Yelp_NLP_RA.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EAName/yelp-reviews/blob/main/Yelp_NLP_RA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsePP5VZuxQq"
      },
      "source": [
        "## Imports "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "92Dg33FmuxQs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import pickle \n",
        "import re,string\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize \n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.decomposition import TruncatedSVD, LatentDirichletAllocation\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import roc_auc_score, accuracy_score, confusion_matrix, silhouette_score\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, classification_report\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split, KFold\n",
        "from sklearn.manifold import MDS\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.model_selection import GridSearchCV \n",
        "from sklearn.model_selection import cross_val_score \n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import KFold \n",
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from gensim.models import Word2Vec,LdaMulticore, TfidfModel, CoherenceModel\n",
        "from gensim import corpora\n",
        "from gensim import similarities\n",
        "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
        "from gensim.models import LsiModel,LdaModel\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "#from keras.preprocessing.sequence import pad_sequences\n",
        "from tqdm import tqdm\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, LSTM, Dense, SpatialDropout1D, GRU\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.initializers import Constant\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.optimizers import Adam, Nadam, RMSprop\n",
        "from keras import Input\n",
        "\n",
        "from IPython.display import display"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGHly-zIpris",
        "outputId": "91d60b9a-4bf1-40c3-a651-060c7dfcedf8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "ovbC3RlUpxKS"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqTk3PNGvHH2",
        "outputId": "553cafa6-6561-4251-b643-72c0e43ac35d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/gdrive/MyDrive/yelp_dataset/yelp_reviews_clean_version4_st_louis.csv')"
      ],
      "metadata": {
        "id": "ofrho4sfu5nc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfQeqg9yvQnR",
        "outputId": "7befb5ce-b77b-4af6-dea0-9c8532d0de54"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(292376, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ede7p75luxQy"
      },
      "source": [
        "**Extract year**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "SggCnX1zuxQy"
      },
      "outputs": [],
      "source": [
        "df['review_year'] = df['date'].apply(lambda x:int(x[:4]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_4Eohp3uxQy"
      },
      "source": [
        "**Look at distribution of reviews by year**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.review_year.value_counts(ascending= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BiT5Omkvh9q",
        "outputId": "b363edb5-5ff3-4a50-c723-d79bdf94857a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2005       70\n",
              "2006      314\n",
              "2007      669\n",
              "2022     1016\n",
              "2008     2867\n",
              "2009     3261\n",
              "2010     9617\n",
              "2011    12709\n",
              "2012    14949\n",
              "2013    18420\n",
              "2020    18472\n",
              "2021    21413\n",
              "2014    23235\n",
              "2015    30111\n",
              "2016    31640\n",
              "2017    33296\n",
              "2018    34368\n",
              "2019    35949\n",
              "Name: review_year, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Du6o1gITuxQy"
      },
      "source": [
        "**Create 1-0 sentiment flag**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "zH9VbEWHuxQy"
      },
      "outputs": [],
      "source": [
        "df['sentiment'] = df['stars'].apply(lambda x: 1 if x >= 4 else 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7yBmnQguxQz"
      },
      "source": [
        "**Drop reviews older than 2011**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "v3QeIOP6uxQz"
      },
      "outputs": [],
      "source": [
        "#df = df[(df['review_year'] > 2011)]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df.shape"
      ],
      "metadata": {
        "id": "z9TQ9u8L4QfF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2OVxMlpuxQz"
      },
      "source": [
        "## Text Preprocessing "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_doc(doc): \n",
        "    #split review into individual words\n",
        "    tokens=doc.split()\n",
        "    re_punc = re.compile('[%s]' % re.escape(string.punctuation))\n",
        "    # remove punctuation from each word\n",
        "    tokens = [re_punc.sub('', w) for w in tokens]\n",
        "\n",
        "    #word lemmzatization    \n",
        "    lemm = WordNetLemmatizer()\n",
        "    tokens=[lemm.lemmatize(word) for word in tokens]\n",
        "\n",
        "    # remove remaining tokens that are not alphabetic\n",
        "    tokens = [word for word in tokens if word.isalpha()]\n",
        "    # filter out short tokens\n",
        "    tokens = [word for word in tokens if len(word) > 3]\n",
        "    #lowercase all words\n",
        "    tokens = [word.lower() for word in tokens]\n",
        "    # filter out stop words\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    tokens = [w for w in tokens if not w in stop_words]     \n",
        "\n",
        "    # filter out common words\n",
        "    #tokens = [w for w in tokens if not w in ['movie', 'movies', 'films']]      \n",
        "    # # word stemming    \n",
        "    # ps=PorterStemmer()\n",
        "    # tokens=[ps.stem(word) for word in tokens]\n",
        "\n",
        "    return tokens\n",
        "\n",
        "def final_processed_text(doc):\n",
        "    #this is a function to join the processed text back\n",
        "    ' '.join(doc)\n",
        "    return doc"
      ],
      "metadata": {
