set_up_notebook.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## High-level overview\n",
    "\n",
    "We can all agree researching products, platforms or services is painful and time taking.\n",
    "\n",
    "I often find myself researching multiple sources (R.I.P chrome tabs) to see if something is reliable, worthy, and reasonable to buy and still feel like I am lost. Information is scattered, hard to find, and hard to sort through (multiple mentions of different products, mixed reviews) and get a clear summary.\n",
    "\n",
    "Moreover, Misleading ads, SEO spam (best 5 product articles with the least research), and fake reviews (Affiliated links, sponsored content) can make the whole experience frustrating.\n",
    "\n",
    "Reddit is one place where people leave honest reviews and discourage any fake ones. With over 52 million daily active users, Reddit provides a platform for people from all walks of life to share their experiences and opinions.\n",
    "\n",
    "### The goal of this project is to help users find:\n",
    "1) if a product or service is worth it via sentiment analysis. This helps with queries such as \"Is regal unlimited subscription worth it?\"\n",
    "2) product mentions amongst posts and comments via named entity recognition (NER). This helps with queries such as \"Best 4K TV to buy\".\n",
    "\n",
    "Please see the explore_data_and_model.ipynb that addresses second problem - Identifying products with Named Entity Recognition (NER/Token Classification)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook focuses on solving the first problem - Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\avanjavakam\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# For gathering data from reddit\n",
    "import praw # Python Reddit API Wrapper\n",
    "from praw.models import MoreComments\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
    "from nltk import FreqDist\n",
    "nltk.downloader.download('vader_lexicon')\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA\n",
    "import re # Remove links\n",
    "import en_core_web_sm\n",
    "import spacy\n",
    "\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# for visualization\n",
    "import seaborn as sns\n",
    "sns.set(style = 'whitegrid', palette = 'Dark2')\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud, get_single_color_func\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Reddit Comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<praw.reddit.Reddit at 0x1f999e7a460>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading development settings for Reddit API\n",
    "%load_ext dotenv\n",
    "%dotenv\n",
    "\n",
    "# Create a reddit connection with reddit API details\n",
    "reddit = praw.Reddit(client_id = os.getenv(\"CLIENT_ID\"),\n",
    "                     client_secret = os.getenv(\"CLIENT_SECRET\"),\n",
    "                     user_agent = os.getenv(\"USER_AGENT\"))\n",
    "reddit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.77\n",
      "13715\n",
      "Hi, I’m Anthony Palma, founder of Jump, the “Netflix of Indie Games” service that launched on Tuesday. AMA!\n",
      "Submission ID =  71iy3w \n",
      "\n",
      "0.95\n",
      "13516\n",
      "Regal Cinemas Unlimited Ticket Subscription Program Set To Launch This Month\n",
      "Submission ID =  c80yte \n",
      "\n",
      "0.93\n",
      "12390\n",
      "If you use Alexa, make sure \"she\" didn't subscribe you to AmazonMusic Unlimited without your knowledge!\n",
      "Submission ID =  c3bx74 \n",
      "\n",
      "0.98\n",
      "11887\n",
      "YSK many Companies Are Offering Free Services And/Or Waiving Fees Due to the quarantine. Here’s a Full List\n",
      "Submission ID =  fpdxob \n",
      "\n",
      "0.98\n",
      "8653\n",
      "[Home Crafting] When a company tried to make a bunch of stay at home moms pay rent to use a machine they already own during a global pandemic\n",
      "Submission ID =  mmmcy7 \n",
      "\n",
      "0.9\n",
      "8094\n",
      "I saved 88% on coffee insurance by switching to Panera (from Starbucks)\n",
      "Submission ID =  ildmel \n",
      "\n",
      "0.99\n",
      "8032\n",
      "[Web Media] Critical Role and Orion Acaba: How to get kicked out of what is now Twitch's most successful channel so hard, people don't even know you were part of the show\n",
      "Submission ID =  q5c2xf \n",
      "\n",
      "0.88\n",
      "7974\n",
      "I Dared My Best Friend to Ruin My Life - He's Succeeding [Part 7]\n",
      "Submission ID =  4qzu9h \n",
      "\n",
      "0.98\n",
      "6705\n",
      "AITAH for suggesting to my wife that we kick out our daughter and son-in-law?\n",
      "Submission ID =  12yeuzo \n",
      "\n",
      "0.92\n",
      "5438\n",
      "Stealing my gf's wifi strained (or ended?) their relationship...\n",
      "Submission ID =  sgf497 \n",
      "\n",
      "0.95\n",
      "5152\n",
      "Vote with your dollars.\n",
      "Submission ID =  1099h4y \n",
      "\n",
      "1.0\n",
      "4988\n",
      "Master List: I Compiled and Ranked Every Major Studying Resource for A+, Network+, and Security+ Here!\n",
      "Submission ID =  i7hx4t \n",
      "\n",
      "0.96\n",
      "4545\n",
      "I put out a Craigslist ad for a new roommate to ward off my stalker and avoid violating my lease. My new roommate might be a demon. (Part 2)\n",
      "Submission ID =  a01n6u \n",
      "\n",
      "0.99\n",
      "4268\n",
      "8Gbps internet in Canada. Wtf...\n",
      "Submission ID =  xsccsy \n",
      "\n",
      "0.92\n",
      "3585\n",
      "GoPro could be a turnaround company $GPRO (value $15.95 vs price $8.78)\n",
      "Submission ID =  sm0vql \n",
      "\n",
      "0.97\n",
      "3468\n",
      "New TV service subscription is to my liking so I want to refund.\n",
      "Submission ID =  p140g4 \n",
      "\n",
      "0.93\n",
      "3258\n",
      "They know. Just adding to the voice. Weekly player/DM since 1994. Honestly not sure if I will ever go back even if they fix this mess. Will you guys?\n",
      "Submission ID =  10ckdts \n",
      "\n",
      "0.99\n",
      "3194\n",
      "I’ve compiled all the low-cost medical resources everyone shared in the comments of my last post. Hope it’s useful.\n",
      "Submission ID =  elryxp \n",
      "\n",
      "0.95\n",
      "3157\n",
      "Thanks to reddit, my app, Weather on the Way has been downloaded over 30k times. Today I’m launching a first big update!\n",
      "Submission ID =  lzoci4 \n",
      "\n",
      "0.87\n",
      "2741\n",
      "Amazon launches Its book Subscription Service, Kindle Unlimited in Australia.\n",
      "Submission ID =  5cjsva \n",
      "\n"
     ]
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       "['71iy3w',\n",
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       " '5cjsva']"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subreddit = reddit.subreddit('all')\n",
    "submissions_list = []\n",
    "search_term = \"regal unlimited subscription\"\n",
    "# search popular subreddits for reviews and get first 3 posts\n",
    "for submission in subreddit.search(search_term,limit = 20,sort='top'):\n",
    "\n",
    "    #print(submission.likes)\n",
    "    #print(submission.num_comments)\n",
    "    #print(submission.subreddit_subscribers)\n",
    "    #print(submission.view_count)\n",
    "    print(submission.upvote_ratio)\n",
    "    print(submission.score)    \n",
    "    print(submission.title)\n",
    "       \n",
    "    print('Submission ID = ', submission.id, '\\n')\n",
    "    submissions_list.append(submission.id)\n",
    "submissions_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "subreddit = reddit.subreddit('all')\n",
    "submissions_list = []\n",
    "search_term = \"best chairs\"\n",
    "\n",
    "#top = subreddit.search(search_term,limit = 20,sort='top').top(params={'t': 'all'}, limit=25) # For a more potentially accurate set of top comments, increase the limit (but it'll take longer)\n",
    "posts = dict()\n",
    "all_comments = []\n",
    "comments_body = []\n",
    "top_comments_body = {}\n",
    "    \n",
    "# search all subreddits and get comments from top posts\n",
