed', 'moderate', 'modern', 'money', 'mortal', 'mostly', 'moules', 'mozz', 'mozzarella', 'mozzerella', 'msg', 'much', 'mud', 'mulino', 'mushroom', 'music', 'mussaman', 'mussel', 'must', 'myriad', 'naan', 'name', 'narrow', 'nasty', 'neapolitan', 'nearby', 'nearing', 'nebbiolo', 'neighborhood', 'never', 'nevertheless', 'new', 'next', 'nice', 'nicest', 'night', 'nightcap', 'nigiri', 'nina', 'nite', 'noir', 'noise', 'noisy', 'nonchalant', 'nonsense', 'noodle', 'nori', 'nosh', 'notch', 'nothing', 'nyc', 'oatmeal', 'obnoxious', 'obv', 'obviously', 'odd', 'offer', 'offered', 'offeres', 'offering', 'often', 'oil', 'oily', 'ok', 'okay', 'okra', 'old', 'olive', 'omelletes', 'one', 'onglet', 'onion', 'oozing', 'open', 'opened', 'opening', 'opposite', 'order', 'ordered', 'ordering', 'organic', 'orrechiete', 'ot', 'otherwise', 'otherworldly', 'outdoor', 'outragous', 'outshine', 'outside', 'outstanding', 'overall', 'overbearing', 'overcooked', 'overdone', 'overdue', 'overlooking', 'overpowering', 'overpriced', 'overwhelmed', 'ow', 'owner', 'oyster', 'oz', 'pack', 'packed', 'pad', 'page', 'paid', 'painted', 'pakistani', 'palate', 'panchetta', 'panini', 'paratha', 'parathas', 'parched', 'parmigana', 'part', 'passed', 'pasta', 'pastrami', 'pastry', 'pathetic', 'patient', 'patio', 'pay', 'peaceful', 'pelligrino', 'people', 'pepper', 'per', 'percent', 'perfect', 'perk', 'person', 'personally', 'pesto', 'peter', 'phenomenal', 'pho', 'phone', 'photobook', 'pick', 'pickels', 'pickle', 'picture', 'pie', 'piece', 'piled', 'pink', 'piped', 'piping', 'pistachio', 'pita', 'pizza', 'pizzeria', 'place', 'plain', 'plate', 'platter', 'playing', 'pleasant', 'pleasurable', 'plentiful', 'plenty', 'plus', 'point', 'poivre', 'poor', 'poorly', 'popcorn', 'popular', 'porcini', 'pork', 'portioins', 'portion', 'portrait', 'pot', 'potato', 'potential', 'pound', 'pour', 'poured', 'precise', 'precisely', 'premium', 'prepared', 'present', 'presentaion', 'presentation', 'presented', 'pretty', 'previous', 'price', 'priced', 'pricey', 'prime', 'primo', 'prior', 'privacy', 'prix', 'probably', 'proceeded', 'professional', 'promised', 'prompt', 'promptly', 'proper', 'proportioned', 'proprietor', 'pub', 'pudding', 'puff', 'puke', 'pulled', 'put', 'quail', 'quality', 'quantity', 'quick', 'quickly', 'quiet', 'quite', 'raddichio', 'rao', 'rarity', 'rasamalai', 'raucus', 'ravioli', 'raw', 'ready', 'real', 'reallocating', 'really', 'reasonable', 'reasonably', 'received', 'recently', 'recommend', 'red', 'refined', 'refleshment', 'refreshing', 'regular', 'rejoice', 'relax', 'relaxed', 'relaxing', 'reliable', 'remained', 'remember', 'replaced', 'reputation', 'requested', 'resembled', 'reservation', 'rest', 'restaurant', 'return', 'reuben', 'review', 'rice', 'rich', 'ridding', 'right', 'ripped', 'roast', 'roasted', 'roll', 'romantic', 'room', 'root', 'rose', 'roth', 'roti', 'round', 'rude', 'run', 'running', 'rush', 'rushed', 'rushing', 'rye', 'said', 'sake', 'salad', 'salmon', 'salty', 'samosa', 'sampler', 'sampling', 'sandwich', 'sangria', 'sapphire', 'sara', 'sardine', 'sashimi', 'sat', 'sauce', 'saul', 'save', 'saving', 'saw', 'say', 'scallion', 'scallop', 'scatty', 'scene', 'scenery', 'scent', 'scrumptious', 'se', 'sea', 'seafood', 'seasonal', 'seasoning', 'seat', 'seated', 'seating', 'second', 'secondo', 'secret', 'see', 'seem', 'seen', 'selecion', 'selection', 'sell', 'sensual', 'sent', 'serf', 'serious', 'servant', 'serve', 'served', 'server', 'service', 'serving', 'set', 'setting', 'seved', 'several', 'sevpuri', 'shabu', 'shanghai', 'share', 'sharing', 'shell', 'shining', 'short', 'shout', 'show', 'showed', 'shredded', 'shrimp', 'shut', 'sichuan', 'sicilian', 'side', 'sign', 'similar', 'similarly', 'simple', 'simply', 'since', 'singapore', 'sit', 'sitting', 'size', 'sized', 'sketchy', 'skimp', 'skimpy', 'slaw', 'sleek', 'slice', 'sliced', 'slighly', 'slightly', 'slow', 'small', 'smaller', 'smile', 'smoked', 'snacking', 'snobby', 'soft', 'soggy', 'solid', 'someone', 'something', 'sometimes', 'sommelier', 'sommlier', 'somosas', 'son', 'soon', 'sort', 'sound', 'soup', 'sour', 'south', 'southern', 'soy', 'space', 'spaghetti', 'spare', 'speak', 'speaking', 'special', 'specialty', 'specjal', 'spectacular', 'speedy', 'spend', 'spent', 'spice', 'spiced', 'spicy', 'spilled', 'spinach', 'split', 'spot', 'spotty', 'spread', 'spring', 'sprinkled', 'square', 'squid', 'staff', 'stale', 'standard', 'staple', 'star', 'start', 'started', 'starter', 'stauff', 'stay', 'steak', 'steep', 'steven', 'stick', 'still', 'stir', 'stocked', 'stomach', 'straight', 'strawberry', 'strict', 'strike', 'strong', 'stuff', 'stuffing', 'style', 'styled', 'subpar', 'substantial', 'subtle', 'succulent', 'suggested', 'suggestion', 'sum', 'summer', 'sunken', 'super', 'superb', 'suppose', 'supposed', 'sure', 'surprise', 'surprising', 'surrounding', 'sushi', 'svc', 'sweet', 'swirl', 'swordfish', 'table', 'tail', 'taiwanese', 'take', 'takeout', 'tal', 'talk', 'tandoori', 'tank', 'tao', 'tapa', 'tarte', 'taste', 'tasted', 'tastier', 'tasting', 'tasty', 'tea', 'teapot', 'technique', 'tell', 'tempura', 'tender', 'tends', 'term', 'terrace', 'terrible', 'terrific', 'test', 'texture', 'thai', 'thali', 'thank', 'theatre', 'thh', 'thier', 'thin', 'thing', 'thinking', 'thinner', 'third', 'thoroughly', 'though', 'thought', 'three', 'tight', 'tikka', 'tiled', 'till', 'time', 'timely', 'tiny', 'tip', 'tiramisu', 'toast', 'toaster', 'today', 'tofu', 'together', 'told', 'tom', 'tomato', 'ton', 'tone', 'tonic', 'took', 'top', 'topping', 'torro', 'total', 'totally', 'touch', 'toward', 'tower', 'tradional', 'traditional', 'traffic', 'trained', 'tramezzinis', 'translucent', 'treasure', 'treated', 'treatment', 'trendy', 'tried', 'trout', 'truly', 'try', 'trying', 'tuesday', 'tuna', 'turn', 'turned', 'turnip', 'twice', 'two', 'type', 'typical', 'udon', 'ultra', 'unable', 'unaccommodating', 'unattentive', 'unavailable', 'uncomfortable', 'unconventional', 'unda', 'undercooked', 'understated', 'understood', 'unforgetable', 'unfortunately', 'unfriendly', 'unheralded', 'unique', 'unisex', 'unless', 'unlike', 'unlimited', 'unobtrusive', 'unpretensious', 'unremarkable', 'upper', 'upscale', 'upstairs', 'use', 'used', 'usher', 'using', 'usual', 'usually', 'valentine', 'value', 'variety', 'various', 'veal', 'vegetable', 'vegetarian', 'vegtables', 'version', 'vibe', 'vibrant', 'view', 'virgnin', 'visit', 'visiting', 'vittorio', 'voluminous', 'wait', 'waited', 'waiter', 'waiting', 'waitress', 'waitstaff', 'walked', 'walking', 'wall', 'wallet', 'walnut', 'want', 'wanted', 'warm', 'warmed', 'wasabi', 'water', 'waterbugs', 'watering', 'watery', 'way', 'weekday', 'weekend', 'well', 'went', 'whatsoever', 'whip', 'whisk', 'white', 'whole', 'wide', 'wife', 'wild', 'wine', 'winnie', 'wintermelon', 'without', 'wo', 'woman', 'wondered', 'wonderful', 'wood', 'work', 'worked', 'worker', 'working', 'worst', 'worth', 'would', 'wow', 'wrap', 'wrapped', 'written', 'wrong', 'ya', 'yeah', 'yellow', 'yellowfun', 'yellowtail', 'yes', 'yet', 'york', 'yoshi', 'young', 'yum', 'yummy', 'zen', 'ziti', 'zucchini']\n",
            "Training feature shape (2136, 1539)\n",
            "Testing feature shape (691, 1539)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vw6uYOe9d4lk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74e63af2-eb47-435f-f071-6f040d3cc11d"
      },
      "source": [
        "# split training data into training and validation set\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(x_train_tfidf , df_train['sentiment'], test_size=0.25, random_state=42,shuffle = True)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_valid.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1602, 1539)\n",
            "(1602,)\n",
            "(534, 1539)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0R-ZMuC2uSVZ"
      },
      "source": [
        "- GRID SEARCH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xcn3UbKuMP0"
      },
      "source": [
        "# function to perform grid search for some_parameters \n",
        "def doGridSearch(model, some_parameters):\n",
        "\n",
        "  cv = StratifiedKFold(n_splits=5, random_state=42, shuffle = True)\n",
        "  search = GridSearchCV(model, some_parameters, scoring='accuracy', n_jobs=-1, cv=cv)\n",
        "\n",
        "  result = search.fit(x_train, y_train)\n",
        "\n",
        "  # print summary\n",
        "  print('Best Score: %s' % result.best_score_)\n",
        "  print('Best Hyperparameters: %s' % result.best_params_)\n",
        "  print()\n",
        "\n",
        "  means = result.cv_results_['mean_test_score']\n",
        "  stds = result.cv_results_['std_test_score']\n",
        "\n",
        "  for mean, std, params in zip(means, stds, result.cv_results_['params']):\n",
        "    print(\"%0.3f (+/-%0.03f) for %r\"% (mean, std * 2, params))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIG4MCY28uq6"
      },
      "source": [
        "- LOGISTIC REGRESSION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0Af7FDq9CeQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1effaf1e-b37d-478e-e839-584a6a56c1b3"
      },
      "source": [
        "logisticreg =  LogisticRegression(multi_class='multinomial')\n",
        "\n",
        "parameters = {'solver':  ['lbfgs','newton-cg' , 'sag','saga'] ,  'C': [0.01,0.1,0.5,1.0,1.5], 'tol':[0.0001,0.001, 0.01,0.1]}\n",
        "doGridSearch(logisticreg, parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: 0.6928855140186917\n",
            "Best Hyperparameters: {'C': 1.5, 'solver': 'lbfgs', 'tol': 0.0001}\n",