        "id": "9aA2s_9J1hao"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['cleaned_text'] = df['text'].apply(lambda x: clean_doc(x))\n",
        "\n",
        "final_processed_text = [' '.join(x) for x in df['cleaned_text'].tolist()]\n",
        "df['final_processed_text'] = final_processed_text     #add to dataframe"
      ],
      "metadata": {
        "id": "cFEYh7_b1hc0"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text length'] = df['text'].apply(len)"
      ],
      "metadata": {
        "id": "3jczJFSds5vH"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_processed_text[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KOC1OSzkbri6",
        "outputId": "8efaf63b-b46d-45cc-8dc1-9431e39862de"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'love going happy hour dinner great patio beat heat alsovery accomodating location like veal milanese mixed green instead pasta theyll modify menu suit taste'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df.to_csv('/content/gdrive/MyDrive/data/yelp_reviews_pre_processed_v2.csv', index = False)"
      ],
      "metadata": {
        "id": "m2gkVi83txjl"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df = pd.read_csv('/content/gdrive/MyDrive/data/yelp_reviews_pre_processed_v2.csv')"
      ],
      "metadata": {
        "id": "UdiIQGmkamjR"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "OmWMPf4vt4M9",
        "outputId": "6471e205-071c-4bdd-8f80-192561db7f7b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  user_id             business_id                 date  stars  \\\n",
              "0  9OAtfnWag-ajVxRbUTGIyg  lj-E32x9_FA7GmUrBGBEWg  2014-06-27 22:44:01    4.0   \n",
              "1  5h9JA231vPilNAIjHxwGng  lj-E32x9_FA7GmUrBGBEWg  2014-08-24 19:24:26    5.0   \n",
              "2  c1fSI6Dv5lybr0AJh67e7w  lj-E32x9_FA7GmUrBGBEWg  2015-09-24 15:01:11    4.0   \n",
              "3  st-q1iyW3sJm-v0OCrheoQ  lj-E32x9_FA7GmUrBGBEWg  2015-10-17 04:56:25    5.0   \n",
              "4  9qBdzBzoDxLFSMhhGrTWJg  lj-E32x9_FA7GmUrBGBEWg  2016-01-04 16:56:32    4.0   \n",
              "\n",
              "                                                text  review_year  sentiment  \\\n",
              "0  Love going here for happy hour or dinner!  Gre...         2014          1   \n",
              "1  My friends at work (connoisseurs of good food ...         2014          1   \n",
              "2  Great service, relaxing atmosphere and the foo...         2015          1   \n",
              "3  I went to Brio for the first time on Wednesday...         2015          1   \n",
              "4  I usually steer clear of the chain restaurants...         2016          1   \n",
              "\n",
              "                                        cleaned_text  \\\n",
              "0  [love, going, happy, hour, dinner, great, pati...   \n",
              "1  [friend, work, connoisseur, good, food, louis,...   \n",
              "2  [great, service, relaxing, atmosphere, food, e...   \n",
              "3  [went, brio, first, time, wednesday, dinner, f...   \n",
              "4  [usually, steer, clear, chain, restaurant, sin...   \n",
              "\n",
              "                                final_processed_text  text length  \n",
              "0  love going happy hour dinner great patio beat ...          242  \n",
              "1  friend work connoisseur good food louis area i...          305  \n",
              "2  great service relaxing atmosphere food excepti...          130  \n",
              "3  went brio first time wednesday dinner friend s...          898  \n",
              "4  usually steer clear chain restaurant since man...          336  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>business_id</th>\n",
              "      <th>date</th>\n",
              "      <th>stars</th>\n",
              "      <th>text</th>\n",
              "      <th>review_year</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>cleaned_text</th>\n",
              "      <th>final_processed_text</th>\n",
              "      <th>text length</th>\n",
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              "      <th>0</th>\n",
              "      <td>9OAtfnWag-ajVxRbUTGIyg</td>\n",
              "      <td>lj-E32x9_FA7GmUrBGBEWg</td>\n",
              "      <td>2014-06-27 22:44:01</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Love going here for happy hour or dinner!  Gre...</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>[love, going, happy, hour, dinner, great, pati...</td>\n",
              "      <td>love going happy hour dinner great patio beat ...</td>\n",
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              "      <td>lj-E32x9_FA7GmUrBGBEWg</td>\n",
              "      <td>2014-08-24 19:24:26</td>\n",
              "      <td>5.0</td>\n",
              "      <td>My friends at work (connoisseurs of good food ...</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>[friend, work, connoisseur, good, food, louis,...</td>\n",
              "      <td>friend work connoisseur good food louis area i...</td>\n",
              "      <td>305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c1fSI6Dv5lybr0AJh67e7w</td>\n",
              "      <td>lj-E32x9_FA7GmUrBGBEWg</td>\n",
              "      <td>2015-09-24 15:01:11</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Great service, relaxing atmosphere and the foo...</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>[great, service, relaxing, atmosphere, food, e...</td>\n",
              "      <td>great service relaxing atmosphere food excepti...</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>st-q1iyW3sJm-v0OCrheoQ</td>\n",
              "      <td>lj-E32x9_FA7GmUrBGBEWg</td>\n",
              "      <td>2015-10-17 04:56:25</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I went to Brio for the first time on Wednesday...</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>[went, brio, first, time, wednesday, dinner, f...</td>\n",
              "      <td>went brio first time wednesday dinner friend s...</td>\n",
              "      <td>898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9qBdzBzoDxLFSMhhGrTWJg</td>\n",
              "      <td>lj-E32x9_FA7GmUrBGBEWg</td>\n",
              "      <td>2016-01-04 16:56:32</td>\n",
              "      <td>4.0</td>\n",
              "      <td>I usually steer clear of the chain restaurants...</td>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>[usually, steer, clear, chain, restaurant, sin...</td>\n",