    "for submission in subreddit.search(search_term, limit = 1, sort='relevance'):\n",
    "    # sort comments by 'best' (which is default mode we see when using reddit)\n",
    "    submission.comment_sort = 'best'\n",
    "\n",
    "    # This line is required in case we need commentForest (includes replies tree)\n",
    "    # or else there would be an error if we do not replace moreComments.\n",
    "    submission.comments.replace_more(limit = None)\n",
    "\n",
    "    # exlclude bot posts/advertised posts \n",
    "    if not submission.stickied:\n",
    "\n",
    "        # add original post text to the posts dictionary\n",
    "        posts[submission.selftext] = submission.score\n",
    "\n",
    "        # for each post, get top level comments (no replies, no commentForest)\n",
    "        #if top_level_comments_only:\n",
    "        real_comments = [comment for comment in list(submission.comments)]\n",
    "        # for comments and replies (includes commentForest)\n",
    "        #else:                \n",
    "            #real_comments = [comment for comment in submission.comments.list()]            \n",
    "        all_comments += real_comments\n",
    "# if the search term found any posts on reddit search\n",
    "if posts:\n",
    "    # sort all comments by no. of upvotes\n",
    "    all_comments.sort(key=lambda comment: comment.score, reverse=True)\n",
    "\n",
    "    # store top n comments with most no. of upvotes\n",
    "    top_comments = all_comments[:5] #top 5 comments\n",
    "\n",
    "    for comment in top_comments:\n",
    "        top_comments_body[comment.body] = comment.score\n",
    "\n",
    "    for comment in all_comments:\n",
    "        comments_body.append(comment.body)\n",
    "\n",
    "    for text, score in posts.items():    \n",
    "        # add post texts to comments body\n",
    "        comments_body.append(text)\n",
    "else:\n",
    "    print('No posts found! Please enter another search term')\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_comments(search_term, no_of_posts=10, no_of_comments=20, no_of_top_comments=5, include_replies=False):\n",
    "    \"\"\"function to get comments from reddit posts.\n",
    "\n",
    "    Args:\n",
    "        search_term (str): strings to search on reddit\n",
    "        no_of_posts (int, optional): number of posts to fetch. Defaults to 10.\n",
    "        no_of_comments (int, optional): number of comments to fetch. Defaults to 20.\n",
    "        no_of_top_comments (int, optional): number of comments with most upvotes to fetch. Defaults to 5.\n",
    "        include_replies (bool, optional): set True to include replies. Defaults to False.\n",
    "\n",
    "    Returns:\n",
    "        comments_body: list of all comments (including text in posts)\n",
    "        top_comments_body: a dict of of top comments (sorted by upvotes) and number of upvotes\n",
    "        len(posts): number of posts fetched\n",
    "        len(all_comments): number of comments fetched (excluding post texts)\n",
    "    \"\"\"\n",
    "    posts = {}\n",
    "    all_comments = []\n",
    "    top_comments_body = {}\n",
    "\n",
    "    # Search all subreddits and get comments from top posts\n",
    "    for submission in subreddit.search(search_term, limit=no_of_posts, sort='relevance'):\n",
    "        # Get comments from each submission\n",
    "        submission.comments.replace_more(limit=None)\n",
    "\n",
    "        if include_replies:\n",
    "            comments = submission.comments.list()\n",
    "        else:\n",
    "            comments = submission.comments\n",
    "\n",
    "        # Exclude stickied posts\n",
    "        if not submission.stickied:\n",
    "            posts[submission.id] = submission.selftext\n",
    "            all_comments += comments\n",
    "\n",
    "        # If the number of comments exceeds user input, stop searching for more posts\n",
    "        if len(all_comments) >= no_of_comments:\n",
    "            break\n",
    "\n",
    "    # Sort all comments by number of upvotes\n",
    "    all_comments.sort(key=lambda comment: comment.score, reverse=True)\n",
    "\n",
    "    # Store top n comments with most upvotes\n",
    "    top_comments = all_comments[:no_of_top_comments]\n",
    "\n",
    "    # Store top comments body\n",
    "    for comment in top_comments:\n",
    "        top_comments_body[comment.body] = comment.score\n",
    "\n",
    "    # Store all comments body\n",
    "    comments_body = [comment.body for comment in all_comments]\n",
    "\n",
    "    # Add original post text to comments body\n",
    "    for text in posts.values():\n",
    "        comments_body.append(text)\n",
    "\n",
    "    return comments_body, top_comments_body, len(posts), len(all_comments)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['automatically', 'go', 'month', 'month', 'end', '3', 'month', 'signup', 'fan', 'run', 'sub', 'don', 't', 'work', 'regal', '100', 'want', 'direct', 'complaint', 'cancellation', 'request', 'c', 'fastest', 'way', 'facebook', 'messenger', 'twitter', 'typically', '24', 'hour', 'cancelled', 'agree', 'suck', 'wish', 'cancel', 'button', 'don', 't', 'repeatedly', 'address', 'best', 'luck', 'd', 'appreciate', 'directing', 'legal', 'question', 'correct', 'sub', 'like', 'r', 'legaladvice', 'maybe', 'best', 'use', 'everyday', 'week', 'week', 'saw', 'blow', 'pipeline', 'suzume', 'renfield', 'john', 'wick', 'chapter', '4', 'beau', 'afraid', 'm', 'seeing', 'return', 'jedi', 'tomorrow', 'account', 'tied', 'single', 'device', 's', 'kind', 'hard', 'gift', 'surprise', 'd', 'steal', 'phone', 'steal', 'phone', 'set', 'wouldn', 't', 'need', 'id', 'picture', 'pick', 'photo', 'phone', 's', 'storage', 'best', 'way', 'gift', 'unlimited', 'gift', 'card', 'walk', 'setting', 'gift', 'card', 'payment', 'regular', 'gift', 'card', 'way', 'doesn', 't', 'need', 'unlimited', 'gift', 'card', 'offer', 'want', 'sent', '$50', 'e', 'gift', 'card', '90', 'day', 'promo', 'gift', 'told', 'sign', '90', 'day', 'promo', 'pay', '3', 'month', 'month', 'free', 'pay', '2', 'month', 'gc', 'fully', 'cover', 'told', 'need', 'credit', 'card', 'file', 'sign', 'use', 'gift', 'card', 'pay', 'subscription', 'sign', 'll', 'let', 'know', 'go', 'received', 'gift', 'card', 'plan', 'sign', 'later', 'today', 'nice', 'perk', '$10', 'concession', 'gc', 'sent', 'use', '1', '1', '22', 'p', 'heck', 'time', 'completing', 'gift', 'card', 'order', '1st', 'attempt', 'said', 'order', 'declined', 'ridiculous', 'online', 'bank', 'statement', 'charge', 'gone', 'immediately', 'credited', 'regal', 'charged', 'thing', 'second', 'attempt', 'attempt', 'decided', 'yes', 'receiving', 'promotional', 'info', 'went', 'sure', 'difference', 'thing', 'correct', 'change', 'previous', '2', 'attempt', 'frustrating', 'thought', 'd', 'warn', 'case', 'experience', 'issue', 'regal', 'unlimited', 'amc', 'list', 'great', 'depends', 'theater', 'prefer', 'regal', 'better', 'seating', 'amc', 'nice', 'dolby', 'imax', 'theater', 'stated', 'll', 'turn', 'month', 'month', 'note', 's', 'month', 'feel', 'like', 'going', 'movie', 'll', 'month', 'month', 'cancel', 'subscription', 'movie', 'd', 'drive', '45min', 'slow', 'year', 'd', 'fully', 'cancel', 'signup', 'minimum', '3months', 'later', 's', 'awesome', 'live', 'close', 'regal', 'theatre', 'time', 'shill', 'generally', 'money', 'worth', 'film', 'month', 'like', 'thread', 'hit', 'theater', 'week', 'seek', 'certain', 'imax', 'showing', 's', '$6', 'surcharge', 'great', 'service', 'amc', 'list', 'service', 'good', 'premium', 'pricing', 'regal', 'hasn', 't', 'adopted', 'practice', 'mean', 'yea', 's', 'good', 'deal', '$0.53', 'convenience', 'fee', 'time', 'use', 'annoying', 'af', 'black', 'friday', 'deal', 'unlimited', 'know', 'want', 'option', 'regal', 'unlimited', 'egift', 'card', 'reason', 'buying', 'printing', 'wouldn', 't', 'work', 'amc', 'list', '$21.99', 'drive', 'columbia', 'deal', 'guy', 'regal', 's', 'subscription', 'service', 'thinking', 'getting', 'look', 'like', 'great', 'deal', 'regular', 'moviegoer', 'review', 'tried', 'hi', 'signed', '3', 'month', 'regal', 'subscription', 'ending', 'day', 'like', 'continue', 'having', 'subscription', 'm', 'moving', 'month', 'location', 'regal', 'theatre', 'like', '45', 'mile', 'radius', 'able', '3', 'month', 'subscription', 'instead', 'signing', '12', 'month', 'commitment', 'shouldn', 't', 'class', 'actionable', 'offense', 'thought', 'ftc', 'rule', 'business', 'making', 'difficult', 'cancel', 'reoccurring', 'subscription', 'fact', 'email', 'cancel', 'regal', 'unlimited', 'subscription', 'suspicious', 'potentially', 'nefarious', 'contact', 'info', 'readily', 'available', 'fact', 'm', 'pretty', 'sure', 'actively', 'bury', 'information', 'scroll', 'bylaw', 'find', 'contact', 'info', 'cancel', 'subscription', 'regal', 'contact', 'link', 'drop', 'box', 'option', 'canceled', 'subscription', 'pretty', 'suspicious', 'id', 'sign', 'regal', 'unlimited', 'gifting', 'regal', 'unlimited', 's', 'like', 'photo', 'id', 'kind', 'want', 'surprise']\n",
