IMDB_Sentiment_Analysis_FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMDB Sentiment Analysis\n",
    "### Practical Data Science Final Project \n",
    "By Boushra Bendou and Jullia Montejo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nH-Gp0Tg1rIe"
   },
   "source": [
    "# Introduction and Motivation\n",
    "\n",
    "In this current era, where an abundance of digital content and entertainment are produced daily, consumers heavily rely on online ratings when deciding whether content is worth their time. Because of this, IMDb stands out as it offers consumers, from movie enthusiasts, professionals in the film industry, and casual viewers, a platform to share their reviews on movies. The vast amount of user-generated movie reviews on IMDb then contributes to the creation of a collective narrative about movies that helps consumers make decisions about their viewing choices.\n",
    "\n",
    "Our project's primary objective is to employ natural language processing techniques to build a predictive model which would conclude the underlying sentiments within user-generated IMDb reviews. Training our predictive model to detect patterns using supervised learning techniques on our labelled dataset enables it to precisely detect the sentiments behind user reviews on IMDb.\n",
    "\n",
    "Not only might casual movie viewers be influenced by our project's model but the professional film industry may also be impacted by it. Filmmakers may utilize sentiment analysis to better understand the nuanced responses of their audience and to influence and improve their next films in order to better appeal to the target market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents:\n",
    "1. [Data Collection](#Data-Collection)\n",
    "2. [Data Cleaning and Processing](#data-cleaning-and-processing)\n",
    "3. [Feature Extraction](#Feature-Extraction)\n",
    "4. [Data Visualization](#data-visualization)\n",
    "5. [Modeling and Error Analysis](#modeling-and-error-analysis)\n",
    "6. [Conclusion](#conclusion)\n",
    "7. [References](#references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: textblob in /Users/andrei/anaconda3/lib/python3.11/site-packages (0.17.1)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install textblob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: vaderSentiment in /Users/andrei/anaconda3/lib/python3.11/site-packages (3.3.2)\n",
      "Requirement already satisfied: requests in /Users/andrei/anaconda3/lib/python3.11/site-packages (from vaderSentiment) (2.31.0)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install vaderSentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ey0uC354p3tI",
    "outputId": "2279ed03-13a2-4c6e-d1f1-00ce4b245095"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/andrei/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /Users/andrei/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /Users/andrei/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from joblib import Parallel, delayed\n",
    "from sklearn.model_selection import train_test_split\n",
    "import nltk\n",
    "import collections\n",
    "import string\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import gzip\n",
    "import csv\n",
    "import re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')\n",
    "from nltk import word_tokenize\n",
    "from wordcloud import WordCloud\n",
    "from nltk.util import ngrams\n",
    "from textblob import TextBlob\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DhpvvRb-1xRK"
   },
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project, we used the IMDB Review dataset from Stanford which was made specifically for sentiment analysis purposes. This dataset can be found at: https://ai.stanford.edu/~amaas/data/sentiment/\n",
    "\n",
    "Now lets load the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "GmVmnGtI2xZN",
    "outputId": "de0aacdf-9581-4d45-bcef-019f376cf951"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Story of a man who has unnatural feelings for ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>As a serious horror fan, I get that certain ma...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>It's strange, while the film features full X-r...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'Bloody Birthday' is an odd and, at times, hum...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I am a big fan of bad horrors, cheap horrors, ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>Trailers of this movie may show scenes of viol...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>It's not a brilliant idea to watch Hundstage i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>\"Hundstage\" is seidl's first fiction film (bef...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>Nick and Kelly are ready to be married but Tra...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>I've seen this story before but my kids haven'...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review  sentiment\n",
       "0      Story of a man who has unnatural feelings for ...          0\n",
       "1      As a serious horror fan, I get that certain ma...          0\n",
       "2      It's strange, while the film features full X-r...          0\n",
       "3      'Bloody Birthday' is an odd and, at times, hum...          0\n",
       "4      I am a big fan of bad horrors, cheap horrors, ...          0\n",
       "...                                                  ...        ...\n",
       "49995  Trailers of this movie may show scenes of viol...          1\n",
       "49996  It's not a brilliant idea to watch Hundstage i...          1\n",
       "49997  \"Hundstage\" is seidl's first fiction film (bef...          1\n",
       "49998  Nick and Kelly are ready to be married but Tra...          1\n",
       "49999  I've seen this story before but my kids haven'...          1\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('IMDB_reviews.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FvJvi4vq111c"
   },
   "source": [
    "# Data Cleaning and Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nOTwR-Q5bwWi"
   },
   "source": [
    "To preprocess the review text data, we will use a cleaning function that performs the following to each review:\n",