            "\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'lbfgs', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'lbfgs', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'lbfgs', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'lbfgs', 'tol': 0.1}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'newton-cg', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'newton-cg', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'newton-cg', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'newton-cg', 'tol': 0.1}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'sag', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'sag', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'sag', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'sag', 'tol': 0.1}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'saga', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'saga', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'saga', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.01, 'solver': 'saga', 'tol': 0.1}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'lbfgs', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'lbfgs', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'lbfgs', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'lbfgs', 'tol': 0.1}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'newton-cg', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'newton-cg', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'newton-cg', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'newton-cg', 'tol': 0.1}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'sag', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'sag', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'sag', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'sag', 'tol': 0.1}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'saga', 'tol': 0.0001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'saga', 'tol': 0.001}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'saga', 'tol': 0.01}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'solver': 'saga', 'tol': 0.1}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'lbfgs', 'tol': 0.0001}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'lbfgs', 'tol': 0.001}\n",
            "0.660 (+/-0.007) for {'C': 0.5, 'solver': 'lbfgs', 'tol': 0.01}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'lbfgs', 'tol': 0.1}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'newton-cg', 'tol': 0.0001}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'newton-cg', 'tol': 0.001}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'newton-cg', 'tol': 0.01}\n",
            "0.660 (+/-0.007) for {'C': 0.5, 'solver': 'newton-cg', 'tol': 0.1}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'sag', 'tol': 0.0001}\n",
            "0.659 (+/-0.008) for {'C': 0.5, 'solver': 'sag', 'tol': 0.001}\n",
            "0.659 (+/-0.009) for {'C': 0.5, 'solver': 'sag', 'tol': 0.01}\n",
            "0.659 (+/-0.009) for {'C': 0.5, 'solver': 'sag', 'tol': 0.1}\n",
            "0.660 (+/-0.007) for {'C': 0.5, 'solver': 'saga', 'tol': 0.0001}\n",
            "0.660 (+/-0.007) for {'C': 0.5, 'solver': 'saga', 'tol': 0.001}\n",
            "0.660 (+/-0.009) for {'C': 0.5, 'solver': 'saga', 'tol': 0.01}\n",
            "0.660 (+/-0.014) for {'C': 0.5, 'solver': 'saga', 'tol': 0.1}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'lbfgs', 'tol': 0.0001}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'lbfgs', 'tol': 0.001}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'lbfgs', 'tol': 0.01}\n",
            "0.684 (+/-0.027) for {'C': 1.0, 'solver': 'lbfgs', 'tol': 0.1}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'newton-cg', 'tol': 0.0001}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'newton-cg', 'tol': 0.001}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'newton-cg', 'tol': 0.01}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'newton-cg', 'tol': 0.1}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'sag', 'tol': 0.0001}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'sag', 'tol': 0.001}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'sag', 'tol': 0.01}\n",
            "0.684 (+/-0.025) for {'C': 1.0, 'solver': 'sag', 'tol': 0.1}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'saga', 'tol': 0.0001}\n",
            "0.685 (+/-0.028) for {'C': 1.0, 'solver': 'saga', 'tol': 0.001}\n",
            "0.685 (+/-0.025) for {'C': 1.0, 'solver': 'saga', 'tol': 0.01}\n",
            "0.685 (+/-0.024) for {'C': 1.0, 'solver': 'saga', 'tol': 0.1}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'lbfgs', 'tol': 0.0001}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'lbfgs', 'tol': 0.001}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'lbfgs', 'tol': 0.01}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'lbfgs', 'tol': 0.1}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'newton-cg', 'tol': 0.0001}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'newton-cg', 'tol': 0.001}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'newton-cg', 'tol': 0.01}\n",
            "0.692 (+/-0.033) for {'C': 1.5, 'solver': 'newton-cg', 'tol': 0.1}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'sag', 'tol': 0.0001}\n",
            "0.693 (+/-0.036) for {'C': 1.5, 'solver': 'sag', 'tol': 0.001}\n",
            "0.692 (+/-0.035) for {'C': 1.5, 'solver': 'sag', 'tol': 0.01}\n",
            "0.690 (+/-0.038) for {'C': 1.5, 'solver': 'sag', 'tol': 0.1}\n",
            "0.692 (+/-0.034) for {'C': 1.5, 'solver': 'saga', 'tol': 0.0001}\n",
            "0.692 (+/-0.034) for {'C': 1.5, 'solver': 'saga', 'tol': 0.001}\n",
            "0.692 (+/-0.034) for {'C': 1.5, 'solver': 'saga', 'tol': 0.01}\n",
            "0.690 (+/-0.039) for {'C': 1.5, 'solver': 'saga', 'tol': 0.1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZyAycTgdYO7"
      },
      "source": [
        "# Best Score: 0.6928855140186917\n",
        "# Best Hyperparameters: {'C': 1.5, 'solver': 'lbfgs', 'tol': 0.0001}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SdHMBC6O8zpU"
      },
      "source": [
        "- MULTINOMIAL NAIVE BAYES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWQiWlZVaBUg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c5943b4-d8da-4262-82fd-1a2f77303f2d"
      },
      "source": [
        "naivebayes  = MultinomialNB()\n",
        "parameters = {'alpha': [0.001, 0.01,0.1,0.5,1.0,1.2]}\n",
        "doGridSearch(naivebayes, parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: 0.6810066199376947\n",
            "Best Hyperparameters: {'alpha': 0.5}\n",
            "\n",
            "0.656 (+/-0.041) for {'alpha': 0.001}\n",
            "0.656 (+/-0.041) for {'alpha': 0.01}\n",
            "0.671 (+/-0.036) for {'alpha': 0.1}\n",
            "0.681 (+/-0.037) for {'alpha': 0.5}\n",
            "0.657 (+/-0.015) for {'alpha': 1.0}\n",
            "0.653 (+/-0.009) for {'alpha': 1.2}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOVmajCvdbZo"
      },
      "source": [
        "# Best Score: 0.6810066199376947\n",
        "# Best Hyperparameters: {'alpha': 0.5}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHpksNFu85dv"
      },
      "source": [
        "- SUPPORT VECTOR CLASSIFIER"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlfMM3WYehSm",
        "outputId": "6e112cfd-c3f6-46ae-c141-abecbb3acbf3"
      },
      "source": [
        "svc = SVC()\n",
        "parameters = {'kernel':  ['rbf', 'poly','sigmoid'] , 'C': [0.1,0.5,1.0,5,10], 'gamma': [0.0001,0.001,0.01,0.1]}\n",
        "doGridSearch(svc, parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Score: 0.690369937694704\n",
            "Best Hyperparameters: {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
            "\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.0001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.01, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.01, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.1, 'gamma': 0.1, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.0001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.0001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.01, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.01, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.01, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.1, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.1, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 0.5, 'gamma': 0.1, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.0001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.0001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.01, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.01, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.01, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.1, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.1, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 1.0, 'gamma': 0.1, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.0001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.0001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.01, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.01, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.01, 'kernel': 'sigmoid'}\n",
            "0.680 (+/-0.021) for {'C': 5, 'gamma': 0.1, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 5, 'gamma': 0.1, 'kernel': 'poly'}\n",
            "0.663 (+/-0.013) for {'C': 5, 'gamma': 0.1, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.0001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.0001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.001, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.001, 'kernel': 'sigmoid'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.01, 'kernel': 'poly'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.01, 'kernel': 'sigmoid'}\n",
            "0.690 (+/-0.029) for {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
            "0.637 (+/-0.002) for {'C': 10, 'gamma': 0.1, 'kernel': 'poly'}\n",
            "0.688 (+/-0.026) for {'C': 10, 'gamma': 0.1, 'kernel': 'sigmoid'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daFFAbwNdhZe"
      },
      "source": [
        "# Logistic Regression\n",
        "# Best Score: 0.6928855140186917\n",
        "# Best Hyperparameters: {'C': 1.5, 'solver': 'lbfgs', 'tol': 0.0001}\n",
        "\n",
        "# Mult NB\n",
        "# Best Score: 0.6810066199376947\n",
        "# Best Hyperparameters: {'alpha': 0.5}\n",
        "\n",
        "# SVC\n",
        "# Best Score: 0.690369937694704\n",
        "# Best Hyperparameters: {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
        "\n",
        "# Logistic Regression has best accuracy on validation set #"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6hm5rkAdkID"
      },
      "source": [
        "- Test Set Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btx3iEhChqqC",
        "outputId": "b6b914b4-01e1-4e5c-de31-d6a074efac90"