      "number of words =  457\n"
     ]
    }
   ],
   "source": [
    "# Pre-processing comments\n",
    "\n",
    "# convert to a string object\n",
    "# map to a list of strings\n",
    "strings_all = [str(i) for i in comments_body]\n",
    "# join all strings spearated by a commma\n",
    "strings_uncleaned = ' , '.join(strings_all)\n",
    "strings_uncleaned\n",
    "\n",
    "# tokenizing and cleaning strings\n",
    "tokenizer = RegexpTokenizer('\\w+|\\$[\\d\\.]+|http\\S+')\n",
    "tokenized_string = tokenizer.tokenize(strings_uncleaned)\n",
    "\n",
    "# converting all tokens to lowercase\n",
    "tokenized_string_lower = [word.lower() for word in tokenized_string]\n",
    "\n",
    "# removing stop words\n",
    "nlp = en_core_web_sm.load()\n",
    "all_stopwords = nlp.Defaults.stop_words\n",
    "tokens_without_stopwords = [word for word in tokenized_string_lower if not word in all_stopwords]\n",
    "\n",
    "# stemming might return a root word that is not an actual word\n",
    "# whereas, lemmatizing returns a root word that is an actual language word.\n",
    "# let's normalize words via lemmatizing\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "lemmatized_tokens = ([lemmatizer.lemmatize(w) for w in tokens_without_stopwords])\n",
    "print(lemmatized_tokens)\n",
    "\n",
    "print(\"number of words = \", len(lemmatized_tokens))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying a sentiment analyzer (VADER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>go</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>photo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5267</td>\n",
       "      <td>kind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>want</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2732</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neg  neu  pos  compound          words\n",
       "0    0.0  1.0  0.0    0.0000  automatically\n",
       "1    0.0  1.0  0.0    0.0000             go\n",
       "2    0.0  1.0  0.0    0.0000          month\n",
       "3    0.0  1.0  0.0    0.0000          month\n",
       "4    0.0  1.0  0.0    0.0000            end\n",
       "..   ...  ...  ...       ...            ...\n",
       "452  0.0  1.0  0.0    0.0000          photo\n",
       "453  0.0  1.0  0.0    0.0000             id\n",
       "454  0.0  0.0  1.0    0.5267           kind\n",
       "455  0.0  0.0  1.0    0.0772           want\n",
       "456  0.0  0.0  1.0    0.2732       surprise\n",
       "\n",
       "[457 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying a sentiment analyzer (VADER)\n",
    "sia = SIA()\n",
    "results = []\n",
    "\n",
    "# get polarity score for words\n",
    "for word in lemmatized_tokens:\n",
    "    pol_score = sia.polarity_scores(word)\n",
    "    pol_score['words'] = word\n",
    "    results.append(pol_score)\n",
    "results\n",
    "\n",
    "pd.set_option('display.max_columns', None, 'max_colwidth', None)\n",
    "df = pd.DataFrame.from_records(results)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>words</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>automatically</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>go</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>month</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>month</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>end</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neg  neu  pos  compound          words  label\n",
       "0  0.0  1.0  0.0       0.0  automatically      0\n",
       "1  0.0  1.0  0.0       0.0             go      0\n",
       "2  0.0  1.0  0.0       0.0          month      0\n",
       "3  0.0  1.0  0.0       0.0          month      0\n",
       "4  0.0  1.0  0.0       0.0            end      0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compound score as seen above is a normalized single unideimensional measure of sentiment for a given word.\n",
    "# adding a label column\n",
    "df['label'] = 0 # neutral\n",
    "df.loc[df['compound'] > 0.10, 'label'] = 1 # positive\n",
    "df.loc[df['compound'] < -0.10, 'label'] = -1 # negative\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    380\n",
       " 1     52\n",
       "-1     25\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many words are neutral (0), positive (1) and negative (-1) in sentiment\n",
    "df.label.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of them are neutral. Comments seem to be neutral - positive skewed.There are only 2 negative words.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>words</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2960</td>\n",
       "      <td>complaint</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4404</td>\n",
       "      <td>suck</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>cancel</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>hard</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4939</td>\n",
       "      <td>steal</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4939</td>\n",
       "      <td>steal</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>pay</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>pay</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>pay</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3612</td>\n",
       "      <td>ridiculous</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2023</td>\n",
       "      <td>charged</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4404</td>\n",
       "      <td>frustrating</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>warn</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>cancel</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>cancel</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4019</td>\n",
       "      <td>annoying</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>offense</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3612</td>\n",
       "      <td>difficult</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>cancel</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>cancel</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3612</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2500</td>\n",
       "      <td>cancel</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2732</td>\n",
       "      <td>drop</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3612</td>\n",
       "      <td>suspicious</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     neg  neu  pos  compound        words  label\n",
       "18   1.0  0.0  0.0   -0.2960    complaint     -1\n",
       "30   1.0  0.0  0.0   -0.2500    cancelled     -1\n",
       "32   1.0  0.0  0.0   -0.4404         suck     -1\n",
       "34   1.0  0.0  0.0   -0.2500       cancel     -1\n",
       "80   1.0  0.0  0.0   -0.1027         hard     -1\n",
       "84   1.0  0.0  0.0   -0.4939        steal     -1\n",
       "86   1.0  0.0  0.0   -0.4939        steal     -1\n",
       "136  1.0  0.0  0.0   -0.1027          pay     -1\n",
       "141  1.0  0.0  0.0   -0.1027          pay     -1\n",
       "156  1.0  0.0  0.0   -0.1027          pay     -1\n",
       "192  1.0  0.0  0.0   -0.3612   ridiculous     -1\n",
       "201  1.0  0.0  0.0   -0.2023      charged     -1\n",
       "220  1.0  0.0  0.0   -0.4404  frustrating     -1\n",
       "223  1.0  0.0  0.0   -0.1027         warn     -1\n",
       "258  1.0  0.0  0.0   -0.2500       cancel     -1\n",