    "\n",
    "\n",
    "* Lower casing\n",
    "* Removing links\n",
    "* Removing special charcters\n",
    "* Removing Dashes\n",
    "* Removing hyphens\n",
    "* Keeping only letters and digits\n",
    "* Tokenization\n",
    "* Lemmatization\n",
    "* Removing stopwords\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "_4CSsSIkbpy0"
   },
   "outputs": [],
   "source": [
    "def clean_review(review):\n",
    "    # Step 0 - Remove HTML line break tags\n",
    "    review = re.sub(r'<br\\s*/?>', ' ', review)\n",
    "\n",
    "    #Step 1 - Normalize case\n",
    "    review = review.lower()\n",
    "    #Step 2 - remove links\n",
    "    review = re.sub(r'(?:http|https)://t\\.co/\\w{10}', ' ', review)\n",
    "\n",
    "    #Step 3 - Remove any non-ascii characters\n",
    "    review =  re.sub(r'[^\\x00-\\x7F]+',' ', review)\n",
    "\n",
    "    #Step 4 - check for apostrophes\n",
    "    review = re.sub(r\"'s\", \"\", review)\n",
    "    review = review.replace(\"'\", \"\")\n",
    "\n",
    "    #Step 5 - Remove hyphens\n",
    "    review = review.replace('-', '')\n",
    "    review = review.replace('.', '')\n",
    "    review = review.replace(',', '')\n",
    "\n",
    "    #Step 6 - Keep letters and digits only\n",
    "    review = re.sub(r'[^\\x00-\\x7F]+', ' ', review)\n",
    "\n",
    "    #Step 7 - Tokenize\n",
    "    tokenize = nltk.word_tokenize(review)\n",
    "\n",
    "    #Step 8 - Lemmatize\n",
    "    lemmatizer= nltk.stem.wordnet.WordNetLemmatizer()\n",
    "    lemmatize_text = map(lambda x: lemmatizer.lemmatize(x), tokenize)\n",
    "\n",
    "    #Step 8 - Remove stop words\n",
    "    stopwords = set(nltk.corpus.stopwords.words('english'))\n",
    "    remove_stop = list(filter(lambda x: x not in stopwords, lemmatize_text))\n",
    "\n",
    "    #Step 9 - Remove any empty strings from list\n",
    "    processed_list = list(filter(lambda a: a != \"\", remove_stop))\n",
    "\n",
    "    #Step 10 - Join everything to one string!\n",
    "    processed_string = \" \".join(processed_list)\n",
    "\n",
    "    return processed_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "bSNpVEGJ2X7U"
   },
   "outputs": [],
   "source": [
    "df[\"clean_text\"] = df[\"review\"].apply(lambda x: clean_review(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MJjt1QTjHW_f"
   },
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review Length\n",
    "We think that a length of a review could provide crucial information when identifying sentiments as unhappy reviewers would generally have more to say; leaving more descriptive and lengthy reviews. Because of this, we believe that a review's length can provide important information about the nature of negative reviews.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review_length'] = df['review'].apply(len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UsKV51MrKYsS"
   },
   "source": [
    "## Frequency of Positive and Negative Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Efm528D4Hcm0"
   },
   "source": [
    "The first two feature we will extract are the frequency of positive and negative words in each review text. We found and downloaded a negative word list and a positive world list made especially for sentiment analysis and used them to extract these features. **(This is an optimized version since we have so many reviews)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "4J_g2tjVHal2"
   },
   "outputs": [],
   "source": [
    "with open('negative-words.txt', 'r') as file:\n",
    "    negative_words = file.read().splitlines()\n",
    "\n",
    "with open('positive-words.txt', 'r') as file:\n",
    "    positive_words = file.read().splitlines()\n",
    "\n",
    "def count_and_list_words(review, neg_words, pos_words):\n",
    "    review_words = set(review.split())\n",
    "    neg_list = list(review_words.intersection(neg_words))\n",
    "    pos_list = list(review_words.intersection(pos_words))\n",
    "    neg_count = len(neg_list)\n",
    "    pos_count = len(pos_list)\n",
    "    return neg_count, pos_count, neg_list, pos_list\n",
    "\n",
    "# Parallel processing\n",
    "results = Parallel(n_jobs=-1)(delayed(count_and_list_words)(review, negative_words, positive_words) for review in df['clean_text'])\n",
    "\n",
    "# Unpack results and assign to DataFrame\n",
    "df['negative_word_count'], df['positive_word_count'], df['negative_words'], df['positive_words'] = zip(*results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dNpBE9ruKf_W"
   },
   "source": [
    "## TF-IDF\n",
    "Next, we convert our pre-processed reviews into a sparse feature matrix, where each row corresponds to a review and each column to a possible word. We can use scikit-learn's `TfidfVectorizer` to do this quite easily. TF–IDF is a short for term frequency–inverse document frequency. It is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "mhS8do-MhPTy"
   },
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "# Fit and transform the text data\n",
    "tfidf_matrix = tfidf_vectorizer.fit_transform(df['clean_text'])\n",
    "svd = TruncatedSVD(n_components=100)  \n",
    "reduced_tfidf = svd.fit_transform(tfidf_matrix)\n",
    "\n",
    "# Add to DataFrame\n",
    "df['tfidf_scores'] = list(reduced_tfidf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exclamation and Question Mark Counts\n",
    "We think these features could be of importance as exclamation marks are often used to express strong feelings which might correlate with sentiment and question Marks can indicate confusion or questioning, potentially relevant for sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['exclamation_count'] = df['clean_text'].apply(lambda x: x.count('!'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['question_count'] = df['clean_text'].apply(lambda x: x.count('?'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VADER Score\n",
    "The VADER sentiment analysis tool is particularly suited for social media and short texts. It's good for capturing sentiment from common expressions and emotions. This tool gives a compound score, with 0 denoting neutrality, that illustrates the overall sentiment polarity of a text. The value ranges from -1 (most negative) to 1 (most positive).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyzer = SentimentIntensityAnalyzer()\n",