      },
      "source": [
        "# TEST SET\n",
        "\n",
        "model = LogisticRegression(multi_class='multinomial',C = 1.5, solver= 'lbfgs', tol =  0.0001, random_state=42) \n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test_tfidf) #x_test_tfidf\n",
        "\n",
        "accuracy_score(y_pred, y_test) #0.6800"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7279305354558611"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "DVVfAW7oO4Gz",
        "outputId": "ebe0e327-4c87-485f-e31c-dd87b72ecf0f"
      },
      "source": [
        "# Print confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm, annot=True, ax = ax,cmap='Blues'); \n",
        "\n",
        "# labels, title and ticks\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels([ \"negative\", \"neutral\",\"positive\"]);\n",
        "ax.yaxis.set_ticklabels([\"negative\", \"neutral\",\"positive\"],rotation = 0);\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sIakzcA/Qd7kDzOEWiJlZCWnEcyCLRcQs4J9Af6CzpJrGw5rAh2n6Q2CtFENroBMwo756nUDMzEpII16FtWpqeSCpDbAH8AZZIjkorXY0cG+avi/Nk5Y/EVH/NfbuwjIzKyGNeB/IasBISa3IGgt3RMRoSa8DoyT9FngJGJHWHwHcLGkqMBM4rKEdOIGYmZWQxsofETEF2LKO8neAbeoonw8cvCz7cAIxMyshHsrEzMwKUlWpQ5lI6iJps6YKxsyspWusk+jNocEEImmspI6SugKTgOsk/bHpQzMza3ma4jLeppJPC6RTRMwGfgTcFBHbArs3bVhmZi1TlfJ7lYJ8EkhrSasBhwCjmzgeM7MWLd+hTEpBPgnk18AjwNSIeEHSusDbTRuWmVnLpDz/lYIGr8KKiDuBO3Pm3wEObMqgzMxaqhJpXORlqQlE0v8jG/q3ThFxapNEZGbWgpXKCfJ81NcCmdhsUZiZGVA6l+jmY6kJJCJG5s5LahsRXzd9SGZmLVdF3UgoqX8afOtfaX5zSX9u8sjMzFqgSrsK63JgL9K48BHxMrBzUwZlZtZSldOd6HmNhRUR79c6sbOwacIxM2vZyqkLK58E8r6k7YGQtALwc7KHkpiZWSMrn/SRXxfWCcDJZA9c/wjYIs2bmVkjK6exsPK5kfBz4PBmiMXMrMUrkfPjecnnKqx1Jd0v6TNJ0yXdm4YzMTOzRlZpV2HdBtxB9nzd1cmGNflbUwZlZtZSlVMXVj4JpG1E3BwR1el1C7ByUwdmZtYSldNw7vWNhdU1TT4k6WxgFNnYWIcCDzZDbGZmLU6ptC7yUd9J9BfJEkbNu/lZzrIAzmmqoMzMWqrySR/1j4W1TnMGYmZm0KpU+qfykNed6JI2BTYm59xHRNzUVEFZfi48/1yeeXIsXbp25Y577l9i2S0jb+DyP/yex598ls5duhQpwsry7rR3OGv46YvnP/zgfU4cdiqHH3l0EaOqDB/8+10uufCsxfOffPQhQ447ka/mzuHR0X+nU+fsd/jInw6j33Y7FSvMZlEpXVgASPoVMIAsgTwIDAKeAUoigUjqDWwfEbcVsO3ciGjf6EE1k/1+uD+HHjaE8889e4nyTz75mPHPjaPnaqsVKbLK1Huddbn97n8AsHDhQvbabRd2Hbh7kaOqDGv26s0VI24HsmN77EF70X+nXXn8ofsYfPARHHDYUUWOsPmUUf7I6yqsg4CBwCcRcSywOdCpSaNaNr2BIXUtkJRXC6tcbdXv+3Ts1Pk75X/8/cWcetrwsvomU24mjH+ONddai9VXX6PYoVScKZMm0HP1Nenec/Vih1IUVVJer1KQTwKZFxGLgGpJHYHpwFrLu2NJvSW9Iek6Sa9JelRSG0nrSXpY0ouSnpbUN61/o6SDcrafmyYvBnaSNFnSaZKOkXSfpCeAMZLaSxojaZKkVyQNXt7YS9nYf46he/cebLBh32KHUtEeeehB9t7nB8UOoyI9NeYRdh649+L5B+4ZxSnHHsIVF1/A3DmzixhZ8yin0XjzSSATJXUGriO7MmsS8Fwj7b8PcFVEbALMInvW+rXAKRGxNTAcaOjZI2cDT0fEFhFxWSrbCjgoInYB5gMHRMRWwK7AH9TAV3NJQyVNlDTxhr9eW/Cba27z583jhuuu5YSTTyl2KBVtwYL/8OTYJ9hjz70bXtmWyYIFC5jw7JPsMGAPAAYNPphrbrufK0aMomu3VRhx1R+LHGHTK6cbCfMZC+ukNPkXSQ8DHSNiSiPtf1pETE7TL5J1R20P3JlzgFYqoN7HImJmmhZwkaSdgUVkg0L2AD5Z2sYRcS1ZImPON4uW+lz4UvPB++/z0Ycf8OOD9wdg+qefcvihBzLytttZZZVVixxd5Xjm6afpu9HGdFtllWKHUnFefP4Z1uvTly5duwEs/h9gz31/xG/OObVYoTWbViWSHPJR342EW9W3LCImNcL+v8mZXkj2wT4rIraoY91qUotJUhWwYj31fpUzfTiwKrB1RCyQ9C4Veif9+htswGNPjls8v9/eA7n5b3f5KqxG9vCDD7j7qok8PebhJbqvZs74jK7dsi8/459+grXXWa9YoTWbMrqKt94WyB/qWRbAbo0cC8BsYJqkgyPiztTVtFl6CuK7wNZk43L9EFghbTMH6FBPnZ2A6Sl57Aqs3QRxF8UvzzyDFydOYNasWeyz+wCGnjSM/X90UMMbWsHmff01zz83jvN+dWGxQ6k48+fNY/LE5znpjPMWl9149RVMm/omSPTouRonDT+vnhoqQzklEEUUp4cmXX47OiI2TfPDgfbASOBqssEbVwBGRcSvJfUA7gXaAA8DJ0dE+/SQq0eAbsCNwBdAv4gYlupdBbg/1T0R2A4YFBHv5nMZbzl1YZWrcrpxqly9P2NesUNoETbs2Xa5f5nPuP/NvD5z/rDfhkX/wylaAikXTiBNzwmk6TmBNI/GSCC/GJ1fArlk3+InkIq+T8LMrNyU0Tl0JxAzs1LSuowySD5PJJSkIySdn+Z7Sdqm6UMzM2t5Ku1Gwj8D/YEfp/k5wFVNFpGZWQtWTkOZ5NOFtW1EbCXpJYCI+EJSffdgmJlZgUokN+QlnwSyQFIrsns/kLQq2R3dZmbWyMrposR8urD+BNwDdJf0P2RDuV/UpFGZmbVQraqU16shktaS9E9Jr6cBa3+eyrtKekzS2+n/Lqlckv4kaaqkKfWNRlIjn7GwbpX0ItmQ7gL2j4g3GozezMyWWSO2QKqBMyJikqQOwIuSHgOOAcZExMWSziYbkPYssmc99Umvbclu6N623lgbikBSL+Brsru57wO+SmVmZtbIlOe/hkTExzVjFkbEHOANssFkB5ON+EH6f/80PRi4KTLjgc6S6n0qXT7nQB4gO/8hskEI1wHeBDbJY1szM1sG+bZAJA0FhuYUXZtGEq9r3d7AlsDzQI+I+Dgt+oRsEFvIksv7OZt9kMo+Ziny6cL6Xq1AtgJOWsrqZma2HPJNILmPnaiPpPbA3cB/RcTs3GeJRERIKni4pnxOoi8hNYnq7RczM7PCNOYDpdJgs3cDt0bE31PxpzVdU+n/6an8Q5Z82uyaqWypGmyBSDo9Z7aK7Gl/H+UVvZmZLZNWy/y1vm7pcRgjgDciIvdRjvcBR5M9DvxoslHOa8qHSRpF1kj4Mqerq075nAPJfdZGNdk5kbvzegdmZrZMGvEu8x2AI4FXJNU8+fWXZInjDknHA+8Bh6RlDwL7AFPJLpw6tqEd1JtA0g2EHSJieEHhm5nZMmmsy3gj4hlY6uVaA+tYP4CTl2Uf9T3StnVEVEvaYVkqNDOzwlXKUCYTyM53TJZ0H3AnOc8azzkhY2ZmjaQqj3s8SkU+50BWBmaQPQO95n6QAJxAzMwaWaW0QLqnK7Be5dvEUcOPeTUzawKty2g0xfoSSCugPXWfhHECMTNrApXSAvk4In7dbJGYmVnJPCwqH/UlkPJ5F2ZmFaKM8ke9CeQ71wmbmVnTaqQb0ZvFUhNIRMxszkDMzKxyurDMzKyZOYGYmVlByid9OIGYmZWUMmqAOIGYmZWSfJ/1UQqcQMzMSkhFXIVlZmbNzyfRK4kHbWly5fQHU662GHRmsUNoEea9dOVy1+EuLDMzK4i7sMzMrCBugZiZWUHKJ304gZiZlZRWboGYmVkhyih/OIGYmZUSlVEnlhOImVkJcQvEzMwKUuUWiJmZFcItEDMzK0g5jczgBGJmVkKqyid/OIGYmZUSX4VlZmYFKaMeLCcQM7NS4haImZkVxOdAzMysIL4Ky8zMClI+6cMJxMyspLgFYmZmBSmf9OEEYmZWWsoogziBmJmVEHdhmZlZQconfUBVsQMwM7McyvOVT1XS9ZKmS3o1p6yrpMckvZ3+75LKJelPkqZKmiJpq4bqdwIxMyshyvNfnm4E9q5VdjYwJiL6AGPSPMAgoE96DQWubqhyJxAzsxIi5ffKR0Q8BcysVTwYGJmmRwL755TfFJnxQGdJq9VXvxOImVkJacQerKXpEREfp+lPgB5peg3g/Zz1PkhlS+UEYmZWQiTl+xoqaWLOa+iy7isiAohCY/VVWGZmJWQZuqeuBa4tYBefSlotIj5OXVTTU/mHwFo5662ZypbKLRAzsxLSDF1Y9wFHp+mjgXtzyo9KV2NtB3yZ09VVJ7dAzMxKSSPeCCLpb8AAYBVJHwC/Ai4G7pB0PPAecEha/UFgH2Aq8DVwbEP1O4GYmZWQxnygVET8eCmLBtaxbgAnL0v9TiBl7MLzz+WZp8bSpWtX7vj7/QCc84vTeO+9dwGYM2c2HTp05LY77ililJXjm2++4dijDmfBf/5D9cKF7LHnXpw07NRih1VSqqrEuFvP5KPpX3Lgz//yneUH7rEl556wDxHwylsfcswvb1yu/XXp2Jabf3cca6/elfc+mskRZ45g1px5HDaoH6cfsweSmPv1fE696HZeeave7vySUUYjmZRfApF0AvB1RNwk6Rjg0Yj4KC37K/DHiHi9mDE2l/0G78+hPx7C+eeevbjsfy+5bPH0ZZf+jvbt2xcjtIq04oor8tfrR9K2XTsWLFjAMUcOYceddmazzbcodmglY9iQXXlz2qd0aLfyd5at12tVhh+3J7sd80dmzZnHql3y/93caes+HPnDbRn6q1uWKB9+7B6MnfAml97wGMOP3YPhx+7JeX+6l3c/msGeP7mcWXPmsecOG3PVeT9m56MuXe731xzKKYGU3Un0iPhLRNyUZo8BVs9Z9pOWkjwAttr6+3Ts2LnOZRHB448+zF6DftDMUVUuSbRt1w6A6upqqqury+uvvYmt0b0ze++4CTfc82ydy487YHuuueMpZs2ZB8BnX8xdvOy0owbyzC2/YMLt53DeCfvkvc99B2zGLfc/D8At9z/PfrtuBsD4l6ct3s+EKdNYo0fdfyelqJHvRG9SzZpAJPWW9C9Jt0p6Q9JdktpKGijpJUmvpLFbVkrrXyzp9TQuy6Wp7AJJwyUdBPQDbpU0WVIbSWMl9ZN0gqRLcvZ7jKQr0/QRkiakba6R1Ko5j0FzeWnSRLp260avtXsXO5SKsnDhQg750WB23Wl7tuu/PZtttnmxQyoZl/ziQM694h8sWlT3bQV91u5On17deeKG03hy5Bnssf1GAAzcri/r9erOjkdcwraHXcyWG/Vih63Wy2uf3bt14JPPZwPwyeez6d6tw3fWOWb/7XlkXPl8r2zMO9GbWjG6sDYEjo+IcZKuB04HfgYMjIi3JN0EnCjpZuAAoG9EhKQlvkJExF2ShgHDI2IiZN8Qk7uB54BfpPlDgf+RtFGa3iEiFkj6M3A4cBMV5pGHHmCvvd36aGytWrXijr/fy+zZsznt1JN5++236NNng2KHVXSDdtqU6TPn8NIb77PT1n3qXKdVq1as36s7e/70Ctbo3oXHR/wX/Q6+iN37b8Tu/fsyflTWFdu+zUqs36s74yb9H0/dNJwVV2xN+zYr0aVT28XrnHfFvTz+3Bvf2UfUyl079+vD0fv3Z+Bxl31n3VJVIrkhL8VIIO9HxLg0fQvw38C0iHgrlY0kuxLgSmA+MELSaGB0vjuIiM8kvZOuZX4b6AuMS/VuDbyQkk0bvr2JZrF0R+dQgCuuvJpjj1/mGzyLqrq6mn+OeZybR91V7FAqVseOHfn+Ntvy7DNPO4EA/bdYl313+R5777gJK624Ah3brcz1vz2K48779rvZh9Nn8cIr71JdvYj3PprB2+9NZ/1eqyLBJdc/yoi7x32n3przFks7BzJ9xhx6rtKRTz6fTc9VOvLZzDmLl23aZ3WuPn8Ig4ddzcwvv2qid94EyiiDFOMcSO327aw6V4qoBrYB7gL2BR5exv2MIru++UDgnnSJmoCREbFFem0YERfUse9rI6JfRPQrt+QBMOH55+i9zjr06NGz2KFUlJkzZzJ7dtZdMn/+fMY/9yy911m3yFGVhvP/332sv/d/0/cHv+Kos29g7AtvLZE8AO7/58vs3C9rnXTr3I4+a3dn2oczeOzZNzh6cH/atVkRgNVX7ZT3CfYHnnyFI/bbFoAj9tuW0WOnALBWzy6MuvSnHP/fNzH139/5jljSqqS8XqWgGC2QXpL6R8RzwEhasSAAAAp8SURBVBBgIvAzSetHxFTgSOBJSe2BthHxoKRxwDt11DUH+G6nZ+Ye4FxgS+CsVDYGuFfSZRExXVJXoENEvNd4b6/5/PKsM3hx4gRmzZrFPnsMYOiJw9j/Rwfx6MMPsqe7rxrd559N57xfns2iRQtZtCjYc6+92WXArsUOq6T994k/YNLr/+aBJ1/hsWffYPf+GzHp7nNZuDD45eX/YOaXXzFm/L/ou05Pxo4cDsBX877h2HNHLnGSfWkuveExbvndcRy9f3/+/fFMjjjzegDOGTqIrp3bcfk5hwJQvXAROx7++6Z7o42oNFJDfhS1Ow2bcmdSb7KWxESyrqTXyRJGf+BSsoT2AnAi0JXsFvuVyY7ppRExUtIFwNyIuFTSgcBFwLxUx0MseU5kNLBxRCz+mijpUOAcstbXAuDkNHRxnebMX8oZQWs0K7Quu4sBy06X7w8rdggtwryXrlzuz/+3Pv06r8+cDXq0LXquKUYCGR0RmzbbTpeTE0jTcwJpek4gzaMxEsjbn87L6zOnT482RU8gZXcjoZlZJSuR0xt5adYEEhHvAmXT+jAza25llD/cAjEzKyUqoyaIE4iZWQkpo/zhBGJmVkrKKH84gZiZlZQyyiBOIGZmJaRURtrNhxOImVkJ8TkQMzMrSJUTiJmZFaZ8MogTiJlZCXEXlpmZFaSM8ocTiJlZKXELxMzMCuKhTMzMrCDlkz6cQMzMSkoZNUCcQMzMSonvRDczs8KUT/5wAjEzKyVllD+cQMzMSklVGZ0EcQIxMyshZZQ/qCp2AGZmVp7cAjEzKyHl1AJxAjEzKyG+jNfMzAriFoiZmRXECcTMzAriLiwzMyuIWyBmZlaQMsofTiBmZiWljDKIE4iZWQkpp6FMFBHFjsEamaShEXFtseOoZD7GTc/HuPR5KJPKNLTYAbQAPsZNz8e4xDmBmJlZQZxAzMysIE4glcn9xk3Px7jp+RiXOJ9ENzOzgrgFYmZmBXECMTOzgjiBVDhJnSWdlDO/uqS7ihlTpZDUW9KQAred29jxVApJJ0g6Kk0fI2n1nGV/lbRx8aKzXD4HUuEk9QZGR8SmRQ6l4kgaAAyPiH3rWNY6Iqrr2XZuRLRvyvgqgaSxZMd4YrFjse9yC6TI0rfYNyRdJ+k1SY9KaiNpPUkPS3pR0tOS+qb115M0XtIrkn5b801WUntJYyRNSssGp11cDKwnabKkS9L+Xk3bjJe0SU4sYyX1k9RO0vWSJkh6KaeuilDAMb9R0kE529e0Hi4GdkrH9rT0bfk+SU8AY+r5mVSsdGz/JenWdIzvktRW0sD0u/RK+t1aKa1/saTXJU2RdGkqu0DS8HTM+wG3pmPcJud39ARJl+Ts9xhJV6bpI9Lv7mRJ10hqVYxj0SJEhF9FfAG9gWpgizR/B3AEMAbok8q2BZ5I06OBH6fpE4C5abo10DFNrwJMJRuWrTfwaq39vZqmTwMuTNOrAW+m6YuAI9J0Z+AtoF2xj1URj/mNwEE529cc8wFkrbua8mOAD4Cu9f1McuuotFc6tgHskOavB84D3gc2SGU3Af8FdAPezDkmndP/F5C1OgDGAv1y6h9LllRWBabmlD8E7AhsBNwPrJDK/wwcVezjUqkvt0BKw7SImJymXyT7I9weuFPSZOAasg94gP7AnWn6tpw6BFwkaQrwOLAG0KOB/d4B1HyzPgSoOTeyJ3B22vdYYGWg1zK/q9K2LMd8WTwWETPTdCE/k0rwfkSMS9O3AAPJjvdbqWwksDPwJTAfGCHpR8DX+e4gIj4D3pG0naRuQF9gXNrX1sAL6ec4EFi3Ed6T1cGj8ZaGb3KmF5J9yMyKiC2WoY7Dyb6VbR0RCyS9S/bBv1QR8aGkGZI2Aw4la9FA9sF3YES8uQz7LzfLcsyrSd29kqqAFeup96uc6WX+mVSI2idWZ5G1NpZcKaJa0jZkH/IHAcOA3ZZhP6PIvvj8C7gnIkKSgJERcU5BkdsycQukNM0Gpkk6GECZzdOy8cCBafqwnG06AdPTB9WuwNqpfA7QoZ593Q6cCXSKiCmp7BHglPTHiKQtl/cNlYH6jvm7ZN9qAX4IrJCmGzq2S/uZVLpekvqn6SHARKC3pPVT2ZHAk5Lak/3ePUjWnbr5d6uq9xjfAwwGfkyWTCDrhjxIUncASV0ltZTj3uycQErX4cDxkl4GXiP7Q4Gs7/j01C2yPlk3AMCtQD9JrwBHkX0rIyJmAOMkvZp70jHHXWSJ6I6cst+QfUhOkfRamm8JlnbMrwN2SeX9+baVMQVYKOllSafVUV+dP5MW4E3gZElvAF2Ay4BjyboHXwEWAX8hSwyj0+/yM8DpddR1I/CXmpPouQsi4gvgDWDtiJiQyl4nO+fyaKr3MQrrirQ8+DLeMiOpLTAvNdcPIzuhXvFX91h5kC8bb1F8DqT8bA1cmbqXZgHHFTkeM2uh3AIxM7OC+ByImZkVxAnEzMwK4gRiZmYFcQKxkiVpYbp881VJd6Yr0Aqta/F4VmpgRFdJAyRtX8A+3pW0Sr7ltdZZptF5a8aLWtYYzRqTE4iVsnkRsUW6JPQ/fHunPJCNeFtIpRHxk3S/wNIMIBvWxMzq4QRi5eJpYP3UOnha0n3A65JaKRtl+IU0ouvPYPGd5FdKelPS40D3mopqRnRN03srGy33ZWUj5/YmS1SnpdbPTpJWlXR32scLknZI23ZTNpLva5L+SjYETL0k/UPZaL+vSRpaa9llqXyMpFVTWZ0jBNfa7lR9O6LtqNrLzZqK7wOxkpdaGoOAh1PRVsCmETEtfQh/GRHfVzZE+DhJjwJbAhsCG5ONc/U62ciwufWuSnaX+c6prq4RMVPSX8hGy60ZXvw24LKIeEZSL7KhXjYCfgU8ExG/lvQD4Pg83s5xaR9tyAb8uzuNFtAOmBgRp0k6P9U9DLgWOCEi3pa0LdnosrXHizobWCcivpHUOa+DatYInECslLVRNqIqZC2QEWRdSxMiYloq3xPYTN8+r6MT0IdstNe/RcRC4CNlz+iobTvgqZq6ckbRrW13YOM0NBhAxzSO087Aj9K2D0j6Io/3dKqkA9L0WinWGWTDe9yeym8B/p72UTNCcM32K9VR5xSyZ2b8A/hHHjGYNQonECtl82qPjps+SHNHvBVwSkQ8Umu9fRoxjipgu4iYX0cseVP2BMPdgf4R8bWyp+0tbXTeSPvNZ1TmH5Als/2AcyV9L+p5GqJZY/E5ECt3jwAnSloBQNIGktoBTwGHpnMkqwG71rHteGBnSeukbbum8tojwD4KnFIzI6nmA/0pstFmkTSIbODA+nQCvkjJoy9ZC6hGFd8+m2UIWddYfSME18RSBawVEf8Ezkr78KNyrVk4gVi5+yvZ+Y1Jyh7Vew1Zy/oe4O207CbgudobpocSDSXrLnqZb7uQ7gcOqDmJDpxKNqruFEmv8+3VYBeSJaDXyLqy/t1ArA8DrZWNUnsxWQKr8RWwTXoPuwG/TuVLGyG4RivgFmWj3L4E/CkiZjUQh1mj8FhYZmZWELdAzMysIE4gZmZWECcQMzMriBOImZkVxAnEzMwK4gRiZmYFcQIxM7OC/H9eT+Cd63lDCAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4V9D2ml2GNO"