              "      <td>usually steer clear chain restaurant since man...</td>\n",
              "      <td>336</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f966dab6-e9a1-4ed1-b55e-f44f3fdc4af2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f966dab6-e9a1-4ed1-b55e-f44f3fdc4af2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets visualise the review text and stars for a business\n",
        "g = sns.FacetGrid(data=df, col='stars')\n",
        "g.map(plt.hist, 'text length', bins=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "dRBhqCZ_s9LJ",
        "outputId": "bdb6e01f-e046-4f84-d65f-7774eae9f09c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f90ac63fd90>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8g_YzUYfuxQ0"
      },
      "source": [
        "## Create Train/Validation/Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "X-9E7x3XuxQ0"
      },
      "outputs": [],
      "source": [
        "X_train_set, X_test_set, y_train, y_test = train_test_split(df['final_processed_text'], \n",
        "                                                    df['stars'], \n",
        "                                                    test_size=0.30, \n",
        "                                                    random_state=42)\n",
        "\n",
        "# X_test_set, X_val_set, y_test, y_val = train_test_split(X_nottrain, \n",
        "#                                                     y_nottrain, \n",
        "#                                                     test_size=0.5, \n",
        "#                                                     random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Djzofqy4f6s",
        "outputId": "464e408a-a219-4148-af29-26b1f89d39aa"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "246153    mediocre food nothing cracked recommend lunch ...\n",
              "1749      visited thursday night dinner elsewhere great ...\n",
              "18036     mediterranean american food intimate upscale s...\n",
              "118652    excited pretty busy meat dried like sitting he...\n",
              "147620    told multiple people restaurant great correct ...\n",
              "Name: final_processed_text, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aH9EZqlQuxQ0",
        "outputId": "e9fa1cb5-4347-494e-e0b2-022e6dccff8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0    86821\n",
              "4.0    51541\n",
              "3.0    24769\n",
              "1.0    23151\n",
              "2.0    18381\n",
              "Name: stars, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0OP2sZ2uxQ0",
        "outputId": "d674cec3-b550-4820-d364-ec669ff816ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0    37426\n",
              "4.0    22107\n",
              "3.0    10576\n",
              "1.0     9722\n",
              "2.0     7882\n",
              "Name: stars, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "y_test.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tc74TbMwuxQ0"
      },
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqdIAVzzuxQ1"
      },
      "source": [
        "**1-gram Count**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "H2e29xc0uxQ1"
      },
      "outputs": [],
      "source": [
        "cv = CountVectorizer(binary=False)\n",
        "cv.fit(X_train_set)\n",
        "X_train_1gram_count = cv.transform(X_train_set)\n",
        "X_test_1gram_count = cv.transform(X_test_set)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_1gram_count.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qpAGbL9Ee0gZ",
        "outputId": "6d0e58de-eab4-4944-9bf5-3865d51007a9"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 123689)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1-gram Count, max 10,000 words**"
      ],
      "metadata": {
        "id": "sLUMeNKhfICY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer(binary=False, max_features = 10000)\n",
        "cv.fit(X_train_set)\n",
        "X_train_1gram_count_10000 = cv.transform(X_train_set)\n",
        "X_test_1gram_count_10000 = cv.transform(X_test_set)"
      ],
      "metadata": {
        "id": "_W7UnveOepqT"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_1gram_count_10000.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uk2fqEUkfW6y",
        "outputId": "57254afe-3508-48cd-cad7-95e3f373728b"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgUMPSl0uxQ1"
      },
      "source": [
        "**2-gram Count**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer(binary=False, ngram_range = (1,2))\n",
        "cv.fit(X_train_set)\n",
        "X_train_2gram_count = cv.transform(X_train_set)\n",
        "X_test_2gram_count = cv.transform(X_test_set)"
      ],
      "metadata": {
        "id": "0Jv8etP3feJR"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_2gram_count.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UlQzrWRfk5v",
        "outputId": "27d41ceb-6ca9-41d6-9517-0481655ec698"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 2884128)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2-gram Count, max 10,000 words**"
      ],
      "metadata": {
        "id": "8jFWAY6WfmMM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer(binary=False, ngram_range = (1,2), max_features = 10000)\n",
        "cv.fit(X_train_set)\n",
        "X_train_2gram_count_10000 = cv.transform(X_train_set)\n",
        "X_test_2gram_count_10000 = cv.transform(X_test_set)"
      ],
      "metadata": {
        "id": "yqlW7m-_fmXH"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_2gram_count_10000.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ogwK4udfuHq",
        "outputId": "1857736e-95e9-4a39-9e84-621bb0da3516"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EE1VHvGouxQ1"
      },
      "source": [
        "**1-gram Tf-Idf**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "7pKG6CERuxQ1"
      },
      "outputs": [],
      "source": [
        "cv = TfidfVectorizer()\n",
        "cv.fit(X_train_set)\n",
        "X_train_1tfidf = cv.transform(X_train_set)\n",
        "X_test_1tfidf = cv.transform(X_test_set)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_1tfidf.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8XLGxKUf2q1",