    "df['vader_score'] = df['clean_text'].apply(lambda x: analyzer.polarity_scores(x)['compound'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us take a look at our dataframe with all these new extracted features!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>review_length</th>\n",
       "      <th>negative_word_count</th>\n",
       "      <th>positive_word_count</th>\n",
       "      <th>negative_words</th>\n",
       "      <th>positive_words</th>\n",
       "      <th>tfidf_scores</th>\n",
       "      <th>exclamation_count</th>\n",
       "      <th>question_count</th>\n",
       "      <th>vader_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Story of a man who has unnatural feelings for ...</td>\n",
       "      <td>0</td>\n",
       "      <td>story man ha unnatural feeling pig start openi...</td>\n",
       "      <td>655</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>[absurd, crazy, unfortunately, violent, unnatu...</td>\n",
       "      <td>[easy, terrific, better, good, great]</td>\n",
       "      <td>[0.11066799673298097, -0.030326231038633644, -...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>As a serious horror fan, I get that certain ma...</td>\n",
       "      <td>0</td>\n",
       "      <td>serious horror fan get certain marketing ploy ...</td>\n",
       "      <td>4596</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>[doubt, warning, cry, weaker, zombie, funny, o...</td>\n",
       "      <td>[clearly, daring, interesting, decent, recomme...</td>\n",
       "      <td>[0.31251052811100305, -0.002922564095214745, 0...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.9966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>It's strange, while the film features full X-r...</td>\n",
       "      <td>0</td>\n",
       "      <td>strange film feature full xrated sex scene vio...</td>\n",
       "      <td>1040</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>[strange, shocking, disbelief, rape, shock, br...</td>\n",
       "      <td>[work, happy]</td>\n",
       "      <td>[0.12673067308474, -0.0551909896409926, -0.061...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.9906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'Bloody Birthday' is an odd and, at times, hum...</td>\n",
       "      <td>0</td>\n",
       "      <td>bloody birthday odd time humorous lowbudget ho...</td>\n",
       "      <td>1556</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>[killing, odd, problem, ridiculous, bloody, di...</td>\n",
       "      <td>[humorous, convincing, amusing, good, intellig...</td>\n",
       "      <td>[0.17652293065258173, -0.06782120887627999, -0...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.9845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I am a big fan of bad horrors, cheap horrors, ...</td>\n",
       "      <td>0</td>\n",
       "      <td>big fan bad horror cheap horror b movie bottom...</td>\n",
       "      <td>1637</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>[bad, infamous, cheap, silly, fat, mobster, po...</td>\n",
       "      <td>[fun, enjoy, enough, humor, best, ovation, wel...</td>\n",
       "      <td>[0.2136008465575531, 0.059293481673378445, -0....</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>Trailers of this movie may show scenes of viol...</td>\n",
       "      <td>1</td>\n",
       "      <td>trailer movie may show scene violence non main...</td>\n",
       "      <td>1495</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>[fear, oppression, brutally, disturbingly, fun...</td>\n",
       "      <td>[nice, like, tough, top, glitter, tougher]</td>\n",
       "      <td>[0.21493462601066632, 0.026570580012912157, -0...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.1306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>It's not a brilliant idea to watch Hundstage i...</td>\n",
       "      <td>1</td>\n",
       "      <td>brilliant idea watch hundstage youre 100 % sur...</td>\n",
       "      <td>1279</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>[violent, scratch, disturbing, tormented]</td>\n",
       "      <td>[brilliant, worth, maturity, like, ready, stab...</td>\n",
       "      <td>[0.2096851881529851, -0.12960590066739905, 0.0...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>\"Hundstage\" is seidl's first fiction film (bef...</td>\n",
       "      <td>1</td>\n",
       "      <td>`` hundstage '' seidl first fiction film ( dir...</td>\n",
       "      <td>691</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>[bad, lonely, excuse, fiction, disturbing, sho...</td>\n",
       "      <td>[rewarding, good, best, love, great, worked]</td>\n",
       "      <td>[0.16730510985046626, -0.10754119258937433, -0...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>Nick and Kelly are ready to be married but Tra...</td>\n",
       "      <td>1</td>\n",
       "      <td>nick kelly ready married travis ( kelly dog ) ...</td>\n",
       "      <td>620</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>[strange, smoke, dying]</td>\n",
       "      <td>[good, ready, fine, fun, love, lead, survival]</td>\n",
       "      <td>[0.08165972196197306, -0.04508487702856677, -0...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>I've seen this story before but my kids haven'...</td>\n",
       "      <td>1</td>\n",
       "      <td>ive seen story kid havent boy troubled past jo...</td>\n",
       "      <td>1235</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>[slow, neglected, wasted, troubled, intense, f...</td>\n",
       "      <td>[compliment, perfectly, pleasure, work, well, ...</td>\n",
       "      <td>[0.16360753706587267, 0.007697450661069064, -0...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review  sentiment  \\\n",
       "0      Story of a man who has unnatural feelings for ...          0   \n",
       "1      As a serious horror fan, I get that certain ma...          0   \n",
       "2      It's strange, while the film features full X-r...          0   \n",
       "3      'Bloody Birthday' is an odd and, at times, hum...          0   \n",
       "4      I am a big fan of bad horrors, cheap horrors, ...          0   \n",