      },
      "source": [
        "## EMBEDDINGS WITH NEURAL NETWORK"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvnPjb7_5jih",
        "outputId": "9bedea3d-58fc-48fd-9c50-4ebcf1420184"
      },
      "source": [
        "# test label\n",
        "y_test = pd.get_dummies(df_test['sentiment'])\n",
        "y_test = y_test.values\n",
        "\n",
        "# all training label\n",
        "y = pd.get_dummies(df_train['sentiment'])\n",
        "label = y.values\n",
        "label"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0],\n",
              "       [0, 0, 1],\n",
              "       [0, 0, 1],\n",
              "       ...,\n",
              "       [0, 1, 0],\n",
              "       [0, 1, 0],\n",
              "       [0, 1, 0]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QEjQTJRA0sj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "842a82f4-4608-4349-bfa4-c7eea732964c"
      },
      "source": [
        "print(df_train.describe())\n",
        "print()\n",
        "print(df_test.describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          len_text\n",
            "count  2136.000000\n",
            "mean      2.640918\n",
            "std       0.852489\n",
            "min       0.000000\n",
            "25%       2.000000\n",
            "50%       2.000000\n",
            "75%       3.000000\n",
            "max       8.000000\n",
            "\n",
            "         len_text\n",
            "count  691.000000\n",
            "mean     2.602026\n",
            "std      0.776052\n",
            "min      0.000000\n",
            "25%      2.000000\n",
            "50%      2.000000\n",
            "75%      3.000000\n",
            "max      5.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thn9Rv8G2lsO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bd788a4-9a31-4f17-80b4-a45e7b632628"
      },
      "source": [
        "from tensorflow.keras.preprocessing import text\n",
        "\n",
        "x = df_train['processed_text'].values\n",
        "\n",
        "max_features = 1500         # most freq 1500 words in the dataset would be kept\n",
        "max_text_length = 8        # as  max text length = 8\n",
        "\n",
        "# change each text into a sequence of integers, each integer being index of token in a dictionary\n",
        "x_tokenizer = text.Tokenizer(max_features)\n",
        "x_tokenizer.fit_on_texts(list(x))\n",
        "x_tokenized = x_tokenizer.texts_to_sequences(x)\n",
        "x_training_all = sequence.pad_sequences(x_tokenized,maxlen = max_text_length)#padding\n",
        "\n",
        "x_training_all"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0, ...,   0,  10, 174],\n",
              "       [  0,   0,   0, ..., 685,   1,  85],\n",
              "       [  0,   0,   0, ...,   0,   1, 437],\n",
              "       ...,\n",
              "       [  0,   0,   0, ..., 198, 242, 191],\n",
              "       [  0,   0,   0, ..., 191, 588,  74],\n",
              "       [  0,   0,   0, ...,   0,  71,  65]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7arVPuF82l1_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae395830-c0df-4571-ca85-acef3c287abd"
      },
      "source": [
        "#tokenise and pad the text \n",
        "x_test_tokenized = x_tokenizer.texts_to_sequences(df_test['processed_text'].values)\n",
        "x_testing = sequence.pad_sequences(x_test_tokenized,maxlen = max_text_length) \n",
        "\n",
        "x_testing"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    0, ...,   95,  909,   36],\n",
              "       [   0,    0,    0, ...,    0, 1017,   79],\n",
              "       [   0,    0,    0, ...,    1,   15,   21],\n",
              "       ...,\n",
              "       [   0,    0,    0, ...,    0,    0,    0],\n",
              "       [   0,    0,    0, ...,    0,    0,   39],\n",
              "       [   0,    0,    0, ...,    0,    0,  264]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EJFsrCy2Yw4"
      },
      "source": [
        "#### Embedding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3f1VPWm_jYeX",
        "outputId": "722d4df0-26d4-4b05-91a3-1f1f2e3921d4"
      },
      "source": [
        "# code to download glove data\n",
        "!wget http://nlp.stanford.edu/data/glove.6B.zip\n",
        "!unzip -q glove.6B.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-10 19:57:05--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2021-06-10 19:57:05--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2021-06-10 19:57:06--  http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  5.04MB/s    in 2m 41s  \n",
            "\n",
            "2021-06-10 19:59:46 (5.12 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fF11yNgZ2bEN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b09a8579-3ea7-4465-faa8-9288bfe2ae34"
      },
      "source": [
        "# open glove and store word embeddings\n",
        "embedding_dim = 100\n",
        "embeddings_index = dict()\n",
        "\n",
        "f = open('glove.6B.100d.txt')\n",
        "\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:],dtype = 'float32') #store all excepy val[0]\n",
        "    embeddings_index[word] = coefs                  #populate embedding dict\n",
        "    \n",
        "f.close()\n",
        "\n",
        "print(f'Found {len(embeddings_index)} word vectors') "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nghT2cgj2geD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e92439c-c36d-4c15-c7b2-c049b9c88afa"
      },
      "source": [
        "# prepare embedding_matrix\n",
        "\n",
        "embedding_matrix = np.zeros((max_features,embedding_dim))\n",
        "\n",
        "for word,index in x_tokenizer.word_index.items():\n",
        "    \n",
        "    if index> max_features -1: #bounding by max feat\n",
        "        break\n",
        "    else:\n",
        "        embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector\n",
        "\n",
        "embedding_matrix.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1500, 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elxP5PTg2gig",
        "outputId": "c074a6a9-72b4-4296-d3d5-13c401897c99"
      },
      "source": [
        "print(embedding_matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.          0.          0.         ...  0.          0.\n",
            "   0.        ]\n",
            " [-0.16486     0.91996998  0.22736999 ... -0.067904    1.5072\n",
            "   0.60889   ]\n",
            " [-0.4224     -0.13313    -0.41418001 ... -0.070558    0.12285\n",
            "   0.43399999]\n",
            " ...\n",
            " [ 0.75694001  0.033857    0.37807    ...  0.26293999  0.60535997\n",
            "   0.62142998]\n",
            " [-0.34031999  0.16266     0.12526    ... -0.56076998 -0.41714001\n",
            "   0.43031999]\n",
            " [ 0.066709    0.27447999  0.81423998 ...  0.50615001  0.50641\n",
            "   0.025595  ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17DIpfFh6w-T",
        "outputId": "687ef9b6-b7ef-4f8f-b757-98350c4eb8c9"
      },
      "source": [
        "# split data into training and vslidation set\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(x_training_all ,label, test_size=0.2, random_state=42, shuffle = True)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "\n",
        "print(x_valid.shape)\n",
        "print(y_valid.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1708, 8)\n",
            "(1708, 3)\n",
            "(428, 8)\n",
            "(428, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciCeV1JaBkkY",
        "outputId": "e748fe81-e91f-4f99-c7e8-92440995b788"
      },
      "source": [
        "x_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    0, ...,    0,    0,    0],\n",
              "       [   0,    0,    0, ...,  154,   21,  264],\n",
              "       [   0,    0,    0, ...,    0,  259,  173],\n",
              "       ...,\n",
              "       [   0,    0,    0, ...,    0,    6,  618],\n",
              "       [   0,    0,    0, ...,  183, 1185,   31],\n",
              "       [   0,    0,    0, ...,    0,  219, 1012]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8d2MyFkd29HM"
      },
      "source": [
        "#### Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJQAHDXEuxQU"
      },
      "source": [
        "# plot accuracy and loss curves\n",
        "def plot_history(history):\n",
        "    \n",
        "    plt.figure(figsize=(8,5),linewidth = 7, edgecolor=\"whitesmoke\")    \n",
        "    n = len(history.history['accuracy'])\n",
        "    \n",
        "    plt.plot(np.arange(0,n)+1,history.history['accuracy'], color='orange',marker=\".\")\n",
        "    plt.plot(np.arange(0,n)+1,history.history['loss'],'b',marker=\".\")\n",
        "    \n",
        "    # offset both validation curves\n",
        "    plt.plot(np.arange(0,n)+ 1,history.history['val_accuracy'],'r')  \n",
        "    plt.plot(np.arange(0,n)+ 1,history.history['val_loss'],'g')\n",
        "    \n",
        "    plt.legend(['Train Acc','Train Loss','Val Acc','Val Loss'])\n",
        "    plt.grid(True)\n",
        "    \n",
        "    # set vertical limit to 1\n",
        "    plt.gca().set_ylim(0,1)\n",
        "\n",
        "    plt.xlabel(\"Number of Epochs\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.suptitle(\"Learning Curve\", size=16, y=0.927)\n",
        "    plt.show() "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNDCXRY92RkV"
      },
      "source": [
        "#function to evaluate the model using validation data\n",
        "def model_evalulate(model_name):\n",
        "    \n",
        "    testres = model_name.evaluate(x_valid, y_valid, verbose=0)\n",
        "    \n",
        "    print(\"Validation set\")\n",