        "outputId": "e2a20f5c-7544-49b9-88fd-40ad403808b9"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 123689)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1-gram Tf-Idf, max 10000 words**"
      ],
      "metadata": {
        "id": "JW4eJ6wBgLjy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv = TfidfVectorizer(max_features = 10000)\n",
        "cv.fit(X_train_set)\n",
        "X_train_1tfidf_10000 = cv.transform(X_train_set)\n",
        "X_test_1tfidf_10000 = cv.transform(X_test_set)"
      ],
      "metadata": {
        "id": "UuDxYqFogLrP"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_1tfidf_10000.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0YO6d44gLtQ",
        "outputId": "380a8d60-111e-430e-cc69-40233bc946b7"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cShwCuSBuxQ2"
      },
      "source": [
        "**2-gram Tf-Idf**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv = TfidfVectorizer(ngram_range=(1,2))\n",
        "cv.fit(X_train_set)\n",
        "X_train_2tfidf = cv.transform(X_train_set)\n",
        "X_test_2tfidf = cv.transform(X_test_set)"
      ],
      "metadata": {
        "id": "zkVDT_BvgzHi"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_2tfidf.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2m4v8X9gzJx",
        "outputId": "66beabf3-d1c3-4875-8400-a57744b9f9f6"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 2884128)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2-gram Tf-Idf, max 10000 words****"
      ],
      "metadata": {
        "id": "bttlULfog5PZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "OdcuPWA8uxQ2"
      },
      "outputs": [],
      "source": [
        "cv = TfidfVectorizer(ngram_range=(1,2), max_features = 10000)\n",
        "cv.fit(X_train_set)\n",
        "X_train_2tfidf_10000 = cv.transform(X_train_set)\n",
        "X_test_2tfidf_10000 = cv.transform(X_test_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyYwc1LkuxQ2",
        "outputId": "93d6847b-1a43-46ca-df9b-60de3d3ce6b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(204663, 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "X_train_2tfidf_10000.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_true, y_pred):\n",
        "    mtx = confusion_matrix(y_true, y_pred)\n",
        "    fig, ax = plt.subplots(figsize=(8,8))\n",
        "    sns.heatmap(mtx, annot=True, fmt='d', linewidths=.75,  cbar=False, ax=ax,cmap='Blues',linecolor='white')\n",
        "    #  square=True,\n",
        "    plt.ylabel('true label')\n",
        "    plt.xlabel('predicted label')\n",
        "    ax.xaxis.set_ticklabels(['1', '2', '3', '4', '5'])\n",
        "    ax.yaxis.set_ticklabels(['1', '2', '3', '4', '5'])"
      ],
      "metadata": {
        "id": "jqDw5SSQzK4H"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_validation_report(test_labels, predictions):\n",
        "    print(\"Classification Report\")\n",
        "    print(classification_report(test_labels, predictions))\n",
        "    print('Accuracy Score: {:.3f}'.format(accuracy_score(test_labels, predictions)))\n",
        "    print('Mean Absolute Error: {:.3f}'.format(mean_absolute_error(test_labels, predictions)))\n",
        "    print('Root Mean Square Error: {:.3f}'.format(np.sqrt(MSE(test_labels, predictions))))\n",
        "    print('Mean Square Error: {:.3f}'.format(MSE(test_labels, predictions, squared = True)))"
      ],
      "metadata": {
        "id": "kl0iW1lU2Rlo"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-OPwB5euxQ2"
      },
      "source": [
        "## Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Naive Bayes"
      ],
      "metadata": {
        "id": "udeC9wEYHs_a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list1 = [X_train_1gram_count, X_train_1gram_count_10000, X_train_2gram_count, X_train_2gram_count_10000, X_train_1tfidf, X_train_1tfidf_10000, X_train_2tfidf, X_train_2tfidf_10000]\n",
        "list2 = [X_test_1gram_count, X_test_1gram_count_10000, X_test_2gram_count, X_test_2gram_count_10000, X_test_1tfidf, X_test_1tfidf_10000, X_test_2tfidf, X_test_2tfidf_10000]\n",
        "list3 = ['1-gram count', '1-gram count max 10,000 words', '2-gram count', '2-gram count, max 10,000 words', '1-gram tf-idf', '1-gram tf-idf max 10,000 words', '2-gram tf-idf', '2-gram tf-idf, max 10,000 words']\n",
        "for i,j,k in zip(list1, list2, list3):\n",
        "  nb = MultinomialNB()\n",
        "  nb.fit(i, y_train)\n",
        "  preds = nb.predict(j)\n",
        "  print(k)\n",
        "  print(print_validation_report(y_test,preds),'\\n')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVELZU8JiJi9",
        "outputId": "d796e111-e7ec-40ee-dce6-8944872d77d6"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1-gram count\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.62      0.72      0.66      9722\n",
            "         2.0       0.40      0.26      0.31      7882\n",
            "         3.0       0.42      0.30      0.35     10576\n",
            "         4.0       0.50      0.48      0.49     22107\n",
            "         5.0       0.74      0.84      0.78     37426\n",
            "\n",
            "    accuracy                           0.62     87713\n",
            "   macro avg       0.54      0.52      0.52     87713\n",
            "weighted avg       0.60      0.62      0.60     87713\n",
            "\n",
            "Accuracy Score: 0.617\n",
            "Mean Absolute Error: 0.486\n",
            "Root Mean Square Error: 0.878\n",
            "Mean Square Error: 0.772\n",
            "None \n",
            "\n",
            "1-gram count max 10,000 words\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.61      0.70      0.65      9722\n",
            "         2.0       0.38      0.34      0.36      7882\n",
            "         3.0       0.42      0.37      0.40     10576\n",
            "         4.0       0.52      0.46      0.49     22107\n",
            "         5.0       0.75      0.82      0.78     37426\n",
            "\n",
            "    accuracy                           0.62     87713\n",
            "   macro avg       0.54      0.54      0.54     87713\n",
            "weighted avg       0.60      0.62      0.61     87713\n",
            "\n",
            "Accuracy Score: 0.618\n",
            "Mean Absolute Error: 0.484\n",
            "Root Mean Square Error: 0.877\n",
            "Mean Square Error: 0.769\n",
            "None \n",
            "\n",