       "...                                                  ...        ...   \n",
       "49995  Trailers of this movie may show scenes of viol...          1   \n",
       "49996  It's not a brilliant idea to watch Hundstage i...          1   \n",
       "49997  \"Hundstage\" is seidl's first fiction film (bef...          1   \n",
       "49998  Nick and Kelly are ready to be married but Tra...          1   \n",
       "49999  I've seen this story before but my kids haven'...          1   \n",
       "\n",
       "                                              clean_text  review_length  \\\n",
       "0      story man ha unnatural feeling pig start openi...            655   \n",
       "1      serious horror fan get certain marketing ploy ...           4596   \n",
       "2      strange film feature full xrated sex scene vio...           1040   \n",
       "3      bloody birthday odd time humorous lowbudget ho...           1556   \n",
       "4      big fan bad horror cheap horror b movie bottom...           1637   \n",
       "...                                                  ...            ...   \n",
       "49995  trailer movie may show scene violence non main...           1495   \n",
       "49996  brilliant idea watch hundstage youre 100 % sur...           1279   \n",
       "49997  `` hundstage '' seidl first fiction film ( dir...            691   \n",
       "49998  nick kelly ready married travis ( kelly dog ) ...            620   \n",
       "49999  ive seen story kid havent boy troubled past jo...           1235   \n",
       "\n",
       "       negative_word_count  positive_word_count  \\\n",
       "0                        7                    5   \n",
       "1                       36                   22   \n",
       "2                       11                    2   \n",
       "3                       14                    5   \n",
       "4                       11                   10   \n",
       "...                    ...                  ...   \n",
       "49995                   12                    6   \n",
       "49996                    4                   14   \n",
       "49997                    6                    6   \n",
       "49998                    3                    7   \n",
       "49999                   11                   10   \n",
       "\n",
       "                                          negative_words  \\\n",
       "0      [absurd, crazy, unfortunately, violent, unnatu...   \n",
       "1      [doubt, warning, cry, weaker, zombie, funny, o...   \n",
       "2      [strange, shocking, disbelief, rape, shock, br...   \n",
       "3      [killing, odd, problem, ridiculous, bloody, di...   \n",
       "4      [bad, infamous, cheap, silly, fat, mobster, po...   \n",
       "...                                                  ...   \n",
       "49995  [fear, oppression, brutally, disturbingly, fun...   \n",
       "49996          [violent, scratch, disturbing, tormented]   \n",
       "49997  [bad, lonely, excuse, fiction, disturbing, sho...   \n",
       "49998                            [strange, smoke, dying]   \n",
       "49999  [slow, neglected, wasted, troubled, intense, f...   \n",
       "\n",
       "                                          positive_words  \\\n",
       "0                  [easy, terrific, better, good, great]   \n",
       "1      [clearly, daring, interesting, decent, recomme...   \n",
       "2                                          [work, happy]   \n",
       "3      [humorous, convincing, amusing, good, intellig...   \n",
       "4      [fun, enjoy, enough, humor, best, ovation, wel...   \n",
       "...                                                  ...   \n",
       "49995         [nice, like, tough, top, glitter, tougher]   \n",
       "49996  [brilliant, worth, maturity, like, ready, stab...   \n",
       "49997       [rewarding, good, best, love, great, worked]   \n",
       "49998     [good, ready, fine, fun, love, lead, survival]   \n",
       "49999  [compliment, perfectly, pleasure, work, well, ...   \n",
       "\n",
       "                                            tfidf_scores  exclamation_count  \\\n",
       "0      [0.11066799673298097, -0.030326231038633644, -...                  0   \n",
       "1      [0.31251052811100305, -0.002922564095214745, 0...                  4   \n",
       "2      [0.12673067308474, -0.0551909896409926, -0.061...                  0   \n",
       "3      [0.17652293065258173, -0.06782120887627999, -0...                  1   \n",
       "4      [0.2136008465575531, 0.059293481673378445, -0....                  0   \n",
       "...                                                  ...                ...   \n",
       "49995  [0.21493462601066632, 0.026570580012912157, -0...                  0   \n",
       "49996  [0.2096851881529851, -0.12960590066739905, 0.0...                  0   \n",
       "49997  [0.16730510985046626, -0.10754119258937433, -0...                  0   \n",
       "49998  [0.08165972196197306, -0.04508487702856677, -0...                  0   \n",
       "49999  [0.16360753706587267, 0.007697450661069064, -0...                  0   \n",
       "\n",
       "       question_count  vader_score  \n",
       "0                   0       0.8720  \n",
       "1                   4      -0.9966  \n",
       "2                   0      -0.9906  \n",
       "3                   0      -0.9845  \n",
       "4                   0       0.8932  \n",
       "...               ...          ...  \n",
       "49995               0      -0.1306  \n",
       "49996               0       0.9778  \n",
       "49997               0       0.8910  \n",
       "49998               0       0.9779  \n",
       "49999               0       0.9920  \n",
       "\n",
       "[50000 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6XsSk9XQM6wV"
   },
   "source": [
    "# Data Visualization\n",
    "### Now that we have more features to analyze and utilize, let's explore and visualize! (Rhymed so perfectly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Distribution\n",
    "As we stated before, our dataset has an equal amount of negative and positive reviews (25000 each)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sentiment', data=df)\n",