       "268  1.0  0.0  0.0   -0.2500       cancel     -1\n",
       "321  1.0  0.0  0.0   -0.4019     annoying     -1\n",
       "396  1.0  0.0  0.0   -0.2500      offense     -1\n",
       "402  1.0  0.0  0.0   -0.3612    difficult     -1\n",
       "403  1.0  0.0  0.0   -0.2500       cancel     -1\n",
       "408  1.0  0.0  0.0   -0.2500       cancel     -1\n",
       "412  1.0  0.0  0.0   -0.3612   suspicious     -1\n",
       "431  1.0  0.0  0.0   -0.2500       cancel     -1\n",
       "436  1.0  0.0  0.0   -0.2732         drop     -1\n",
       "442  1.0  0.0  0.0   -0.3612   suspicious     -1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at negative words\n",
    "df[df['label']== -1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring positive and negative words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.figure.Figure"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's look at how many positive and negative words are found\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "\n",
    "counts = df.loc[df['label'] != 0]['label'].value_counts(normalize=True) * 100\n",
    "\n",
    "sns.barplot(x = counts.index, y = counts, ax = ax)\n",
    "\n",
    "ax.set_xticklabels(['Negative', 'Positive'])\n",
    "ax.set_ylabel(\"Percentage\")\n",
    "type(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1    67.532468\n",
      "-1    32.467532\n",
      "Name: label, dtype: float64\n",
      " 1    67.532468\n",
      "-1    32.467532\n",
      "Name: label, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# get percentage of postive and negative words in all the comments \n",
    "sentiment_perc_of_words = df.loc[df['label'] != 0]['label'].value_counts(normalize=True) * 100\n",
    "\n",
    "print(sentiment_perc_of_words)\n",
    "if sentiment_perc_of_words[1] == 100:\n",
    "    sentiment_perc_of_words[-1] = 0\n",
    "if sentiment_perc_of_words[-1] == 100:\n",
    "    sentiment_perc_of_words[1] = 0\n",
    "\n",
    "print(sentiment_perc_of_words)\n",
    "\n",
    "#print(round(sentiment_perc_of_words[1],2))\n",
    "#print(round(sentiment_perc_of_words[-1],2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'gift': 11, 'like': 7, 'best': 3, 'great': 3, 'kind': 2, 'surprise': 2, 'nice': 2, 'sure': 2, 'good': 2, 'pretty': 2, ...})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequency distribution of the 20 most common positive words\n",
    "frequent_pos_words = FreqDist(df.loc[df['label'] == 1].words)\n",
    "frequent_pos_words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(frequent_pos_words.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('cancel', 6),\n",
       " ('pay', 3),\n",
       " ('steal', 2),\n",
       " ('suspicious', 2),\n",
       " ('complaint', 1),\n",
       " ('cancelled', 1),\n",
       " ('suck', 1),\n",
       " ('hard', 1),\n",
       " ('ridiculous', 1),\n",
       " ('charged', 1),\n",
       " ('frustrating', 1),\n",
       " ('warn', 1),\n",
       " ('annoying', 1),\n",
       " ('offense', 1),\n",
       " ('difficult', 1),\n",
       " ('drop', 1)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequency distribution of the 20 most common negative words\n",
    "frequent_neg_words = FreqDist(df.loc[df['label'] == -1].words)\n",
    "frequent_neg_words.most_common(20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate a word cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'green': ['fan',\n",
       "  'agree',\n",
       "  'wish',\n",
       "  'best',\n",
       "  'luck',\n",
       "  'appreciate',\n",
       "  'legal',\n",
       "  'like',\n",
       "  'best',\n",
       "  'kind',\n",
       "  'gift',\n",
       "  'surprise',\n",
       "  'best',\n",
       "  'gift',\n",
       "  'gift',\n",
       "  'gift',\n",
       "  'gift',\n",
       "  'gift',\n",
       "  'gift',\n",
       "  'gift',\n",
       "  'free',\n",
       "  'credit',\n",
       "  'gift',\n",
       "  'gift',\n",
       "  'nice',\n",
       "  'gift',\n",
       "  'credited',\n",
       "  'yes',\n",
       "  'sure',\n",
       "  'great',\n",
       "  'better',\n",
       "  'nice',\n",
       "  'like',\n",
       "  'awesome',\n",
       "  'worth',\n",
       "  'like',\n",
       "  'certain',\n",
       "  'great',\n",
       "  'good',\n",
       "  'good',\n",
       "  'like',\n",
       "  'great',\n",
       "  'like',\n",
       "  'like',\n",
       "  'commitment',\n",
       "  'pretty',\n",
       "  'sure',\n",
       "  'actively',\n",
       "  'pretty',\n",
       "  'like',\n",
       "  'kind',\n",
       "  'surprise'],\n",
       " 'red': ['complaint',\n",
       "  'cancelled',\n",
       "  'suck',\n",
       "  'cancel',\n",
       "  'hard',\n",
       "  'steal',\n",
       "  'steal',\n",
       "  'pay',\n",
       "  'pay',\n",
       "  'pay',\n",
       "  'ridiculous',\n",
       "  'charged',\n",
       "  'frustrating',\n",
       "  'warn',\n",
       "  'cancel',\n",
       "  'cancel',\n",
       "  'annoying',\n",
       "  'offense',\n",
       "  'difficult',\n",
       "  'cancel',\n",
       "  'cancel',\n",
       "  'suspicious',\n",
       "  'cancel',\n",
       "  'drop',\n",
       "  'suspicious']}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_words_dict = {\n",
    "    'green': list(df.loc[df['label'] == 1].words),\n",
    "    'red': list(df.loc[df['label'] == -1].words)\n",
    "}\n",
    "\n",
    "# neutral words are grey\n",
    "default_color = 'grey'\n",
    "color_words_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleGroupedColorFunc(object):\n",
    "    def __init__(self, color_to_words, default_color):\n",
    "        self.word_to_color = {word: color\n",
    "                              for (color, words) in color_to_words.items()\n",
    "                              for word in words}\n",
    "\n",
    "        self.default_color = default_color\n",
    "\n",
    "    def __call__(self, word, **kwargs):\n",
    "        return self.word_to_color.get(word, self.default_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc = WordCloud(collocations=False, background_color='white').generate_from_frequencies(frequencies=FreqDist(df.words))\n",
    "\n",
    "grouped_color_func = SimpleGroupedColorFunc(color_words_dict, default_color)\n",
    "wc.recolor(color_func=grouped_color_func)\n",
    "plt.figure()\n",
    "plt.imshow(wc, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying bar charts for common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Bar Graph of Frequent Positive Words=%{x}<br>Count=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "gift",
          "like",
          "best",
          "great",
          "kind",
          "surprise",
          "nice",
          "sure",
          "good",
          "pretty",
          "fan",
          "agree",
          "wish",
          "luck",
          "appreciate",
          "legal",
          "free",
          "credit",
          "credited",
          "yes"
         ],
         "xaxis": "x",
         "y": [
          11,
          7,
          3,
          3,
          2,
          2,
          2,
          2,
          2,
          2,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1,
          1
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
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             0.8,
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             0.9,
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           "#00cc96",
           "#ab63fa",
           "#FFA15A",
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           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Commonly Used Negative Words Count"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Bar Graph of Frequent Negative Words"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most common negative words\n",
    "\n",
    "neg_freq_df = pd.DataFrame(frequent_neg_words.most_common(no_of_words))\n",
    "neg_freq_df = neg_freq_df.rename(columns = {0: \"Bar Graph of Frequent Negative Words\", 1: \"Count\"}, inplace=False)\n",
    "\n",
    "fig = px.bar(neg_freq_df, x = \"Bar Graph of Frequent Negative Words\", y = \"Count\", title = \"Commonly Used Negative Words Count\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ideas for improvement:\n",