            "2-gram count\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.68      0.72      0.70      9722\n",
            "         2.0       0.47      0.06      0.10      7882\n",
            "         3.0       0.37      0.07      0.12     10576\n",
            "         4.0       0.39      0.42      0.40     22107\n",
            "         5.0       0.68      0.92      0.78     37426\n",
            "\n",
            "    accuracy                           0.59     87713\n",
            "   macro avg       0.52      0.44      0.42     87713\n",
            "weighted avg       0.55      0.59      0.54     87713\n",
            "\n",
            "Accuracy Score: 0.591\n",
            "Mean Absolute Error: 0.569\n",
            "Root Mean Square Error: 1.001\n",
            "Mean Square Error: 1.002\n",
            "None \n",
            "\n",
            "2-gram count, max 10,000 words\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.64      0.71      0.67      9722\n",
            "         2.0       0.40      0.37      0.39      7882\n",
            "         3.0       0.43      0.41      0.42     10576\n",
            "         4.0       0.52      0.49      0.51     22107\n",
            "         5.0       0.77      0.80      0.78     37426\n",
            "\n",
            "    accuracy                           0.63     87713\n",
            "   macro avg       0.55      0.56      0.55     87713\n",
            "weighted avg       0.62      0.63      0.62     87713\n",
            "\n",
            "Accuracy Score: 0.626\n",
            "Mean Absolute Error: 0.466\n",
            "Root Mean Square Error: 0.849\n",
            "Mean Square Error: 0.721\n",
            "None \n",
            "\n",
            "1-gram tf-idf\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.75      0.51      0.61      9722\n",
            "         2.0       0.45      0.00      0.00      7882\n",
            "         3.0       0.37      0.00      0.01     10576\n",
            "         4.0       0.23      0.10      0.14     22107\n",
            "         5.0       0.52      0.99      0.68     37426\n",
            "\n",
            "    accuracy                           0.50     87713\n",
            "   macro avg       0.46      0.32      0.29     87713\n",
            "weighted avg       0.45      0.50      0.39     87713\n",
            "\n",
            "Accuracy Score: 0.504\n",
            "Mean Absolute Error: 0.853\n",
            "Root Mean Square Error: 1.391\n",
            "Mean Square Error: 1.934\n",
            "None \n",
            "\n",
            "1-gram tf-idf max 10,000 words\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.62      0.73      0.67      9722\n",
            "         2.0       0.45      0.14      0.21      7882\n",
            "         3.0       0.40      0.12      0.18     10576\n",
            "         4.0       0.43      0.33      0.37     22107\n",
            "         5.0       0.64      0.93      0.76     37426\n",
            "\n",
            "    accuracy                           0.59     87713\n",
            "   macro avg       0.51      0.45      0.44     87713\n",
            "weighted avg       0.54      0.59      0.53     87713\n",
            "\n",
            "Accuracy Score: 0.586\n",
            "Mean Absolute Error: 0.575\n",
            "Root Mean Square Error: 1.013\n",
            "Mean Square Error: 1.027\n",
            "None \n",
            "\n",
            "2-gram tf-idf\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.89      0.16      0.27      9722\n",
            "         2.0       0.00      0.00      0.00      7882\n",
            "         3.0       0.40      0.00      0.00     10576\n",
            "         4.0       0.20      0.01      0.01     22107\n",
            "         5.0       0.44      1.00      0.61     37426\n",
            "\n",
            "    accuracy                           0.45     87713\n",
            "   macro avg       0.38      0.23      0.18     87713\n",
            "weighted avg       0.38      0.45      0.29     87713\n",
            "\n",
            "Accuracy Score: 0.446\n",
            "Mean Absolute Error: 1.124\n",
            "Root Mean Square Error: 1.730\n",
            "Mean Square Error: 2.994\n",
            "None \n",
            "\n",
            "2-gram tf-idf, max 10,000 words\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.63      0.75      0.68      9722\n",
            "         2.0       0.47      0.21      0.29      7882\n",
            "         3.0       0.47      0.22      0.30     10576\n",
            "         4.0       0.49      0.44      0.46     22107\n",
            "         5.0       0.70      0.89      0.78     37426\n",
            "\n",
            "    accuracy                           0.62     87713\n",
            "   macro avg       0.55      0.50      0.50     87713\n",
            "weighted avg       0.59      0.62      0.59     87713\n",
            "\n",
            "Accuracy Score: 0.619\n",
            "Mean Absolute Error: 0.501\n",
            "Root Mean Square Error: 0.915\n",
            "Mean Square Error: 0.836\n",
            "None \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nb = MultinomialNB()\n",
        "nb.fit(X_train_2gram_count_10000, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3COsVtwVxPoB",
        "outputId": "c8191157-b846-491e-9882-102f920e6670"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = nb.predict(X_test_2gram_count_10000)"
      ],
      "metadata": {
        "id": "MAiX-PI-uHKc"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(print_validation_report(y_test,preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S11JVtdT2gKz",
        "outputId": "a34c74c3-2caa-444a-a50d-bfd2c6f5d6de"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.64      0.71      0.67      9722\n",
            "         2.0       0.40      0.37      0.39      7882\n",
            "         3.0       0.43      0.41      0.42     10576\n",
            "         4.0       0.52      0.49      0.51     22107\n",
            "         5.0       0.77      0.80      0.78     37426\n",
            "\n",
            "    accuracy                           0.63     87713\n",
            "   macro avg       0.55      0.56      0.55     87713\n",
            "weighted avg       0.62      0.63      0.62     87713\n",
            "\n",
            "Accuracy Score: 0.626\n",