    "plt.title('Distribution of Sentiments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review vs Review Lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='sentiment', y='review_length', data=df)\n",
    "plt.title('Review length by Sentiment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that our earlier prediction does not hold as the review length medians are very close to each other. If anything, postive reviews tend to be very slightly higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Counts vs Sentiments\n",
    "Now let's check if extracting the negative and positive words for each review was worth our while by looking at the distribution of word counts of both types across both type of reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(x='sentiment', y='negative_word_count', data=df)\n",
    "plt.title('Negative Word Count by Sentiment')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(x='sentiment', y='positive_word_count', data=df)\n",
    "plt.title('Positive Word Count by Sentiment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " As we can see from the above boxplots, negative reviews (sentiment 0) have higher counts of negative words, and positive reviews (sentiment 1) have higher counts of positive words. This pattern is expected and suggests that the extraction of negative and positive words from each review can be a valuable feature for sentiment analysis!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VADER Score vs Sentiment\n",
    "Now let's take a look at the distribution of VADER scores across both negative and positive reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vader score by sentiment\n",
    "sns.boxplot(x='sentiment', y='vader_score', data=df)\n",
    "plt.title('VADER Score by Sentiment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The VADER sentiment analysis is hitting the mark, assigning lower median scores to reviews with a negative sentiment (0) and higher ones to positive sentiment (1) reviews. It’s mostly accurate, although the spread of scores, particularly for positive sentiments, includes a few surprises dipping into negative territory, hinting at VADER's nuanced understanding of the reviews' emotional spectrum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TxPEDKfhzmMQ"
   },
   "source": [
    "## Word Clouds\n",
    "The below word clouds show us the most frequent positive words and the most frequent negative words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "TJ1omNg3ywIc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "negative_reviews = df[df['sentiment'] == 0]\n",
    "positive_reviews = df[df['sentiment'] == 1]\n",
    "\n",
    "all_negative_words = ' '.join([' '.join(words) for words in negative_reviews['negative_words']])\n",
    "all_positive_words = ' '.join([' '.join(words) for words in positive_reviews['positive_words']])\n",
    "\n",
    "# Generate word clouds\n",
    "wordcloud_neg = WordCloud(background_color='white').generate(all_negative_words)\n",
    "wordcloud_pos = WordCloud(background_color='white').generate(all_positive_words)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Negative Words Wordcloud\n",
    "plt.subplot(1, 2, 1) \n",
    "plt.imshow(wordcloud_neg, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.title('Negative Words Word Cloud')\n",
    "\n",
    "# Positive Words Wordcloud\n",
    "plt.subplot(1, 2, 2) \n",
    "plt.imshow(wordcloud_pos, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.title('Positive Words Word Cloud')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-F40L75m18mB"
   },
   "source": [
    "# Modeling and Error Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step in training our model is to separate our dataset into two groups: a training set, which contains 80% of the data, and a testing set, which contains 20% of the data. This allows us to assess how effectively our sentiment analysis models work with unseen data. We will use the pre-built `train_test_split` function from sci-kit learn to do this.\n",
    "\n",
    "Each element of our 'tfidf_scores' column of our dataframe is a list of numerical values, which is a data format not supported by machine learning models of the sci-kit learn library. So, we first converted each list in this column to a numpy array. Then, we create a numpy array of these arrays. This is shown below. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert td-idf scores to an array of arrays.\n",
    "df['tfidf_scores'] = df['tfidf_scores'].apply(np.array)\n",
    "tfidf_array = np.vstack(df['tfidf_scores'].values)\n",
    "\n",
    "# Selecting features that we use for our model (by dropping the unwanted ones) from the dataframe and combining them with this td-idf array.\n",
    "x = df.drop(['review', 'clean_text','tfidf_scores', 'sentiment', 'negative_words', 'positive_words'], axis=1)\n",
    "x = np.hstack((x, tfidf_array)) \n",
    "\n",
    "# Our outcome variable: the labels corresponding to the sentiment of each review.\n",
    "y = df['sentiment']\n",
    "\n",
    "# Allocate 20% of our dataset to the testing subset.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For modeling we will use SVMs and Logistic Regression. Although different models, Support Vector Machine (SVM) with a linear kernel and Logistic Regression, are often used in similar contexts due to their effectiveness in binary classification problems.\n",
    "\n",
    "## SVM\n",
    "Using the SVC class from the scikit-learn package, we first initialize a Support Vector Machine (SVM) classifier. Using labeled training data, the SVM algorithm seeks to determine the ideal hyperplane for classifying unseen data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model for our selected features using a linear kernel\n",
    "svm_model = SVC(kernel='linear')  \n",
    "svm_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training our model, we now evaluate its accuracy. To do this, we use the pre-built performance metrics from the sklearn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7327\n",
      "Precision: 0.7331519979470709\n",
      "Recall: 0.7325967030721715\n",
      "F1 Score: 0.7325093085609802\n",
      "Confusion Matrix:\n",
      " [[3797 1225]\n",