        "    print('loss:', testres[0])\n",
        "    print(\"accuracy:\" ,testres[1])\n",
        "\n",
        "#function to evaluate the model using test data\n",
        "def model_evalulate_test(model_name):\n",
        "    \n",
        "    testres = model_name.evaluate(x_testing, y_test, verbose=0)\n",
        "    \n",
        "    print(\"Test set\")\n",
        "    print('loss:', testres[0])\n",
        "    print(\"accuracy:\" ,testres[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umVLmxXO2T1z"
      },
      "source": [
        "# set some values\n",
        "\n",
        "embedding_dim = 100\n",
        "max_features = 1500   \n",
        "actfn = 'relu'\n",
        "optimizer_ = tf.optimizers.Adam\n",
        "learningrate = 0.001\n",
        "batch_size = 20"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGtRWwgI7BAx"
      },
      "source": [
        "# function to build the CNN model \n",
        "def model_cnn():\n",
        "    \n",
        "    model = Sequential()\n",
        "    \n",
        "    #create embedding layer\n",
        "    model.add(Embedding(max_features, embedding_dim, trainable = False ,embeddings_initializer = tf.keras.initializers.Constant(embedding_matrix)))  \n",
        "\n",
        "    # 1st dropout\n",
        "    model.add(Dropout(0.4))\n",
        "    \n",
        "    # 1st convolutional 1-D layer\n",
        "    model.add(Conv1D(32,3, padding = 'valid',activation = actfn)) #no padding\n",
        "    \n",
        "    #max pooling layer\n",
        "    model.add(MaxPooling1D())\n",
        "    \n",
        "    # 2nd convolutional 1-D layer\n",
        "    model.add(Conv1D(64, 3, padding = 'valid', activation = actfn))\n",
        "    \n",
        "    # global max pooling layer\n",
        "    model.add(GlobalMaxPooling1D())\n",
        "    \n",
        "    # 1st dense layer\n",
        "    model.add(Dense(6,activation = actfn))\n",
        "    \n",
        "    # 2nd dropout\n",
        "    model.add(Dropout(0.4))\n",
        "    \n",
        "    # final dense layer\n",
        "    model.add(Dense(3,activation = 'softmax')) #sigmoid\n",
        "    \n",
        "    # compile the model\n",
        "    model.compile(loss = 'categorical_crossentropy',    \n",
        "                 optimizer = optimizer_(learning_rate = 0.001),\n",
        "                 metrics = ['accuracy']) \n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T625u-ZOZc64"
      },
      "source": [
        "# function to build the LSTM model \n",
        "def model_lstm():\n",
        "    \n",
        "    model = Sequential()\n",
        "    \n",
        "    #create embedding layer\n",
        "    model.add(Embedding(max_features, embedding_dim, trainable = False ,embeddings_initializer = tf.keras.initializers.Constant(embedding_matrix)))  \n",
        "\n",
        "    model.add(layers.Bidirectional(layers.LSTM(20, dropout=0.5,activation='tanh')))\n",
        "\n",
        "    model.add(Dense(10, activation='relu'))\n",
        "\n",
        "    # final dense layer\n",
        "    model.add(Dense(3,activation = 'softmax'))\n",
        "    \n",
        "    # compile the model\n",
        "    model.compile(loss = 'categorical_crossentropy',    \n",
        "                 optimizer = 'rmsprop'     ,   \n",
        "                 metrics = ['accuracy']) \n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFPb7hvx7BEa"
      },
      "source": [
        "# function to compile and fit the model \n",
        "def model_run(actfn, n_epochs, batch_size, model):\n",
        "    \n",
        "    es = EarlyStopping(monitor='val_accuracy', mode='max', verbose = 1 ,patience= 20, restore_best_weights = True ) \n",
        "    \n",
        "    # fit the model\n",
        "    history = model.fit(x_train,y_train,\n",
        "              batch_size = batch_size,\n",
        "              epochs = n_epochs,\n",
        "              validation_data=(x_valid,y_valid),\n",
        "              callbacks=[es] )\n",
        "    \n",
        "    return (history, model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "h8KQ1GfJ7GD_",
        "outputId": "93b26009-bd6a-444b-fa85-e41763b44bd3"
      },
      "source": [
        "# Run LSTM model\n",
        "\n",
        "seed_value = 2\n",
        "random.seed(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "tf.random.set_seed(seed_value)\n",
        "\n",
        "history_1stm, model_1stm = model_run (actfn, 100, batch_size,  model_lstm())\n",
        "plot_history(history_1stm) \n",
        "\n",
        "print()\n",
        "model_evalulate(model_1stm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "86/86 [==============================] - 16s 19ms/step - loss: 1.0083 - accuracy: 0.4429 - val_loss: 0.8817 - val_accuracy: 0.6308\n",
            "Epoch 2/100\n",
            "86/86 [==============================] - 1s 7ms/step - loss: 0.8725 - accuracy: 0.6210 - val_loss: 0.8573 - val_accuracy: 0.6308\n",
            "Epoch 3/100\n",
            "86/86 [==============================] - 1s 7ms/step - loss: 0.8541 - accuracy: 0.6216 - val_loss: 0.8375 - val_accuracy: 0.6425\n",
            "Epoch 4/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.8006 - accuracy: 0.6626 - val_loss: 0.8288 - val_accuracy: 0.6589\n",
            "Epoch 5/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7817 - accuracy: 0.6674 - val_loss: 0.7993 - val_accuracy: 0.6495\n",
            "Epoch 6/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7623 - accuracy: 0.6784 - val_loss: 0.8015 - val_accuracy: 0.6589\n",
            "Epoch 7/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7289 - accuracy: 0.6873 - val_loss: 0.8201 - val_accuracy: 0.6565\n",
            "Epoch 8/100\n",
            "86/86 [==============================] - 1s 7ms/step - loss: 0.7348 - accuracy: 0.6874 - val_loss: 0.8215 - val_accuracy: 0.6636\n",
            "Epoch 9/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7471 - accuracy: 0.6882 - val_loss: 0.7924 - val_accuracy: 0.6589\n",
            "Epoch 10/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7450 - accuracy: 0.6894 - val_loss: 0.7949 - val_accuracy: 0.6729\n",
            "Epoch 11/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7129 - accuracy: 0.6999 - val_loss: 0.7809 - val_accuracy: 0.6729\n",
            "Epoch 12/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6646 - accuracy: 0.7409 - val_loss: 0.7761 - val_accuracy: 0.6752\n",
            "Epoch 13/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7072 - accuracy: 0.6967 - val_loss: 0.7753 - val_accuracy: 0.6846\n",
            "Epoch 14/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6825 - accuracy: 0.7033 - val_loss: 0.7972 - val_accuracy: 0.6799\n",
            "Epoch 15/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.7033 - accuracy: 0.7035 - val_loss: 0.8073 - val_accuracy: 0.6752\n",
            "Epoch 16/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6533 - accuracy: 0.7337 - val_loss: 0.7842 - val_accuracy: 0.6799\n",
            "Epoch 17/100\n",
            "86/86 [==============================] - 1s 7ms/step - loss: 0.6530 - accuracy: 0.7266 - val_loss: 0.7862 - val_accuracy: 0.6682\n",
            "Epoch 18/100\n",
            "86/86 [==============================] - 1s 7ms/step - loss: 0.6577 - accuracy: 0.7470 - val_loss: 0.7758 - val_accuracy: 0.6799\n",
            "Epoch 19/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6688 - accuracy: 0.7073 - val_loss: 0.7730 - val_accuracy: 0.6682\n",
            "Epoch 20/100\n",
            "86/86 [==============================] - 1s 7ms/step - loss: 0.6263 - accuracy: 0.7368 - val_loss: 0.7733 - val_accuracy: 0.6822\n",
            "Epoch 21/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6492 - accuracy: 0.7332 - val_loss: 0.7854 - val_accuracy: 0.6776\n",
            "Epoch 22/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6662 - accuracy: 0.7278 - val_loss: 0.8025 - val_accuracy: 0.6752\n",
            "Epoch 23/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6533 - accuracy: 0.7353 - val_loss: 0.8028 - val_accuracy: 0.6776\n",
            "Epoch 24/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6681 - accuracy: 0.7269 - val_loss: 0.8260 - val_accuracy: 0.6776\n",
            "Epoch 25/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6152 - accuracy: 0.7377 - val_loss: 0.8078 - val_accuracy: 0.6846\n",
            "Epoch 26/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6032 - accuracy: 0.7461 - val_loss: 0.8179 - val_accuracy: 0.6799\n",
            "Epoch 27/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6087 - accuracy: 0.7542 - val_loss: 0.8237 - val_accuracy: 0.6706\n",
            "Epoch 28/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.5700 - accuracy: 0.7677 - val_loss: 0.8148 - val_accuracy: 0.6799\n",
            "Epoch 29/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6072 - accuracy: 0.7465 - val_loss: 0.8411 - val_accuracy: 0.6776\n",
            "Epoch 30/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.5661 - accuracy: 0.7698 - val_loss: 0.8210 - val_accuracy: 0.6822\n",
            "Epoch 31/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.5993 - accuracy: 0.7396 - val_loss: 0.8341 - val_accuracy: 0.6729\n",
            "Epoch 32/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.5842 - accuracy: 0.7504 - val_loss: 0.8396 - val_accuracy: 0.6659\n",
            "Epoch 33/100\n",
            "86/86 [==============================] - 1s 6ms/step - loss: 0.6046 - accuracy: 0.7593 - val_loss: 0.8847 - val_accuracy: 0.6706\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00033: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set\n",
            "loss: 0.7753225564956665\n",
            "accuracy: 0.6845794320106506\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fGt9GZya_TFv",
        "outputId": "e0c97d44-0507-4987-9c6e-49efd19eedc6"
      },
      "source": [
        "# Run CNN Model\n",
        "tf.random.set_seed(seed_value)\n",
        "\n",
        "history_cnn, model_cnn_ = model_run (actfn, 100, batch_size, model_cnn())\n",
        "plot_history(history_cnn) \n",
        "print()\n",