            "Mean Absolute Error: 0.466\n",
            "Root Mean Square Error: 0.849\n",
            "Mean Square Error: 0.721\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_test,preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "gbe5Tl2AzUJZ",
        "outputId": "e65f6d06-f70c-4a9c-e2b5-e0c63ccf57bc"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEl0B-dDuxQ2"
      },
      "source": [
        "###Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68dnsKacuxQ3",
        "outputId": "c70b0031-b20b-45e6-cad8-7e6fc8f943fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For C = 0.01\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.68      0.78      0.73      9722\n",
            "         2.0       0.49      0.32      0.39      7882\n",
            "         3.0       0.51      0.35      0.42     10576\n",
            "         4.0       0.55      0.44      0.49     22107\n",
            "         5.0       0.71      0.89      0.79     37426\n",
            "\n",
            "    accuracy                           0.65     87713\n",
            "   macro avg       0.59      0.55      0.56     87713\n",
            "weighted avg       0.62      0.65      0.63     87713\n",
            "\n",
            "Accuracy Score: 0.646\n",
            "Mean Absolute Error: 0.445\n",
            "Root Mean Square Error: 0.837\n",
            "Mean Square Error: 0.700\n",
            "None \n",
            "\n",
            "For C = 0.05\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.68      0.77      0.72      9722\n",
            "         2.0       0.48      0.36      0.41      7882\n",
            "         3.0       0.49      0.38      0.43     10576\n",
            "         4.0       0.55      0.45      0.49     22107\n",
            "         5.0       0.73      0.87      0.79     37426\n",
            "\n",
            "    accuracy                           0.65     87713\n",
            "   macro avg       0.59      0.56      0.57     87713\n",
            "weighted avg       0.63      0.65      0.63     87713\n",
            "\n",
            "Accuracy Score: 0.648\n",
            "Mean Absolute Error: 0.430\n",
            "Root Mean Square Error: 0.802\n",
            "Mean Square Error: 0.643\n",
            "None \n",
            "\n",
            "For C = 0.25\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.68      0.75      0.71      9722\n",
            "         2.0       0.46      0.37      0.41      7882\n",
            "         3.0       0.47      0.38      0.42     10576\n",
            "         4.0       0.54      0.45      0.49     22107\n",
            "         5.0       0.74      0.86      0.79     37426\n",
            "\n",
            "    accuracy                           0.64     87713\n",
            "   macro avg       0.58      0.56      0.56     87713\n",
            "weighted avg       0.62      0.64      0.63     87713\n",
            "\n",
            "Accuracy Score: 0.641\n",
            "Mean Absolute Error: 0.435\n",
            "Root Mean Square Error: 0.801\n",
            "Mean Square Error: 0.641\n",
            "None \n",
            "\n",
            "For C = 0.5\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.67      0.74      0.70      9722\n",
            "         2.0       0.45      0.37      0.41      7882\n",
            "         3.0       0.46      0.38      0.42     10576\n",
            "         4.0       0.54      0.45      0.49     22107\n",
            "         5.0       0.74      0.85      0.79     37426\n",
            "\n",
            "    accuracy                           0.64     87713\n",
            "   macro avg       0.57      0.56      0.56     87713\n",
            "weighted avg       0.62      0.64      0.63     87713\n",
            "\n",
            "Accuracy Score: 0.638\n",
            "Mean Absolute Error: 0.439\n",
            "Root Mean Square Error: 0.804\n",
            "Mean Square Error: 0.646\n",
            "None \n",
            "\n",
            "For C = 1\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.67      0.73      0.70      9722\n",
            "         2.0       0.44      0.37      0.40      7882\n",
            "         3.0       0.45      0.38      0.42     10576\n",
            "         4.0       0.53      0.45      0.49     22107\n",
            "         5.0       0.74      0.85      0.79     37426\n",
            "\n",
            "    accuracy                           0.64     87713\n",
            "   macro avg       0.57      0.56      0.56     87713\n",
            "weighted avg       0.62      0.64      0.62     87713\n",
            "\n",
            "Accuracy Score: 0.636\n",
            "Mean Absolute Error: 0.441\n",
            "Root Mean Square Error: 0.805\n",
            "Mean Square Error: 0.647\n",
            "None \n",
            "\n"
          ]
        }
      ],
      "source": [
        "#2-gram count with maximum 10,000 words\n",
        "for c in [0.01, 0.05, 0.25, 0.5, 1]:\n",
        "  lr = LogisticRegression(C=c, max_iter= 300)\n",
        "  lr.fit(X_train_2gram_count_10000, y_train)\n",
        "  preds = lr.predict(X_test_2gram_count_10000)\n",
        "  print(\"For C = %s\" % (c))\n",
        "  print(print_validation_report(y_test,preds), '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2 gram tf-idf with maximum 10,000 words\n",
        "for c in [0.01, 0.05, 0.25, 0.5, 1]:\n",
        "  lr = LogisticRegression(C=c, max_iter= 300)\n",
        "  lr.fit(X_train_2tfidf_10000, y_train)\n",
        "  preds = lr.predict(X_test_2tfidf_10000)\n",
        "  print(\"For C = %s\" % (c))\n",
        "  print(print_validation_report(y_test,preds), '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxHKOJP3AzDZ",
        "outputId": "063e840b-5b00-48f3-d6e3-2773d3ffc349"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For C = 0.01\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.67      0.62      0.65      9722\n",
            "         2.0       0.56      0.01      0.03      7882\n",
            "         3.0       0.43      0.05      0.09     10576\n",
            "         4.0       0.41      0.34      0.37     22107\n",
            "         5.0       0.59      0.94      0.73     37426\n",
            "\n",
            "    accuracy                           0.56     87713\n",
            "   macro avg       0.53      0.39      0.37     87713\n",