      " [1448 3530]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_svm = svm_model.predict(x_test)\n",
    "\n",
    "# Evaluate model performance\n",
    "accuracy = accuracy_score(y_test, y_pred_svm)\n",
    "precision = precision_score(y_test, y_pred_svm, average='macro')\n",
    "recall = recall_score(y_test, y_pred_svm, average='macro')\n",
    "f1 = f1_score(y_test, y_pred_svm, average='macro')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.72      0.76      0.74      5022\n",
      "    positive       0.74      0.71      0.73      4978\n",
      "\n",
      "    accuracy                           0.73     10000\n",
      "   macro avg       0.73      0.73      0.73     10000\n",
      "weighted avg       0.73      0.73      0.73     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_tfidf_report=classification_report(y_test,y_pred_svm,target_names=['negative','positive'])\n",
    "print(svm_tfidf_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score, as indicated by the results above, is 73.27%. With a precision score of 0.7332, approximately 73.32% of the reviews that were predicted to be positive are actually positive. A recall of 0.7326 indicates that around 73.26% of real positive reviews are captured by the model. The model exhibits a harmonious performance in terms of both accuracy and recall, indicated by the F1 Score of 0.7325.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1, max_iter=1000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, max_iter=1000, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=1, max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the logistic regression model\n",
    "lr = LogisticRegression(penalty='l2', max_iter=1000, C=1, random_state=42) \n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training our logistic regression model, we now evaluate its accuracy. Again, we use the pre-built performance metrics from the sklearn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8506\n",
      "Precision: 0.8359043578866178\n",
      "Recall: 0.8708316593009241\n",
      "F1 Score: 0.8530106257378984\n",
      "Confusion Matrix:\n",
      " [[4171  851]\n",
      " [ 643 4335]]\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the testing set\n",
    "y_pred_lr = lr.predict(x_test)\n",
    "\n",
    "# Evaluate performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred_lr)\n",
    "precision = precision_score(y_test, y_pred_lr)\n",
    "recall = recall_score(y_test, y_pred_lr)\n",
    "f1 = f1_score(y_test, y_pred_lr)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_lr)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.87      0.83      0.85      5022\n",
      "    positive       0.84      0.87      0.85      4978\n",
      "\n",
      "    accuracy                           0.85     10000\n",
      "   macro avg       0.85      0.85      0.85     10000\n",
      "weighted avg       0.85      0.85      0.85     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr_tfidf_report=classification_report(y_test,y_pred_lr,target_names=['negative','positive'])\n",
    "print(lr_tfidf_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score, as indicated by the results above, is 85.06%. With a precision score of 0.8359, approximately 83.59% of the reviews that were predicted to be positive are actually positive. A recall of 0.8708 indicates that around 87.08% of real positive reviews are captured by the model. The model exhibits a harmonious performance in terms of both accuracy and recall, indicated by the F1 Score of 0.853.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refining Our Model\n",
    "\n",
    "In our continued pursuit of model refinement, we revisited the initial inclusion of the \"Review Length\" feature, a variable we initially considered crucial in discerning sentiments. Our preliminary rationale rested on the assumption that dissatisfied reviewers tend to express their sentiments in more extensive and detailed reviews.\n",
    "\n",
    "However, a closer examination through exploratory data analysis (EDA) revealed a nuanced picture. Contrary to our initial hypothesis, the medians of review lengths across different sentiment categories were remarkably close. If anything, positive reviews exhibited only a marginal increase in length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We drop our review length column\n",
    "x = df.drop(['review', 'clean_text','tfidf_scores', 'sentiment', 'negative_words', 'positive_words', 'review_length'], axis=1)\n",
    "x = np.hstack((x, tfidf_array)) \n",
    "\n",
    "# Our outcome variable: the labels corresponding to the sentiment of each review.\n",
    "y = df['sentiment']\n",
    "\n",
    "# Allocate 20% of our dataset to the testing subset.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model for our new selected features using a linear kernel\n",
    "svm_model = SVC(kernel='linear')  \n",
    "svm_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we evaluate the performance of our new SVM model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8634\n",
      "Precision: 0.863631735859785\n",
      "Recall: 0.8634563165142877\n",
      "F1 Score: 0.8633889345036949\n",
      "Confusion Matrix:\n",
      " [[4272  750]\n",
      " [ 616 4362]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_svm = svm_model.predict(x_test)\n",
    "\n",
    "# Evaluate model performance\n",
    "accuracy = accuracy_score(y_test, y_pred_svm)\n",
    "precision = precision_score(y_test, y_pred_svm, average='macro')\n",
    "recall = recall_score(y_test, y_pred_svm, average='macro')\n",
    "f1 = f1_score(y_test, y_pred_svm, average='macro')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# Print the metrics\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score, as indicated by the results above, is 86.34%. With a precision score of 0.8636, approximately 86.36% of the reviews that were predicted to be positive are actually positive. A recall of 0.8634 indicates that around 86.34% of real positive reviews are captured by the model. The model exhibits a harmonious performance in terms of both accuracy and recall, indicated by the F1 Score of 0.8633. All of these performance scores are better than those of our initial SVM model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression(C=1, max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the logistic regression model for our new selected features\n",