        "model_evalulate(model_cnn_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "86/86 [==============================] - 18s 9ms/step - loss: 1.0242 - accuracy: 0.5486 - val_loss: 0.8916 - val_accuracy: 0.6308\n",
            "Epoch 2/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.9345 - accuracy: 0.6329 - val_loss: 0.8675 - val_accuracy: 0.6308\n",
            "Epoch 3/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.8753 - accuracy: 0.6361 - val_loss: 0.8605 - val_accuracy: 0.6379\n",
            "Epoch 4/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.8557 - accuracy: 0.6495 - val_loss: 0.8409 - val_accuracy: 0.6425\n",
            "Epoch 5/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.8482 - accuracy: 0.6391 - val_loss: 0.8091 - val_accuracy: 0.6542\n",
            "Epoch 6/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.8540 - accuracy: 0.6314 - val_loss: 0.7954 - val_accuracy: 0.6589\n",
            "Epoch 7/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.8318 - accuracy: 0.6419 - val_loss: 0.8070 - val_accuracy: 0.6612\n",
            "Epoch 8/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.8146 - accuracy: 0.6547 - val_loss: 0.7908 - val_accuracy: 0.6565\n",
            "Epoch 9/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7600 - accuracy: 0.6775 - val_loss: 0.7866 - val_accuracy: 0.6659\n",
            "Epoch 10/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7593 - accuracy: 0.6616 - val_loss: 0.7843 - val_accuracy: 0.6659\n",
            "Epoch 11/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7681 - accuracy: 0.6373 - val_loss: 0.7840 - val_accuracy: 0.6612\n",
            "Epoch 12/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7416 - accuracy: 0.6790 - val_loss: 0.7740 - val_accuracy: 0.6682\n",
            "Epoch 13/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7761 - accuracy: 0.6579 - val_loss: 0.7887 - val_accuracy: 0.6612\n",
            "Epoch 14/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7178 - accuracy: 0.6750 - val_loss: 0.7920 - val_accuracy: 0.6659\n",
            "Epoch 15/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7223 - accuracy: 0.6875 - val_loss: 0.7890 - val_accuracy: 0.6682\n",
            "Epoch 16/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7319 - accuracy: 0.7018 - val_loss: 0.7976 - val_accuracy: 0.6659\n",
            "Epoch 17/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7182 - accuracy: 0.7062 - val_loss: 0.8094 - val_accuracy: 0.6659\n",
            "Epoch 18/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6857 - accuracy: 0.7095 - val_loss: 0.7929 - val_accuracy: 0.6682\n",
            "Epoch 19/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.7070 - accuracy: 0.6898 - val_loss: 0.7995 - val_accuracy: 0.6659\n",
            "Epoch 20/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.6937 - val_loss: 0.8102 - val_accuracy: 0.6612\n",
            "Epoch 21/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6442 - accuracy: 0.7224 - val_loss: 0.8068 - val_accuracy: 0.6636\n",
            "Epoch 22/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6594 - accuracy: 0.7205 - val_loss: 0.8024 - val_accuracy: 0.6706\n",
            "Epoch 23/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6428 - accuracy: 0.7349 - val_loss: 0.8305 - val_accuracy: 0.6612\n",
            "Epoch 24/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6759 - accuracy: 0.7096 - val_loss: 0.7997 - val_accuracy: 0.6472\n",
            "Epoch 25/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6341 - accuracy: 0.7485 - val_loss: 0.8358 - val_accuracy: 0.6659\n",
            "Epoch 26/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6173 - accuracy: 0.7352 - val_loss: 0.8547 - val_accuracy: 0.6542\n",
            "Epoch 27/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6177 - accuracy: 0.7449 - val_loss: 0.8511 - val_accuracy: 0.6425\n",
            "Epoch 28/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6252 - accuracy: 0.7415 - val_loss: 0.8250 - val_accuracy: 0.6495\n",
            "Epoch 29/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6255 - accuracy: 0.7401 - val_loss: 0.8188 - val_accuracy: 0.6659\n",
            "Epoch 30/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6204 - accuracy: 0.7325 - val_loss: 0.8256 - val_accuracy: 0.6659\n",
            "Epoch 31/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6253 - accuracy: 0.7291 - val_loss: 0.8419 - val_accuracy: 0.6659\n",
            "Epoch 32/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5859 - accuracy: 0.7518 - val_loss: 0.8502 - val_accuracy: 0.6612\n",
            "Epoch 33/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6293 - accuracy: 0.7492 - val_loss: 0.8523 - val_accuracy: 0.6565\n",
            "Epoch 34/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5993 - accuracy: 0.7560 - val_loss: 0.8522 - val_accuracy: 0.6636\n",
            "Epoch 35/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5816 - accuracy: 0.7520 - val_loss: 0.8826 - val_accuracy: 0.6682\n",
            "Epoch 36/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.6167 - accuracy: 0.7320 - val_loss: 0.8792 - val_accuracy: 0.6495\n",
            "Epoch 37/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5968 - accuracy: 0.7547 - val_loss: 0.8746 - val_accuracy: 0.6519\n",
            "Epoch 38/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5825 - accuracy: 0.7597 - val_loss: 0.9133 - val_accuracy: 0.6542\n",
            "Epoch 39/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5492 - accuracy: 0.7616 - val_loss: 0.9338 - val_accuracy: 0.6542\n",
            "Epoch 40/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5816 - accuracy: 0.7583 - val_loss: 0.9035 - val_accuracy: 0.6612\n",
            "Epoch 41/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5654 - accuracy: 0.7617 - val_loss: 0.9066 - val_accuracy: 0.6612\n",
            "Epoch 42/100\n",
            "86/86 [==============================] - 0s 3ms/step - loss: 0.5682 - accuracy: 0.7543 - val_loss: 0.8896 - val_accuracy: 0.6565\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00042: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set\n",
            "loss: 0.8023753762245178\n",
            "accuracy: 0.6705607771873474\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdogMX7Whm_7"
      },
      "source": [
        "- LSTM : Validation set  - loss: 0.7753225564956665, accuracy: 0.6845794320106506\n",
        "\n",
        "- CNN : Validation set  - loss: 0.8023753762245178 ,accuracy: 0.6705607771873474\n",
        "\n",
        "- LSTM Model has a greater accuracy and lower loss than CNN model , so i will optimise LSTM Model further\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nw9uPdmoDNyU"
      },
      "source": [
        "### Optimise LSTM further"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpjmuPSfIGdh"
      },
      "source": [
        "- #1 Batch size and Number of Epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQbmAgO6DSsL",
        "outputId": "270bf030-fcbb-41a2-8fa4-3c44c7c3fb3b"
      },
      "source": [
        "#batch size and epoch\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "\n",
        "# create model\n",
        "model = KerasClassifier(build_fn= model_lstm, verbose=0)\n",
        "\n",
        "# define the grid search parameters\n",
        "batch_size = [10, 20, 32, 64, 80, 128]\n",
        "epochs = [20, 30, 50]\n",
        "\n",
        "param_grid = dict(batch_size=batch_size, epochs=epochs)\n",
        "\n",
        "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)\n",
        "grid_result = grid.fit(x_train, y_train)\n",
        "\n",
        "# summarize results\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best: 0.693799 using {'batch_size': 128, 'epochs': 50}\n",
            "0.689710 (0.017549) with: {'batch_size': 10, 'epochs': 20}\n",
            "0.692623 (0.008614) with: {'batch_size': 10, 'epochs': 30}\n",
            "0.675061 (0.004071) with: {'batch_size': 10, 'epochs': 50}\n",
            "0.685020 (0.010882) with: {'batch_size': 20, 'epochs': 20}\n",
            "0.685609 (0.014705) with: {'batch_size': 20, 'epochs': 30}\n",
            "0.683845 (0.005510) with: {'batch_size': 20, 'epochs': 50}\n",
            "0.690880 (0.019184) with: {'batch_size': 32, 'epochs': 20}\n",
            "0.690878 (0.013417) with: {'batch_size': 32, 'epochs': 30}\n",
            "0.680927 (0.017268) with: {'batch_size': 32, 'epochs': 50}\n",
            "0.686196 (0.016850) with: {'batch_size': 64, 'epochs': 20}\n",
            "0.689119 (0.012681) with: {'batch_size': 64, 'epochs': 30}\n",
            "0.682092 (0.011243) with: {'batch_size': 64, 'epochs': 50}\n",
            "0.680919 (0.007677) with: {'batch_size': 80, 'epochs': 20}\n",
            "0.685606 (0.010935) with: {'batch_size': 80, 'epochs': 30}\n",
            "0.682673 (0.006925) with: {'batch_size': 80, 'epochs': 50}\n",
            "0.668633 (0.018223) with: {'batch_size': 128, 'epochs': 20}\n",
            "0.684443 (0.020128) with: {'batch_size': 128, 'epochs': 30}\n",
            "0.693799 (0.007015) with: {'batch_size': 128, 'epochs': 50}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7MKof2ZitYK"
      },
      "source": [
        "# Best: 0.693799 using {'batch_size': 128, 'epochs': 50}\n",
        "# I will use this batch size and epoch for further tuning of optimizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I56_HXh9IMkD"
      },
      "source": [
        "- #2. Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anInzsTdGY7V"
      },
      "source": [
        "# function to build the model \n",
        "def model_lstm_act(optimizer = 'RMSprop'):\n",
        "    \n",
        "    model = Sequential()\n",
        "    \n",
        "    #create embedding layer\n",
        "    model.add(Embedding(max_features, embedding_dim, trainable = False ,embeddings_initializer = tf.keras.initializers.Constant(embedding_matrix)))  \n",
        "\n",
        "    model.add(layers.Bidirectional(layers.LSTM(20, dropout=0.5,activation='tanh')))\n",
        "\n",
        "    model.add(Dense(10, activation='relu'))\n",
        "\n",
        "    # final dense layer\n",