    "\n",
    "There may be some sentences such as \"They try to take away my uncertainity and nervousness\" which is obviously positive but the individual words \"uncertainity\" and \"nervousness\" contribute more to negative score."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration on how inference of a fine tuned model can be done and demonstrating an alternative approach to identify reddit products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, Trainer, AutoModelForTokenClassification\n",
    "model = AutoModelForTokenClassification.from_pretrained(\"anudeepvanjavakam/distilbert_finetuned_wnut17_wandb_ner\")\n",
    "trainer = Trainer(model=model)\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"anudeepvanjavakam/distilbert_finetuned_wnut17_wandb_ner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "id2tag={0: 'O',\n",
    "        1: 'B-corporation',\n",
    "        2: 'I-corporation',\n",
    "        3: 'B-creative-work',\n",
    "        4: 'I-creative-work',\n",
    "        5: 'B-group',\n",
    "        6: 'I-group',\n",
    "        7: 'B-location',\n",
    "        8: 'I-location',\n",
    "        9: 'B-person',\n",
    "        10: 'I-person',\n",
    "        11: 'B-product',\n",
    "        12: 'I-product'}\n",
    "def tag_sentence(text:str):\n",
    "    # convert our text to a  tokenized sequence\n",
    "    inputs = tokenizer(text, truncation=True, return_tensors=\"pt\")#.to(\"cuda\")\n",
    "    # get outputs\n",
    "    outputs = model(**inputs)\n",
    "    # convert to probabilities with softmax\n",
    "    probs = outputs[0][0].softmax(1)\n",
    "    # get the tags with the highest probability\n",
    "    word_tags = [(tokenizer.decode(inputs['input_ids'][0][i].item()), id2tag[tagid.item()], np.round(probs[i][tagid].item() *100,2) ) \n",
    "                  for i, tagid in enumerate (probs.argmax(axis=1))]\n",
    "\n",
    "    return pd.DataFrame(word_tags, columns=['word', 'tag', 'probability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[9.9869e-01, 1.2160e-04, 8.6211e-05,  ..., 7.9969e-05, 1.7210e-04,\n",
       "         1.4388e-04],\n",
       "        [9.9901e-01, 1.0065e-04, 5.7168e-05,  ..., 5.4531e-05, 1.1765e-04,\n",
       "         9.6648e-05],\n",
       "        [9.9880e-01, 8.5012e-05, 8.4165e-05,  ..., 8.0438e-05, 1.4654e-04,\n",
       "         1.3866e-04],\n",
       "        ...,\n",
       "        [9.9902e-01, 8.0855e-05, 6.6490e-05,  ..., 4.9744e-05, 1.2823e-04,\n",
       "         1.1736e-04],\n",
       "        [9.9904e-01, 8.2292e-05, 6.5471e-05,  ..., 5.4804e-05, 1.1888e-04,\n",
       "         1.0058e-04],\n",
       "        [9.9458e-01, 5.4785e-04, 4.3356e-04,  ..., 1.6870e-04, 5.8321e-04,\n",
       "         6.2125e-04]], grad_fn=<SoftmaxBackward0>)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = tokenizer(sentences, truncation=True, return_tensors=\"pt\")\n",
    "outputs = model(**inputs)\n",
    "probs = outputs[0][0].softmax(1)\n",
    "probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The best. I use it almost everyday of the week. This week alone I saw How To Blow Up A Pipeline, Suzume, Renfield, John Wick Chapter 4, Beau is Afraid, and I’m seeing Return of the Jedi tomorrow.',\n",
       " 'I have Regal Unlimited and AMC A-List. They’re both great. It really just depends on what theater you prefer. My Regal has better seating, but my AMC has really nice Dolby and IMAX theaters.',\n",
       " \"It's awesome if you live close enough to a regal theatre and have time to go.\",\n",
       " 'Not to shill it, but you generally get your moneys worth within two films a month and like some others in this thread, I hit up the theater once a week and seek out certain IMAX showings and it’s only a $6 surcharge. Great service, I had AMC A-List and that service was also good, but they do premium pricing where Regal hasn’t yet adopted that practice.',\n",
       " 'I mean yea, it’s a good deal, but the $0.53 convenience fee for every time you use it is annoying af.',\n",
       " \"Have you guys used the Regal's subscription service ? I am thinking of getting one since it looks like a great deal for regular moviegoers. What is your review of it if you have tried it before ?\"]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comments_body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_term = \"best gaming monitor\"\n",
    "comments, top_comments, no_of_posts, no_of_comments = get_comments(search_term=search_term,no_of_posts=2, no_of_comments=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>tag</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[CLS]</td>\n",
       "      <td>O</td>\n",
       "      <td>99.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>i</td>\n",
       "      <td>O</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>’</td>\n",
       "      <td>O</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ve</td>\n",
       "      <td>O</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>been</td>\n",
       "      <td>O</td>\n",
       "      <td>99.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    word tag  probability\n",
       "0  [CLS]   O        99.85\n",
       "1      i   O        99.90\n",
       "2      ’   O        99.88\n",
       "3     ve   O        99.90\n",
       "4   been   O        99.90"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = []\n",
    "for comment in comments:\n",
    "    dfs.append(tag_sentence(comment))\n",
    "\n",
    "results_df = pd.concat(dfs)\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>tag</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g</td>\n",
       "      <td>B-product</td>\n",
       "      <td>60.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>##3</td>\n",
       "      <td>I-product</td>\n",
       "      <td>70.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>titan</td>\n",
       "      <td>B-product</td>\n",
       "      <td>73.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c</td>\n",
       "      <td>B-product</td>\n",
       "      <td>47.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>308</td>\n",
       "      <td>B-product</td>\n",
       "      <td>73.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>##0</td>\n",
       "      <td>I-product</td>\n",
       "      <td>64.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>##ti</td>\n",
       "      <td>I-product</td>\n",
       "      <td>64.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>l</td>\n",
       "      <td>B-product</td>\n",
       "      <td>64.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>##g</td>\n",
       "      <td>I-product</td>\n",
       "      <td>62.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>27</td>\n",
       "      <td>I-product</td>\n",
       "      <td>64.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ole</td>\n",
       "      <td>B-product</td>\n",
       "      <td>75.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>c2</td>\n",
       "      <td>B-product</td>\n",
       "      <td>75.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>275</td>\n",
       "      <td>B-product</td>\n",
       "      <td>60.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>apex</td>\n",
       "      <td>B-product</td>\n",
       "      <td>74.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c2</td>\n",
       "      <td>B-product</td>\n",
       "      <td>47.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1500</td>\n",
       "      <td>B-product</td>\n",
       "      <td>76.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>##ni</td>\n",
       "      <td>I-product</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>##t</td>\n",
       "      <td>I-product</td>\n",
       "      <td>72.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>q</td>\n",
       "      <td>I-product</td>\n",
       "      <td>64.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>##d</td>\n",
       "      <td>I-product</td>\n",
       "      <td>69.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-</td>\n",
       "      <td>I-product</td>\n",