            "weighted avg       0.53      0.56      0.49     87713\n",
            "\n",
            "Accuracy Score: 0.561\n",
            "Mean Absolute Error: 0.702\n",
            "Root Mean Square Error: 1.227\n",
            "Mean Square Error: 1.505\n",
            "None \n",
            "\n",
            "For C = 0.05\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.67      0.77      0.72      9722\n",
            "         2.0       0.50      0.20      0.28      7882\n",
            "         3.0       0.49      0.25      0.33     10576\n",
            "         4.0       0.50      0.46      0.48     22107\n",
            "         5.0       0.70      0.89      0.78     37426\n",
            "\n",
            "    accuracy                           0.63     87713\n",
            "   macro avg       0.57      0.51      0.52     87713\n",
            "weighted avg       0.60      0.63      0.60     87713\n",
            "\n",
            "Accuracy Score: 0.629\n",
            "Mean Absolute Error: 0.491\n",
            "Root Mean Square Error: 0.911\n",
            "Mean Square Error: 0.829\n",
            "None \n",
            "\n",
            "For C = 0.25\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.68      0.77      0.73      9722\n",
            "         2.0       0.49      0.32      0.39      7882\n",
            "         3.0       0.50      0.35      0.41     10576\n",
            "         4.0       0.54      0.49      0.51     22107\n",
            "         5.0       0.74      0.87      0.80     37426\n",
            "\n",
            "    accuracy                           0.65     87713\n",
            "   macro avg       0.59      0.56      0.57     87713\n",
            "weighted avg       0.63      0.65      0.63     87713\n",
            "\n",
            "Accuracy Score: 0.650\n",
            "Mean Absolute Error: 0.431\n",
            "Root Mean Square Error: 0.807\n",
            "Mean Square Error: 0.651\n",
            "None \n",
            "\n",
            "For C = 0.5\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.69      0.77      0.73      9722\n",
            "         2.0       0.49      0.35      0.41      7882\n",
            "         3.0       0.50      0.38      0.43     10576\n",
            "         4.0       0.54      0.49      0.52     22107\n",
            "         5.0       0.75      0.86      0.80     37426\n",
            "\n",
            "    accuracy                           0.65     87713\n",
            "   macro avg       0.59      0.57      0.58     87713\n",
            "weighted avg       0.64      0.65      0.64     87713\n",
            "\n",
            "Accuracy Score: 0.653\n",
            "Mean Absolute Error: 0.420\n",
            "Root Mean Square Error: 0.787\n",
            "Mean Square Error: 0.619\n",
            "None \n",
            "\n",
            "For C = 1\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.69      0.77      0.73      9722\n",
            "         2.0       0.48      0.37      0.42      7882\n",
            "         3.0       0.49      0.39      0.44     10576\n",
            "         4.0       0.54      0.49      0.52     22107\n",
            "         5.0       0.75      0.85      0.80     37426\n",
            "\n",
            "    accuracy                           0.65     87713\n",
            "   macro avg       0.59      0.57      0.58     87713\n",
            "weighted avg       0.64      0.65      0.64     87713\n",
            "\n",
            "Accuracy Score: 0.653\n",
            "Mean Absolute Error: 0.415\n",
            "Root Mean Square Error: 0.773\n",
            "Mean Square Error: 0.598\n",
            "None \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression(C=1, max_iter= 300)\n",
        "lr.fit(X_train_2tfidf_10000, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hfsxq6_i4aeV",
        "outputId": "4abb0b11-e3cc-4293-840a-8e81dc161b45"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1, max_iter=300)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = lr.predict(X_test_2tfidf_10000)"
      ],
      "metadata": {
        "id": "sFE7lY6c4yn9"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(print_validation_report(y_test,preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2yTdQ5iy5RyI",
        "outputId": "53cc0581-82f8-40c8-9aa9-d44d5bf6eb88"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.69      0.77      0.73      9722\n",
            "         2.0       0.48      0.37      0.42      7882\n",
            "         3.0       0.49      0.39      0.44     10576\n",
            "         4.0       0.54      0.49      0.52     22107\n",
            "         5.0       0.75      0.85      0.80     37426\n",
            "\n",
            "    accuracy                           0.65     87713\n",
            "   macro avg       0.59      0.57      0.58     87713\n",
            "weighted avg       0.64      0.65      0.64     87713\n",
            "\n",
            "Accuracy Score: 0.653\n",
            "Mean Absolute Error: 0.415\n",
            "Root Mean Square Error: 0.773\n",
            "Mean Square Error: 0.598\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_test,preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "qx4rs6Kz5YA7",
        "outputId": "a7c15f0c-f810-44d2-e924-13e3a44a707e"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_to_coef = {\n",
        "    word: coef for word, coef in zip(\n",
        "        cv.get_feature_names(), lr.coef_[0]\n",
        "    )\n",
        "}\n",
        "print(\"Best Positive\")\n",
        "for best_positive in sorted(\n",
        "    feature_to_coef.items(), \n",
        "    key=lambda x: x[1])[:50]:\n",
        "    print (best_positive)\n",
        "\n",
        "print(\"Best Negative\")\n",
        "\n",
        "for best_negative in sorted(\n",
        "    feature_to_coef.items(), \n",
        "    key=lambda x: x[1],\n",
        "    reverse=True)[:50]:\n",
        "    print (best_negative)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30WuPVI25Rz2",
        "outputId": "6c7f84cc-a38f-4930-e38b-d1c51e0392f3"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Positive\n",
            "('delicious', -10.15162007508786)\n",
            "('great', -9.213434246522137)\n",
            "('amazing', -7.542900200974198)\n",