        "    model.add(Dense(3,activation = 'softmax')) \n",
        "    \n",
        "    # compile the model\n",
        "    model.compile(loss = 'categorical_crossentropy',    \n",
        "                 optimizer = optimizer    ,  \n",
        "                 metrics = ['accuracy']) \n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GARq7lxJDSwu",
        "outputId": "a62767ca-8e79-45c6-f49a-7cc7330fb6fd"
      },
      "source": [
        "# create model\n",
        "model = KerasClassifier(build_fn = model_lstm_act, epochs=50, batch_size=128, verbose=0)\n",
        "\n",
        "# define the grid search parameters\n",
        "optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
        "param_grid = dict(optimizer=optimizer)\n",
        "\n",
        "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)\n",
        "\n",
        "grid_result = grid.fit(x_train, y_train)\n",
        "# summarize results\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best: 0.693806 using {'optimizer': 'Adam'}\n",
            "0.632338 (0.024118) with: {'optimizer': 'SGD'}\n",
            "0.691464 (0.015207) with: {'optimizer': 'RMSprop'}\n",
            "0.627652 (0.021391) with: {'optimizer': 'Adagrad'}\n",
            "0.356417 (0.170014) with: {'optimizer': 'Adadelta'}\n",
            "0.693806 (0.014246) with: {'optimizer': 'Adam'}\n",
            "0.665124 (0.023388) with: {'optimizer': 'Adamax'}\n",
            "0.683848 (0.008458) with: {'optimizer': 'Nadam'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eG6AXv-XjAMh"
      },
      "source": [
        "# Best: 0.693806 using {'optimizer': 'Adam'}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyzPrx-GIQoL"
      },
      "source": [
        "- #3. Number of layers in BiLSTM and hiddensize in Dense layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbvQ7J0yDS19"
      },
      "source": [
        "# function to build the model with given no. of layers\n",
        "def model_lstm_layers(no_layers_l, no_layers_d):\n",
        "    \n",
        "    model = Sequential()\n",
        "    \n",
        "    #create embedding layer\n",
        "    model.add(Embedding(max_features, embedding_dim, trainable = False ,embeddings_initializer = tf.keras.initializers.Constant(embedding_matrix)))  \n",
        "\n",
        "    # Bidirectional LSTM Layer\n",
        "    model.add(layers.Bidirectional(layers.LSTM(no_layers_l, dropout=0.5,activation='tanh')))\n",
        "\n",
        "    # dense layer\n",
        "    model.add(Dense(no_layers_d, activation='relu'))\n",
        "\n",
        "    # final dense layer\n",
        "    model.add(Dense(3,activation = 'softmax')) \n",
        "    \n",
        "    # compile the model\n",
        "    model.compile(loss = 'categorical_crossentropy',    \n",
        "                 optimizer = 'adam'     ,   \n",
        "                 metrics = ['accuracy']) \n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gP5lM-Z9DS5x",
        "outputId": "a502acc3-0a63-436f-a71b-c05d5e178d60"
      },
      "source": [
        "# lstm layers\n",
        "tf.random.set_seed(seed_value)\n",
        "\n",
        "lstm_layers = [2, 5, 10, 15, 20, 25, 30]\n",
        "\n",
        "for no_layers in lstm_layers:\n",
        "\n",
        "  model = model_lstm_layers(no_layers, 10)\n",
        "\n",
        "  es = EarlyStopping(monitor='val_accuracy', mode='max', verbose = 1 ,patience= 20, restore_best_weights = True  ) \n",
        "  history = model.fit(x_train,y_train, batch_size = 128, epochs = 60 ,validation_data=(x_valid,y_valid),callbacks=[es], verbose = 0)   # fit the model\n",
        "\n",
        "  print(\"-------------------------\")\n",
        "  print(\"no_layers: \", no_layers)\n",
        "  model_evalulate(model)\n",
        "  print(\"-------------------------\")\n",
        "  print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00024: early stopping\n",
            "-------------------------\n",
            "no_layers:  2\n",
            "Validation set\n",
            "loss: 1.0014516115188599\n",
            "accuracy: 0.6308411359786987\n",
            "-------------------------\n",
            "\n",
            "-------------------------\n",
            "no_layers:  5\n",
            "Validation set\n",
            "loss: 0.7586606740951538\n",
            "accuracy: 0.6705607771873474\n",
            "-------------------------\n",
            "\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00037: early stopping\n",
            "-------------------------\n",
            "no_layers:  10\n",
            "Validation set\n",
            "loss: 0.8013831973075867\n",
            "accuracy: 0.6752336621284485\n",
            "-------------------------\n",
            "\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00037: early stopping\n",
            "-------------------------\n",
            "no_layers:  15\n",
            "Validation set\n",
            "loss: 0.7887979745864868\n",
            "accuracy: 0.672897219657898\n",
            "-------------------------\n",
            "\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00052: early stopping\n",
            "-------------------------\n",
            "no_layers:  20\n",
            "Validation set\n",
            "loss: 0.7647894620895386\n",
            "accuracy: 0.6845794320106506\n",
            "-------------------------\n",
            "\n",
            "-------------------------\n",
            "no_layers:  25\n",
            "Validation set\n",
            "loss: 0.8611069321632385\n",
            "accuracy: 0.6822429895401001\n",
            "-------------------------\n",
            "\n",
            "-------------------------\n",
            "no_layers:  30\n",
            "Validation set\n",
            "loss: 0.9073664546012878\n",
            "accuracy: 0.6565420627593994\n",
            "-------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjUM28n_jce9"
      },
      "source": [
        "# Best- no_layers:  20 , loss: 0.7647894620895386, accuracy: 0.6845794320106506"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eoh_qLIrt4Q",
        "outputId": "9f4cb75e-e7b2-4bdc-f91f-71672cc7a35a"
      },
      "source": [
        "# Dense Layer size optimisation\n",
        "\n",
        "tf.random.set_seed(seed_value)\n",
        "dense_layers = [6,9,10,12,16]\n",
        "\n",
        "for no_layers in dense_layers:\n",
        "\n",
        "  model = model_lstm_layers( 20 , no_layers)\n",
        "\n",
        "  es = EarlyStopping(monitor='val_accuracy', mode='max', verbose = 1 ,patience= 20, restore_best_weights = True  ) # loss - 7, 'min' restore_best_weights = True \n",
        "  history = model.fit(x_train,y_train, batch_size = 128, epochs = 60 ,validation_data=(x_valid,y_valid),callbacks=[es], verbose = 0)   # fit the model\n",
        "\n",
        "  print(\"-------------------------\")\n",
        "  print(\"no_layers: \", no_layers)\n",
        "  model_evalulate(model)\n",
        "  print(\"-------------------------\")\n",
        "  print() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00043: early stopping\n",
            "-------------------------\n",
            "no_layers:  6\n",
            "Validation set\n",
            "loss: 0.7835257649421692\n",
            "accuracy: 0.6705607771873474\n",
            "-------------------------\n",
            "\n",
            "-------------------------\n",
            "no_layers:  9\n",
            "Validation set\n",
            "loss: 0.8408368825912476\n",
            "accuracy: 0.6682242751121521\n",
            "-------------------------\n",
            "\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00031: early stopping\n",
            "-------------------------\n",
            "no_layers:  10\n",
            "Validation set\n",
            "loss: 0.7996227145195007\n",
            "accuracy: 0.677570104598999\n",
            "-------------------------\n",
            "\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00036: early stopping\n",
            "-------------------------\n",
            "no_layers:  12\n",
            "Validation set\n",
            "loss: 0.7715784907341003\n",
            "accuracy: 0.677570104598999\n",
            "-------------------------\n",
            "\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00032: early stopping\n",
            "-------------------------\n",
            "no_layers:  16\n",
            "Validation set\n",
            "loss: 0.796851634979248\n",
            "accuracy: 0.672897219657898\n",
            "-------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeOk6V4hraWQ"
      },
      "source": [
        "# Best size for dense layer- no_layers:  10, loss: 0.7996227145195007 , accuracy: 0.677570104598999"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXI-O0ZUJ6TJ"
      },
      "source": [
        "### Final Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKx5lkonIVnv"
      },
      "source": [
        "# function to build the model \n",
        "def model_lstm_final():\n",
        "    \n",
        "    model = Sequential()\n",
        "    \n",
        "    #create embedding layer\n",
        "    model.add(Embedding(max_features, embedding_dim, trainable = False ,embeddings_initializer = tf.keras.initializers.Constant(embedding_matrix)))  \n",
        "\n",
        "    # Bidirectional LSTM Layer\n",
        "    model.add(layers.Bidirectional(layers.LSTM(20 , dropout=0.5,activation='tanh')))\n",
        "\n",
        "    # dense layer\n",
        "    model.add(Dense(10 , activation='relu'))\n",
        "\n",
        "    # final dense layer\n",
        "    model.add(Dense(3,activation = 'softmax'))\n",
        "    \n",
        "    # compile the model\n",
        "    model.compile(loss = 'categorical_crossentropy',    \n",
        "                 optimizer = 'adam' ,   \n",
        "                 metrics = ['accuracy']) \n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0KNzXnZIVrJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "94454a5a-90f5-493e-937b-083567f658ad"
      },
      "source": [
        "tf.random.set_seed(seed_value)\n",
        "\n",
        "history_final, model_final = model_run (actfn, 60, 128,  model_lstm_final())\n",