       "      <td>50.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ole</td>\n",
       "      <td>I-product</td>\n",
       "      <td>52.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>##d</td>\n",
       "      <td>I-product</td>\n",
       "      <td>48.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>wr</td>\n",
       "      <td>B-product</td>\n",
       "      <td>47.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>##g</td>\n",
       "      <td>I-product</td>\n",
       "      <td>57.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>I-product</td>\n",
       "      <td>59.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>##g</td>\n",
       "      <td>I-product</td>\n",
       "      <td>51.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>##q</td>\n",
       "      <td>I-product</td>\n",
       "      <td>46.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>##90</td>\n",
       "      <td>I-product</td>\n",
       "      <td>48.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>halo</td>\n",
       "      <td>B-product</td>\n",
       "      <td>77.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>infinite</td>\n",
       "      <td>I-product</td>\n",
       "      <td>65.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>as</td>\n",
       "      <td>B-product</td>\n",
       "      <td>45.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>l</td>\n",
       "      <td>B-product</td>\n",
       "      <td>53.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         word        tag  probability\n",
       "7           g  B-product        60.78\n",
       "8         ##3  I-product        70.16\n",
       "16      titan  B-product        73.82\n",
       "7           c  B-product        47.78\n",
       "137       308  B-product        73.84\n",
       "138       ##0  I-product        64.48\n",
       "139      ##ti  I-product        64.57\n",
       "147         l  B-product        64.32\n",
       "148       ##g  I-product        62.88\n",
       "149        27  I-product        64.47\n",
       "1         ole  B-product        75.22\n",
       "24         c2  B-product        75.97\n",
       "4         275  B-product        60.36\n",
       "10       apex  B-product        74.49\n",
       "10         c2  B-product        47.61\n",
       "5        1500  B-product        76.71\n",
       "6        ##ni  I-product        38.07\n",
       "7         ##t  I-product        72.99\n",
       "8           q  I-product        64.35\n",
       "9         ##d  I-product        69.53\n",
       "10          -  I-product        50.69\n",
       "11        ole  I-product        52.32\n",
       "12        ##d  I-product        48.54\n",
       "22         wr  B-product        47.38\n",
       "2         ##g  I-product        57.01\n",
       "3          48  I-product        59.69\n",
       "4         ##g  I-product        51.36\n",
       "5         ##q  I-product        46.03\n",
       "6        ##90  I-product        48.09\n",
       "14       halo  B-product        77.53\n",
       "15   infinite  I-product        65.52\n",
       "38         as  B-product        45.66\n",
       "43          l  B-product        53.96"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = results_df[results_df['tag'].isin(['B-product','I-product'])]#.sort_values(by='probability',ascending=False)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('g', 'B-product'),\n",
       " ('##3', 'I-product'),\n",
       " ('titan', 'B-product'),\n",
       " ('c', 'B-product'),\n",
       " ('308', 'B-product'),\n",
       " ('##0', 'I-product'),\n",
       " ('##ti', 'I-product'),\n",
       " ('l', 'B-product'),\n",
       " ('##g', 'I-product'),\n",
       " ('27', 'I-product'),\n",
       " ('ole', 'B-product'),\n",
       " ('c2', 'B-product'),\n",
       " ('275', 'B-product'),\n",
       " ('apex', 'B-product'),\n",
       " ('c2', 'B-product'),\n",
       " ('1500', 'B-product'),\n",
       " ('##ni', 'I-product'),\n",
       " ('##t', 'I-product'),\n",
       " ('q', 'I-product'),\n",
       " ('##d', 'I-product'),\n",
       " ('-', 'I-product'),\n",
       " ('ole', 'I-product'),\n",
       " ('##d', 'I-product'),\n",
       " ('wr', 'B-product'),\n",
       " ('##g', 'I-product'),\n",
       " ('48', 'I-product'),\n",
       " ('##g', 'I-product'),\n",
       " ('##q', 'I-product'),\n",
       " ('##90', 'I-product'),\n",
       " ('halo', 'B-product'),\n",
       " ('infinite', 'I-product'),\n",
       " ('as', 'B-product'),\n",
       " ('l', 'B-product')]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = list(zip(results_df.word, results_df.tag))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   predited product mentions\n",
      "0                         g3\n",
      "1                      titan\n",
      "2                          c\n",
      "3                     3080ti\n",
      "4                       lg27\n",
      "5                        ole\n",
      "6                         c2\n",
      "7                        275\n",
      "8                       apex\n",
      "9                         c2\n",
      "10            1500nitqd-oled\n",
      "11                 wrg48gq90\n",
      "12              haloinfinite\n",
      "13                        as\n",
      "14                         l\n"
     ]
    }
   ],
   "source": [
    "\n",
    "combined_values = []\n",
    "current_value = \"\"\n",
    "for word, tag in data:\n",
    "    if tag == \"B-product\":\n",
    "        if current_value:\n",
    "            combined_values.append(current_value.strip())\n",
    "            current_value = \"\"\n",
    "        current_value += word\n",
    "    else:\n",
    "        current_value += word\n",
    "\n",
    "# Append the last combined value if any\n",
    "if current_value:\n",
    "    combined_values.append(current_value.strip())\n",
    "\n",
    "df = pd.DataFrame({\"predited product mentions\": combined_values})\n",
    "\n",
    "# remove any #s\n",
    "df = df.replace(\"#\",\"\",regex=True)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An interesting extension is to use promptify package for predicting products. It leverages Open AI's Chat GPT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "from promptify import OpenAI\n",
    "from promptify import Prompter\n",
    "\n",
    "model = OpenAI('OPEN_AI_SECRET_KEY') # or `HubModel()` for Huggingface-based inference\n",
    "nlp_prompter = Prompter(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I’ve been using a 55 inch OLED for a few weeks now and I really like it. \\n\\nEven for work I just put my main focus window in the middle and have whatever other windows I need spread around it. It took me a while for my brain to realize I didn’t need to have two full screen windows like on two monitors. I’d say you could probably have 9 windows open on a 55 inch that display the same aspect ratio on the information as a full screen monitor.\\n\\nFor gaming I can see every edge of the screen without moving my head. Every other aspect of it is awesome. I love it.',\n",
       " 'Didn\\'t care for 48in myself.   Ended up getting the 42\" C2 and love it.   Wouldn\\'t go any bigger.',\n",
       " \"There are people who like 55inchers, 48inchers, 42inchers...then there are people who swear that anything bigger than 27inch is for noobs.\\n\\nNot one of these people are the objective right. It's all subjective totally and completely.\\n\\nI'd rather have a 42 or 48 inch monitor because I love big screens and gaming on it is a treat TO ME. No one other than YOU is right or wrong. And don't listen to people on here who comment as if they are representing 100% of the gamer community. Display size is completely subjective and YOUR EYES ARE NOT THEIR EYES.\\n\\nEnjoy your fattie monitor because I am enjoying mine. Everyone enjoy everything!\",\n",
       " 'Dude why so far',\n",
       " 'I read the title of this post in my head in this style: https://youtu.be/YDXKAwzdxn4?t=37',\n",
       " 'What a beautiful setup.',\n",
       " 'Desk size?',\n",
       " 'I would hold out for a G3 since its the only new model coming with an MLA panel which can hit 1500nits.  Real excited for the extra brightness.',\n",