            "('excellent', -7.135017285474752)\n",
            "('good', -6.646815537796806)\n",
            "('tasty', -6.148659351851435)\n",
            "('best', -5.577491286234105)\n",
            "('fantastic', -5.430197090033788)\n",
            "('awesome', -5.380214432645352)\n",
            "('love', -5.202640701766352)\n",
            "('perfect', -4.848686232099752)\n",
            "('loved', -4.345781157611943)\n",
            "('friendly', -4.319273930785958)\n",
            "('nice', -4.311158904189648)\n",
            "('enjoyed', -4.172659653834047)\n",
            "('favorite', -4.022751718634195)\n",
            "('wonderful', -3.992313184242836)\n",
            "('outstanding', -3.87212845886514)\n",
            "('little', -3.6416516170256963)\n",
            "('yummy', -3.641571921525314)\n",
            "('always', -3.583600964317922)\n",
            "('perfectly', -3.5576761385116593)\n",
            "('star', -3.4936945719148076)\n",
            "('attentive', -3.4154052759753712)\n",
            "('flavorful', -3.3814399183264423)\n",
            "('food good', -3.1035821604868796)\n",
            "('definitely', -3.073675964081823)\n",
            "('juicy', -3.0698933410437435)\n",
            "('incredible', -3.000561567668854)\n",
            "('really good', -2.8564078578564187)\n",
            "('complaint', -2.844465468946024)\n",
            "('solid', -2.77768360839085)\n",
            "('food delicious', -2.7227866575656345)\n",
            "('unique', -2.6888112685064685)\n",
            "('delish', -2.6807838984464487)\n",
            "('happy', -2.66330517449041)\n",
            "('liked', -2.6358708222637186)\n",
            "('atmosphere', -2.6115420685456927)\n",
            "('phenomenal', -2.5547463756481585)\n",
            "('never disappointed', -2.498594048702917)\n",
            "('wont disappointed', -2.4925303311740916)\n",
            "('great food', -2.491966769511713)\n",
            "('pretty', -2.480057504545887)\n",
            "('though', -2.472028366578066)\n",
            "('clean', -2.467983568366585)\n",
            "('generous', -2.4416926268372734)\n",
            "('variety', -2.4294545464259345)\n",
            "('kind', -2.399084396238069)\n",
            "('quick', -2.3812367737623874)\n",
            "('glad', -2.365675152115534)\n",
            "Best Negative\n",
            "('worst', 7.835360620285278)\n",
            "('horrible', 6.29062106490252)\n",
            "('terrible', 6.263986350295146)\n",
            "('awful', 5.558539526466968)\n",
            "('never back', 4.995079155668756)\n",
            "('disgusting', 4.726982834293021)\n",
            "('tasteless', 4.519656335910152)\n",
            "('rude', 4.319196923547396)\n",
            "('gross', 4.206308585894989)\n",
            "('wont back', 4.036005670127348)\n",
            "('disappointing', 3.986368679358215)\n",
            "('poor', 3.8884513928486215)\n",
            "('bland', 3.8817464547922826)\n",
            "('waste', 3.7066979963988103)\n",
            "('worse', 3.609142297389194)\n",
            "('left', 3.596359873505592)\n",
            "('inedible', 3.5179815926357625)\n",
            "('overpriced', 3.4370552803285372)\n",
            "('poorly', 3.3525010140003997)\n",
            "('dirty', 3.3407838681505027)\n",
            "('refused', 3.3283863170990404)\n",
            "('zero star', 3.3152313104428384)\n",
            "('never', 3.3092564430458022)\n",
            "('food poisoning', 3.2306087504294263)\n",
            "('zero', 3.2015905420389523)\n",
            "('poisoning', 3.1874840588947517)\n",
            "('sick', 3.1620734417334058)\n",
            "('flavorless', 3.159564473033269)\n",
            "('edible', 3.1559093525545325)\n",
            "('nasty', 3.1309983791936618)\n",
            "('avoid', 3.128800861535342)\n",
            "('cold', 3.0979022654416903)\n",
            "('disappointment', 3.0257570592109464)\n",
            "('suck', 3.0135129720370886)\n",
            "('joke', 2.9882307800074726)\n",
            "('yuck', 2.97916207711174)\n",
            "('wont', 2.9690145145214224)\n",
            "('disappointed', 2.9451257081699116)\n",
            "('first last', 2.9206865475971444)\n",
            "('even', 2.9195914814657016)\n",
            "('money', 2.911117956079832)\n",
            "('tasted like', 2.8531598152078477)\n",
            "('hair', 2.8370055353946846)\n",
            "('star would', 2.819798527296605)\n",
            "('overcooked', 2.7912974716743935)\n",
            "('frozen', 2.773388404970191)\n",
            "('mediocre best', 2.7506934810654142)\n",
            "('health', 2.7238335753429306)\n",
            "('soggy', 2.690753398840926)\n",
            "('returning', 2.6714337503924934)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Linear Regression"
      ],
      "metadata": {
        "id": "5sxDltyWbv18"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(X_train_2gram_count_10000, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-Imv3LtbxgY",
        "outputId": "a749062b-b6f2-4392-b93c-fdecf8ca2a88"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = lr.predict(X_test_2gram_count_10000)"
      ],
      "metadata": {
        "id": "FmZ_LFsqcBcI"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean Absolute Error: {:.3f}'.format(mean_absolute_error(y_test,preds)))\n",
        "print('Root Mean Square Error: {:.3f}'.format(np.sqrt(MSE(y_test,preds))))\n",
        "print('Mean Square Error: {:.3f}'.format(MSE(y_test,preds, squared = True)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szFZVW3lgMsQ",
        "outputId": "5545a97b-a5fe-4365-d4ef-ed915fd59804"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 0.704\n",
            "Root Mean Square Error: 0.897\n",
            "Mean Square Error: 0.805\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AuDBO7XuuxQ3"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(max_features = 'sqrt')\n",
        "rf.fit(X_train_2tfidf_10000, y_train)"
      ],
      "metadata": {
        "id": "pql31ahmMkOx"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = rf.predict(X_test_2tfidf_10000)"
      ],
      "metadata": {
        "id": "zitotyTWE6PQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(print_validation_report(y_test,preds))"
      ],
      "metadata": {
        "id": "qHuHHM5AVSk4"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}