    "lr = LogisticRegression(penalty='l2', max_iter=1000, C=1, random_state=42) \n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8611\n",
      "Precision: 0.8486496988537012\n",
      "Recall: 0.8774608276416231\n",
      "F1 Score: 0.8628148148148148\n",
      "Confusion Matrix:\n",
      " [[4243  779]\n",
      " [ 610 4368]]\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the testing set\n",
    "y_pred_lr = lr.predict(x_test)\n",
    "\n",
    "# Evaluate performance metrics\n",
    "accuracy = accuracy_score(y_test, y_pred_lr)\n",
    "precision = precision_score(y_test, y_pred_lr)\n",
    "recall = recall_score(y_test, y_pred_lr)\n",
    "f1 = f1_score(y_test, y_pred_lr)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_lr)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.87      0.84      0.86      5022\n",
      "    positive       0.85      0.88      0.86      4978\n",
      "\n",
      "    accuracy                           0.86     10000\n",
      "   macro avg       0.86      0.86      0.86     10000\n",
      "weighted avg       0.86      0.86      0.86     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr_tfidf_report=classification_report(y_test,y_pred_lr,target_names=['negative','positive'])\n",
    "print(lr_tfidf_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score, as indicated by the results above, is 86.11%. With a precision score of 0.8486, approximately 84.86% of the reviews that were predicted to be positive are actually positive. A recall of 0.8775 indicates that around 86.34% of real positive reviews are captured by the model. The model exhibits a harmonious performance in terms of both accuracy and recall, indicated by the F1 Score of 0.8628. All of these performance scores are better than those of our initial logistic regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### When we look at the overall performance metrics of our new models that exclude \"Review Length\", there are notable improvements. Interestingly, all of the performance scores are higher compared to both to our initial logistic regression and SVM models, emphasizing the strategic choice we made to remove the \"Review Length\" feature.  Thus, leaving this feature out has made our sentiment analysis model more robust and accurate.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error Analysis\n",
    "We will now conduct an error analysis on our improved model, where we omitted the \"Review Length\" feature  to help us identify how our model might need to be improved. We do this by looking at the characteristics of reviews that our model has incorrectly classified.\n",
    "\n",
    "### SVM\n",
    "**False Positive Rate**: 0.1470. This indicates that around 14.70% of the actual negative cases were incorrectly predicted as positive by the model.\n",
    "\n",
    "**False Negative Rate**: 0.1179. This suggests that approximately 11.79% of the actual positive cases were incorrectly predicted as negative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conduct this error analysis, we will analyze a false positive review to understand why the model might have misclassified them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 1, Actual: 0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Review:\n",
      "This looks so good on paper - Matt Damon, Lawrence Fishbourne, Jean Reno, nice right? And a heist with $42 million - sounds like a kick-ass crime movie.<br /><br />Big disappointment - I reckon the stars got all the money because the production values on this are lousy.<br /><br />But more than that it the pseudo reservoir Dogs atmosphere when the easy crime goes wrong. It's very much made for TV stuff.<br /><br />All in all hugely disappointing - it score points for being what it is - but loses them massively for being, bluntly, not very interesting at all...\n",
      "----------------------------------------------------------------------------------------------------\n",
      "List of negative words: ['lousy', 'crime', 'disappointing', 'loses', 'disappointment', 'wrong']\n",
      "Count of negative words: 6\n",
      "----------------------------------------------------------------------------------------------------\n",
      "List of positive words: ['like', 'nice', 'easy', 'good', 'right', 'interesting']\n",
      "Count of positive words: 6\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Count of question marks: 1\n",
      "Count of exclamation marks: 0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Vader score: -0.8173\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "y_test_array = y_test.values\n",
    "\n",
    "false_positives_indices = np.where((y_pred_svm == 1) & (y_test_array == 0))[0]\n",
    "\n",
    "if len(false_positives_indices) == 0:\n",
    "    print(\"No false positives found.\")\n",
    "else:\n",
    "    for idx in false_positives_indices[112:113]:\n",
    "        print(f\"Predicted: {y_pred_svm[idx]}, Actual: {y_test_array[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(\"Review:\")\n",
    "        print(df['review'].values[idx])\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"List of negative words: {df['negative_words'].values[idx]}\")\n",
    "        print(f\"Count of negative words: {df['negative_word_count'].values[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"List of positive words: {df['positive_words'].values[idx]}\")\n",
    "        print(f\"Count of positive words: {df['positive_word_count'].values[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"Count of question marks: {df['question_count'].values[idx]}\")\n",
    "        print(f\"Count of exclamation marks: {df['exclamation_count'].values[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"Vader score: {df['vader_score'].values[idx]}\")\n",