       " 'Great setup! Alanah’s Funko and all 🤭. Liking the Titan EVO so far?',\n",
       " 'What mount/arm do you use?  Does the auto brightness limiting bother you in dark games?',\n",
       " 'Looking good, Little light!  48\" here as well.. perfect size for what I want to do.  Seemed large for 2 days lol.',\n",
       " 'Whispers softly: LTTStore.com',\n",
       " 'looks good.',\n",
       " 'Did you have a higher refresh one? How does it compare in apex?',\n",
       " 'I ended up wall mounting my CX at my desk last year lasted about 3-4 months till it was just too big even 38\" away from screen similar setup as your desk it was too much for me looking up and down in fps games became painful so i decided that when i play fps ill play in custom res of 3840x1600 and ended up with a bit of burn in top and bottom bars from it. Was able to get it back to normal with some pixel refresh but ill be waiting for a much smaller version for my desk! Also i found the performance demand for 4k 120hz to be a little much of a strain on a 3080ti even when oled monitors or the lg 27\" 1440 oled drops thats the road ill be going down way better performance @ 1440p! love the setup! i did the same with the vertical side monitor if u fit a 27\" in portrait its the same height as the 48\" CX looks amazing!',\n",
       " 'OLEDs are definitely the best gaming panels but unfortunately this is just too big for my desktop. Even the 42 inch C2 is too big. 32 inches is about the max for my setup.',\n",
       " 'Its a tv men.XD',\n",
       " 'Is that a 275R?',\n",
       " 'How deep is your desk? Do you play apex on mouse and keyboard ?',\n",
       " \"Don't you get bothered by the fact that it's not centered? I'd rearrange everything in my room for that lol\",\n",
       " \"What's the difference between this and the C2?\",\n",
       " 'wait until he sees 1500nit qd-oled with A LOT better color representation than this old wrgb junk :)',\n",
       " 'LG 48GQ900-B is better imo',\n",
       " 'Depends on game and skill. If you are nasty at shooters like halo infinite - no one is buying a 42/48 C2 etc.\\n\\n240fps 1440p all the way, ASUS strix or lg 27oled',\n",
       " '']"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I’ve been using a 55 inch OLED for a few weeks now and I really like it. \\n\\nEven for work I just put my main focus window in the middle and have whatever other windows I need spread around it. It took me a while for my brain to realize I didn’t need to have two full screen windows like on two monitors. I’d say you could probably have 9 windows open on a 55 inch that display the same aspect ratio on the information as a full screen monitor.\\n\\nFor gaming I can see every edge of the screen without moving my head. Every other aspect of it is awesome. I love it. Didn\\'t care for 48in myself.   Ended up getting the 42\" C2 and love it.   Wouldn\\'t go any bigger. There are people who like 55inchers, 48inchers, 42inchers...then there are people who swear that anything bigger than 27inch is for noobs.\\n\\nNot one of these people are the objective right. It\\'s all subjective totally and completely.\\n\\nI\\'d rather have a 42 or 48 inch monitor because I love big screens and gaming on it is a treat TO ME. No one other than YOU is right or wrong. And don\\'t listen to people on here who comment as if they are representing 100% of the gamer community. Display size is completely subjective and YOUR EYES ARE NOT THEIR EYES.\\n\\nEnjoy your fattie monitor because I am enjoying mine. Everyone enjoy everything! Dude why so far I read the title of this post in my head in this style: https://youtu.be/YDXKAwzdxn4?t=37 What a beautiful setup. Desk size? I would hold out for a G3 since its the only new model coming with an MLA panel which can hit 1500nits.  Real excited for the extra brightness. Great setup! Alanah’s Funko and all 🤭. Liking the Titan EVO so far? What mount/arm do you use?  Does the auto brightness limiting bother you in dark games? Looking good, Little light!  48\" here as well.. perfect size for what I want to do.  Seemed large for 2 days lol. Whispers softly: LTTStore.com looks good. Did you have a higher refresh one? How does it compare in apex? I ended up wall mounting my CX at my desk last year lasted about 3-4 months till it was just too big even 38\" away from screen similar setup as your desk it was too much for me looking up and down in fps games became painful so i decided that when i play fps ill play in custom res of 3840x1600 and ended up with a bit of burn in top and bottom bars from it. Was able to get it back to normal with some pixel refresh but ill be waiting for a much smaller version for my desk! Also i found the performance demand for 4k 120hz to be a little much of a strain on a 3080ti even when oled monitors or the lg 27\" 1440 oled drops thats the road ill be going down way better performance @ 1440p! love the setup! i did the same with the vertical side monitor if u fit a 27\" in portrait its the same height as the 48\" CX looks amazing! OLEDs are definitely the best gaming panels but unfortunately this is just too big for my desktop. Even the 42 inch C2 is too big. 32 inches is about the max for my setup. Its a tv men.XD Is that a 275R? How deep is your desk? Do you play apex on mouse and keyboard ? Don\\'t you get bothered by the fact that it\\'s not centered? I\\'d rearrange everything in my room for that lol What\\'s the difference between this and the C2? wait until he sees 1500nit qd-oled with A LOT better color representation than this old wrgb junk :) LG 48GQ900-B is better imo Depends on game and skill. If you are nasty at shooters like halo infinite - no one is buying a 42/48 C2 etc.\\n\\n240fps 1440p all the way, ASUS strix or lg 27oled '"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences = ' '.join(comments)\n",
    "sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"The LG UltraGear 27GL83A-B stands out as the best overall gaming monitor, boasting a stunning 27-inch QHD (1440p) IPS display that delivers breathtaking visuals with vibrant colors and sharp details.\"\n",
    "result = nlp_prompter.fit('ner.jinja',\n",
    "                          domain      = 'products',\n",
    "                          text_input  = sentences,\n",
    "                          labels      = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prompt_tokens': 992,\n",
       " 'completion_tokens': 343,\n",
       " 'total_tokens': 1335,\n",
       " 'text': ' [{\\'T\\': \\'Product\\', \\'E\\': \\'55 inch OLED\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'48in\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'42\" C2\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'27inch\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'G3\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'MLA panel\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'1500nits\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'Funko\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'Titan EVO\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'48\" CX\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'3080ti\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'3840x1600\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'27\" 1440 OLED\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'LG 48GQ900-B\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'ASUS strix\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'LG 27OLED\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'Halo Infinite\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'1440p\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'240fps\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'275R\\'}, {\\'branch\\': \\'Product\\', \\'group\\': \\'Display\\'}]'}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' [{\\'T\\': \\'Product\\', \\'E\\': \\'55 inch OLED\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'48in\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'42\" C2\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'27inch\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'G3\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'MLA panel\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'1500nits\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'Funko\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'Titan EVO\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'48\" CX\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'3080ti\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'3840x1600\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'27\" 1440 OLED\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'LG 48GQ900-B\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'ASUS strix\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'LG 27OLED\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'Halo Infinite\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'1440p\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'240fps\\'}, {\\'T\\': \\'Product\\', \\'E\\': \\'275R\\'}, {\\'branch\\': \\'Product\\', \\'group\\': \\'Display\\'}]'"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = result['text']\n",