        "plot_history(history_final) \n",
        "\n",
        "print()\n",
        "model_evalulate(model_final)\n",
        "model_evalulate_test(model_final)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n",
            "14/14 [==============================] - 7s 109ms/step - loss: 1.0868 - accuracy: 0.2743 - val_loss: 1.0283 - val_accuracy: 0.2453\n",
            "Epoch 2/60\n",
            "14/14 [==============================] - 0s 15ms/step - loss: 1.0198 - accuracy: 0.3455 - val_loss: 0.9923 - val_accuracy: 0.6051\n",
            "Epoch 3/60\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.9701 - accuracy: 0.6161 - val_loss: 0.9242 - val_accuracy: 0.6308\n",
            "Epoch 4/60\n",
            "14/14 [==============================] - 0s 15ms/step - loss: 0.9035 - accuracy: 0.6356 - val_loss: 0.8808 - val_accuracy: 0.6308\n",
            "Epoch 5/60\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.8798 - accuracy: 0.6272 - val_loss: 0.8745 - val_accuracy: 0.6308\n",
            "Epoch 6/60\n",
            "14/14 [==============================] - 0s 15ms/step - loss: 0.8610 - accuracy: 0.6234 - val_loss: 0.8549 - val_accuracy: 0.6308\n",
            "Epoch 7/60\n",
            "14/14 [==============================] - 0s 16ms/step - loss: 0.8242 - accuracy: 0.6517 - val_loss: 0.8398 - val_accuracy: 0.6285\n",
            "Epoch 8/60\n",
            "14/14 [==============================] - 0s 16ms/step - loss: 0.8210 - accuracy: 0.6439 - val_loss: 0.8259 - val_accuracy: 0.6402\n",
            "Epoch 9/60\n",
            "14/14 [==============================] - 0s 16ms/step - loss: 0.8231 - accuracy: 0.6428 - val_loss: 0.8082 - val_accuracy: 0.6425\n",
            "Epoch 10/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.8096 - accuracy: 0.6402 - val_loss: 0.8076 - val_accuracy: 0.6449\n",
            "Epoch 11/60\n",
            "14/14 [==============================] - 0s 23ms/step - loss: 0.7773 - accuracy: 0.6553 - val_loss: 0.7907 - val_accuracy: 0.6542\n",
            "Epoch 12/60\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.7400 - accuracy: 0.6915 - val_loss: 0.7963 - val_accuracy: 0.6542\n",
            "Epoch 13/60\n",
            "14/14 [==============================] - 0s 19ms/step - loss: 0.7635 - accuracy: 0.6769 - val_loss: 0.7918 - val_accuracy: 0.6589\n",
            "Epoch 14/60\n",
            "14/14 [==============================] - 0s 19ms/step - loss: 0.7408 - accuracy: 0.6914 - val_loss: 0.7851 - val_accuracy: 0.6565\n",
            "Epoch 15/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.7629 - accuracy: 0.6846 - val_loss: 0.7840 - val_accuracy: 0.6612\n",
            "Epoch 16/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.7327 - accuracy: 0.6865 - val_loss: 0.7761 - val_accuracy: 0.6612\n",
            "Epoch 17/60\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.7049 - accuracy: 0.7051 - val_loss: 0.7824 - val_accuracy: 0.6659\n",
            "Epoch 18/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.7045 - accuracy: 0.6914 - val_loss: 0.7853 - val_accuracy: 0.6636\n",
            "Epoch 19/60\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.7344 - accuracy: 0.6855 - val_loss: 0.7814 - val_accuracy: 0.6682\n",
            "Epoch 20/60\n",
            "14/14 [==============================] - 0s 16ms/step - loss: 0.7007 - accuracy: 0.7164 - val_loss: 0.7766 - val_accuracy: 0.6706\n",
            "Epoch 21/60\n",
            "14/14 [==============================] - 0s 16ms/step - loss: 0.6933 - accuracy: 0.7131 - val_loss: 0.7793 - val_accuracy: 0.6706\n",
            "Epoch 22/60\n",
            "14/14 [==============================] - 0s 16ms/step - loss: 0.7144 - accuracy: 0.6960 - val_loss: 0.7847 - val_accuracy: 0.6752\n",
            "Epoch 23/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.6999 - accuracy: 0.7027 - val_loss: 0.7787 - val_accuracy: 0.6776\n",
            "Epoch 24/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.7041 - accuracy: 0.7046 - val_loss: 0.7850 - val_accuracy: 0.6776\n",
            "Epoch 25/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.6892 - accuracy: 0.7020 - val_loss: 0.7808 - val_accuracy: 0.6659\n",
            "Epoch 26/60\n",
            "14/14 [==============================] - 0s 19ms/step - loss: 0.6732 - accuracy: 0.7129 - val_loss: 0.7823 - val_accuracy: 0.6706\n",
            "Epoch 27/60\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.6673 - accuracy: 0.7255 - val_loss: 0.7857 - val_accuracy: 0.6659\n",
            "Epoch 28/60\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.6455 - accuracy: 0.7226 - val_loss: 0.7793 - val_accuracy: 0.6636\n",
            "Epoch 29/60\n",
            "14/14 [==============================] - 0s 22ms/step - loss: 0.6734 - accuracy: 0.7214 - val_loss: 0.7848 - val_accuracy: 0.6636\n",
            "Epoch 30/60\n",
            "14/14 [==============================] - 0s 27ms/step - loss: 0.6452 - accuracy: 0.7244 - val_loss: 0.7768 - val_accuracy: 0.6659\n",
            "Epoch 31/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.6687 - accuracy: 0.7278 - val_loss: 0.7945 - val_accuracy: 0.6682\n",
            "Epoch 32/60\n",
            "14/14 [==============================] - 0s 19ms/step - loss: 0.6558 - accuracy: 0.7247 - val_loss: 0.7840 - val_accuracy: 0.6729\n",
            "Epoch 33/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.6705 - accuracy: 0.7252 - val_loss: 0.7941 - val_accuracy: 0.6659\n",
            "Epoch 34/60\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.6158 - accuracy: 0.7398 - val_loss: 0.7928 - val_accuracy: 0.6659\n",
            "Epoch 35/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.6604 - accuracy: 0.7088 - val_loss: 0.8282 - val_accuracy: 0.6682\n",
            "Epoch 36/60\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.6627 - accuracy: 0.7162 - val_loss: 0.7901 - val_accuracy: 0.6682\n",
            "Epoch 37/60\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.6317 - accuracy: 0.7341 - val_loss: 0.8145 - val_accuracy: 0.6659\n",
            "Epoch 38/60\n",
            "14/14 [==============================] - 0s 20ms/step - loss: 0.6581 - accuracy: 0.7247 - val_loss: 0.7932 - val_accuracy: 0.6659\n",
            "Epoch 39/60\n",
            "14/14 [==============================] - 0s 20ms/step - loss: 0.6354 - accuracy: 0.7323 - val_loss: 0.8040 - val_accuracy: 0.6612\n",
            "Epoch 40/60\n",
            "14/14 [==============================] - 0s 20ms/step - loss: 0.6170 - accuracy: 0.7468 - val_loss: 0.8011 - val_accuracy: 0.6636\n",
            "Epoch 41/60\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.6435 - accuracy: 0.7263 - val_loss: 0.8147 - val_accuracy: 0.6659\n",
            "Epoch 42/60\n",
            "14/14 [==============================] - 0s 21ms/step - loss: 0.6386 - accuracy: 0.7375 - val_loss: 0.8032 - val_accuracy: 0.6682\n",
            "Epoch 43/60\n",
            "14/14 [==============================] - 0s 25ms/step - loss: 0.6117 - accuracy: 0.7424 - val_loss: 0.8281 - val_accuracy: 0.6636\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00043: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation set\n",
            "loss: 0.7787066102027893\n",
            "accuracy: 0.677570104598999\n",
            "Test set\n",
            "loss: 0.6610619425773621\n",
            "accuracy: 0.7424023151397705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOTzDOnTIVuN"
      },
      "source": [
        "# Final Result\n",
        "\n",
        "# Validation set\n",
        "# loss: 0.7787066102027893\n",
        "# accuracy: 0.677570104598999\n",
        "\n",
        "# Test set\n",
        "# loss: 0.6610619425773621\n",
        "# accuracy: 0.7424023151397705"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSigY4uJJLu5",
        "outputId": "58c9b3a2-1e58-405d-e5c8-86fd165a9973"
      },
      "source": [
        "y_pred =  model_final.predict(x_testing)\n",
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.16351214, 0.15501368, 0.6814742 ],\n",
              "       [0.34145004, 0.18301876, 0.4755312 ],\n",
              "       [0.0317125 , 0.01569882, 0.9525886 ],\n",
              "       ...,\n",
              "       [0.29577914, 0.23795865, 0.46626225],\n",
              "       [0.1256233 , 0.15146515, 0.7229116 ],\n",
              "       [0.15743099, 0.19474217, 0.64782685]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9s5qbhQBI-4H",
        "outputId": "962b957e-bafa-4091-cd25-7ee943d52605"
      },
      "source": [
        "# confusion matrix\n",
        "y_pred1 = y_pred.round()\n",
        "cm = confusion_matrix(y_test.argmax(axis=1), y_pred1.argmax(axis=1))\n",
        "print(cm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 65   1  50]\n",
            " [ 34   3  59]\n",
            " [ 67   1 411]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "6x45h_KiI-62",
        "outputId": "b68dd2e6-8994-46f4-985d-f517da41bce8"
      },
      "source": [
        "# plot confusion  matrix\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm, annot=True, ax = ax,cmap='Blues');\n",
        "\n",
        "# labels, title and ticks\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels([ \"negative\", \"neutral\",\"positive\"]);\n",
        "ax.yaxis.set_ticklabels([\"negative\", \"neutral\",\"positive\"],rotation = 0);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ip8wj4JWI-9p",
        "outputId": "9b4344f8-11d4-4b8e-9d8c-1b1b4d66415f"
      },
      "source": [
        "# classification_report\n",
        "report = metrics.classification_report(y_test.argmax(axis=1), y_pred1.argmax(axis=1) ,target_names=[\"negative\", \"neutral\",\"positive\"],labels = [0,1,2])\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.39      0.56      0.46       116\n",
            "     neutral       0.60      0.03      0.06        96\n",
            "    positive       0.79      0.86      0.82       479\n",
            "\n",
            "    accuracy                           0.69       691\n",
            "   macro avg       0.59      0.48      0.45       691\n",
            "weighted avg       0.70      0.69      0.66       691\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}