    "        print(\"-\" * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the above review, it clearly is a negative review. We could conclude this from phrases such as: \"I reckon the stars got all the money because the production values on this are lousy.\" or \"All in all hugely disappointing..\". However, our model classified this review as a positive one. A reason for this false positive might be that the total count of positive words is equal to the count of negative words. Thus, if the model places a great importance on these features, the overall sentiment may be unclear to the model as these counts are equal. Also, the model could have misinterpreted seemingly positive phrases like \"sounds like a kick-ass crime movie\", which have a negative connotation when the whole sentence or text is taken into context. Furthermore, the model probably doesn't give much weight to the Vader score, as it is extremely low and is nearing -1, which suggests the review as a negative one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "**False Positive Rate**: 0.1577. This means that about 15.77% of the actual negative cases were incorrectly predicted as positive by the model.\n",
    "\n",
    "**False Negative Rate**: 0.1193. indicating that around 11.93% of the actual positive cases were incorrectly predicted as negative.\n",
    "\n",
    "To conduct this error analysis, we will analyze a false positive review to understand why the model might have misclassified them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 1, Actual: 0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Review:\n",
      "Unbelievable. I never saw something like that. Everything is bad; really bad. From photography (lots of scenes without focus!) to the acting (the young female is terrible). And what can we say about those helicopters made in Paint Brush...? Really amazing B, I mean, Z film.<br /><br />The plot are bad, cliché and bad wrote. Basics conveniences to the screenplay seems to work. I can't even think a young student of cinema making this movie. Nothing justify it.<br /><br />I recommend that you don't even think to see this movie. Sleep or play solitary are best choices. ;)<br /><br />xxx\n",
      "----------------------------------------------------------------------------------------------------\n",
      "List of negative words: ['bad', 'unbelievable', 'plot', 'terrible']\n",
      "Count of negative words: 4\n",
      "----------------------------------------------------------------------------------------------------\n",
      "List of positive words: ['work', 'recommend', 'convenience', 'amazing', 'like', 'best']\n",
      "Count of positive words: 6\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Count of question marks: 1\n",
      "Count of exclamation marks: 1\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Vader score: -0.856\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "false_positives_indices = np.where((y_pred_lr == 1) & (y_test_array == 0))[0]\n",
    "\n",
    "if len(false_positives_indices) == 0:\n",
    "    print(\"No false positives found.\")\n",
    "else:\n",
    "    for idx in false_positives_indices[3:4]:\n",
    "        print(f\"Predicted: {y_pred_svm[idx]}, Actual: {y_test_array[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(\"Review:\")\n",
    "        print(df['review'].values[idx])\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"List of negative words: {df['negative_words'].values[idx]}\")\n",
    "        print(f\"Count of negative words: {df['negative_word_count'].values[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"List of positive words: {df['positive_words'].values[idx]}\")\n",
    "        print(f\"Count of positive words: {df['positive_word_count'].values[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"Count of question marks: {df['question_count'].values[idx]}\")\n",
    "        print(f\"Count of exclamation marks: {df['exclamation_count'].values[idx]}\")\n",
    "        print(\"-\" * 100)\n",
    "        print(f\"Vader score: {df['vader_score'].values[idx]}\")\n",
    "        print(\"-\" * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the above review, it clearly is a negative review. We could deduct this from \"Everything is bad; really bad.\" or \"I recommend that you don't even think to see this movie.\". However, our model labelled this as a positive review. A reason for this false positive might be that the list of positive words outnumber the negative words. Thus, the overall sentiment that is conveyed by the contextual use of these words may be overlooked if the model places a great deal of emphasis on word counts. Also, the model might have found it difficult to detect sarcastic tone and end up misinterpreting the user's tone. Furthermore, the model probably doesn't give much weight to the Vader score, as it is extremely low, nearing -1 which should've flagged it as a negative review."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZyhsTH5L2DaZ"
   },
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nTPMDd7i2HSn"
   },
   "source": [
    "By effectively utilizing supervised machine learning algorithms and NLP techniques we have achieved our project's objectives. We were able to make a robust predictive model by going through stages like feature extraction, data visualization, and feature selection. Thus our objectives and motivations are met as our model provides a strong basis for understanding the sentiments underlying user-generated IMDb movie reviews. By doing error analysis, we are able to further identify several areas in which our model is lacking, allowing us to precisely target areas for improvement. For example, conducting a feature significance analysis, employing ensemble techniques, and utilizing more sophisticated NLP algorithms to accurately capture the complex emotions underlying these assessments would further improve the accuracy of our model and understand the nuanced sentiments behind IMDb reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EYrjtidBEBAh"
   },
   "source": [
    "# References\n",
    "* Minqing Hu and Bing Liu. \"Mining and Summarizing Customer Reviews.\"\n",
    "Proceedings of the ACM SIGKDD International Conference on Knowledge\n",
    "Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle,\n",
    "Washington, USA,\n",
    "* Bing Liu, Minqing Hu and Junsheng Cheng. \"Opinion Observer: Analyzing\n",
    "and Comparing Opinions on the Web.\" Proceedings of the 14th\n",
    "International World Wide Web conference (WWW-2005), May 10-14, 2005, Chiba, Japan."
   ]
  }
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