    "tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>E</th>\n",
       "      <th>branch</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Product</td>\n",
       "      <td>55 inch OLED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Product</td>\n",
       "      <td>48in</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Product</td>\n",
       "      <td>42\" C2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Product</td>\n",
       "      <td>27inch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Product</td>\n",
       "      <td>G3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Product</td>\n",
       "      <td>MLA panel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Product</td>\n",
       "      <td>1500nits</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Product</td>\n",
       "      <td>Funko</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Product</td>\n",
       "      <td>Titan EVO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Product</td>\n",
       "      <td>48\" CX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Product</td>\n",
       "      <td>3080ti</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Product</td>\n",
       "      <td>3840x1600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Product</td>\n",
       "      <td>27\" 1440 OLED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Product</td>\n",
       "      <td>LG 48GQ900-B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Product</td>\n",
       "      <td>ASUS strix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Product</td>\n",
       "      <td>LG 27OLED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Product</td>\n",
       "      <td>Halo Infinite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Product</td>\n",
       "      <td>1440p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Product</td>\n",
       "      <td>240fps</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Product</td>\n",
       "      <td>275R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Product</td>\n",
       "      <td>Display</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          T              E   branch    group\n",
       "0   Product   55 inch OLED      NaN      NaN\n",
       "1   Product           48in      NaN      NaN\n",
       "2   Product         42\" C2      NaN      NaN\n",
       "3   Product         27inch      NaN      NaN\n",
       "4   Product             G3      NaN      NaN\n",
       "5   Product      MLA panel      NaN      NaN\n",
       "6   Product       1500nits      NaN      NaN\n",
       "7   Product          Funko      NaN      NaN\n",
       "8   Product      Titan EVO      NaN      NaN\n",
       "9   Product         48\" CX      NaN      NaN\n",
       "10  Product         3080ti      NaN      NaN\n",
       "11  Product      3840x1600      NaN      NaN\n",
       "12  Product  27\" 1440 OLED      NaN      NaN\n",
       "13  Product   LG 48GQ900-B      NaN      NaN\n",
       "14  Product     ASUS strix      NaN      NaN\n",
       "15  Product      LG 27OLED      NaN      NaN\n",
       "16  Product  Halo Infinite      NaN      NaN\n",
       "17  Product          1440p      NaN      NaN\n",
       "18  Product         240fps      NaN      NaN\n",
       "19  Product           275R      NaN      NaN\n",
       "20      NaN            NaN  Product  Display"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_df = pd.DataFrame.from_records(eval(tags))\n",
    "tags_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags_df = tags_df[(~tags_df['T'].isna())].drop(columns = ['branch','group'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Product</td>\n",
       "      <td>55 inch OLED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Product</td>\n",
       "      <td>48in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Product</td>\n",
       "      <td>42\" C2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Product</td>\n",
       "      <td>27inch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Product</td>\n",
       "      <td>G3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Product</td>\n",
       "      <td>MLA panel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Product</td>\n",
       "      <td>1500nits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Product</td>\n",
       "      <td>Funko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Product</td>\n",
       "      <td>Titan EVO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Product</td>\n",
       "      <td>48\" CX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Product</td>\n",
       "      <td>3080ti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Product</td>\n",
       "      <td>3840x1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Product</td>\n",
       "      <td>27\" 1440 OLED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Product</td>\n",
       "      <td>LG 48GQ900-B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Product</td>\n",
       "      <td>ASUS strix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Product</td>\n",
       "      <td>LG 27OLED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Product</td>\n",
       "      <td>Halo Infinite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Product</td>\n",
       "      <td>1440p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Product</td>\n",
       "      <td>240fps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Product</td>\n",
       "      <td>275R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          T              E\n",
       "0   Product   55 inch OLED\n",
       "1   Product           48in\n",
       "2   Product         42\" C2\n",
       "3   Product         27inch\n",
       "4   Product             G3\n",
       "5   Product      MLA panel\n",
       "6   Product       1500nits\n",
       "7   Product          Funko\n",
       "8   Product      Titan EVO\n",
       "9   Product         48\" CX\n",
       "10  Product         3080ti\n",
       "11  Product      3840x1600\n",
       "12  Product  27\" 1440 OLED\n",
       "13  Product   LG 48GQ900-B\n",
       "14  Product     ASUS strix\n",
       "15  Product      LG 27OLED\n",
       "16  Product  Halo Infinite\n",
       "17  Product          1440p\n",
       "18  Product         240fps\n",
       "19  Product           275R"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References:\n",
    "- https://www.learndatasci.com/tutorials/predicting-reddit-news-sentiment-naive-bayes-text-classifiers/\n",
    "- https://www.nltk.org/\n",
    "- https://praw.readthedocs.io/en/latest/tutorials/comments.html#extracting-comments-with-praw\n",
    "- https://stackoverflow.com/questions/61919884/mapping-wordcloud-color-to-a-value-for-sentiment-analysis\n",
    "- https://www.learndatasci.com/tutorials/sentiment-analysis-reddit-headlines-pythons-nltk/\n",
    "- https://medium.com/analytics-vidhya/simplifying-social-media-sentiment-analysis-using-vader-in-python-f9e6ec6fc52f\n",
    "- https://docs.streamlit.io/\n",
    "- https://github.com/promptslab/Promptify\n",
    "\n",
    "\n"
   ]
  }
 ],
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