FinalProject_group154.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the social media site Reddit.com users can create posts for other Reddit users to view. On each of these posts, other users may leave comments and chat with their responses. Our group is interested in what these Redditors are saying over the months and what the most popular words of every month reveal about popular trend and contemporary events. We went through all the reddit posts in each month of 2008, and calculated the most and least used words that were unique to that month. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Kevin Soloway\n",
    "- Timothy Bian \n",
    "- Aarushi Shah \n",
    "- San Kang\n",
    "- Dingmei Gu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main data science questions are:\n",
    "##### Can the most commonly used words in top Reddit comments of 2008 reveal about major events and trends of the year?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were inspired by various sources that asked similar questions of different data sets. For example, one article released by Mark Chopping performed research on Amazon reviews to find trending reviews and spot fake reviews. Some questions that Mark Chopping was able to answer include:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* How Do Ratings Vary With Vine Membership?\n",
    "* How Do Ratings Vary With Marketplace (i.e. Region)?\n",
    "* How Have Ratings Altered Over Time?\n",
    "* Has Verification of Purchases Changed Over Time for Reviews?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may read more about Mark Chopping’s findings on his article here.\n",
    "\n",
    "Another source we used for inspiration are Word Clouds. Word Clouds are a visual way of analyzing the most popular words used in some data set. There is a website specifically made for generating word clouds, where the user inputs a website URL and it will output a word cloud of the most popular words used on that site. With our research, we are essentially creating an algorithm that would help create WordClouds for Reddit posts on a larger scale over a period of time. \n",
    "\n",
    "Explore Word Clouds here:\n",
    "https://www.wordclouds.com/\n",
    "\n",
    "Finally, we were inspired to find “trending” data by previous studies that explore such patterns over the years. For example, the Social Security Administration has a dedicated web-page of trending names in recent years. Trending names can be found by datasets of names of new-born babies or entire population. We wanted to translate that work into our project, and find trending words used on Reddit comments to explore how Reddit has changed over time. \n",
    "\n",
    "Explore Trending names here:\n",
    "https://www.ssa.gov/oact/babynames/#ht=0\n",
    "https://flowingdata.com/2013/07/29/the-most-trendy-names-in-us-history/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our group predicts that the top words of a single month will be related to each other, either in sentiment or topic. For example, an example of a group of popular words during a month could be “Trump”, “election”, “twitter”, etc. We believe that this would be the case because usually every month there is a different trending topic that will result in several reddit posts. As a result, the most commonly used words unique to that specific month would be related to each other. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the source of our datasets: http://files.pushshift.io/reddit/comments/\n",
    "\n",
    "In the data source, the comments are compressed and collected by month using the Reddit API. For each of these compressed files, there are hundreds of millions of observations that are comments made by Redditors. These files are compressed by month and individual comments are presented in JSON format such as the one below.  In these comments, a plethora of metadata is associated with the comment body that are very useful variables for analysis. For instance, the below comment contains information such as the score (by votes), the user name, the creation date, and the subreddit ID. There are files for comments ranging from December 2015 to February 2019. While we have access to millions of comments and data, we will only be looking at January 2008 - December 2008.\n",
    "\n",
    "Here is a data sample from the dataset:\n",
    "** Important tags to consider: “subreddit_id”, “body”, “author” **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```JSON\n",
    "{\"created_utc\":1134365725,\"author_flair_css_class\":null,\"score\":1,\"ups\":1,\"subreddit\":\"reddit.com\",\"stickied\":false,\"link_id\":\"t3_17866\",\"subreddit_id\":\"t5_6\",\"controversiality\":0,\"body\":\"The site states \\\"What can I use it for? Meeting notes, Reports, technical specs Sign-up sheets, proposals and much more...\\\", just like any other new breeed of sites that want us to store everything we have on the web. And they even guarantee multiple levels of security and encryption etc. But what prevents these web site operators fom accessing and/or stealing Meeting notes, Reports, technical specs Sign-up sheets, proposals and much more, for competitive or personal gains...? I am pretty sure that most of them are honest, but what's there to prevent me from setting up a good useful site and stealing all your data? Call me paranoid - I am.\",\"retrieved_on\":1473738411,\"distinguished\":null,\"gilded\":0,\"id\":\"c14\",\"edited\":false,\"parent_id\":\"t3_17866\",\"author\":\"zse7zse\",\"author_flair_text\":null}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Special focus on 2008 comments\n",
    "\n",
    "For the purpose of time and resource, we will only analyze 2008 comments. This is mostly due to the fact that Reddit have grown exponentially - even for 2008 the comments size are multile gbs (which require days to process!)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will begin processing our data set. We shall first import the appropriate libraries that we will use for data cleaning and plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /Users/san/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /Users/san/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package words to /Users/san/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/words.zip.\n"
     ]
    }
   ],
   "source": [
    "import bz2\n",
    "import multiprocessing\n",
    "import numpy\n",
    "from collections import defaultdict\n",
    "from collections import Counter\n",
    "import nltk\n",
    "import string\n",
    "from nltk.stem.porter import *\n",
    "import operator\n",
    "import math\n",
    "from textblob import TextBlob as tb\n",
    "import pandas as pd\n",
    "import json\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('words')\n",
    "from nltk.stem.snowball import SnowballStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used Natural Language Toolkit to clean up our dataset. We first used SnowballStemmer for English to remove morphological affixes from words in each comment, leaving only the word stem. Next, we used nltk.wordpunct_tokenize() which takes corpus of text and splits it into tokens, then, we used stop words of English, nltk.corpus.words.words() to filter out the non-English words and stop words which are not helpful for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseBzData(f):\n",
    "    source_file = bz2.BZ2File(f, \"r\")\n",
    "    comment_body = []\n",
    "    for line in source_file:\n",
    "        json_line = json.loads(line.decode())\n",
    "        # Drop user name of comment author\n",
    "        if \"author\" in json_line:\n",
    "            del json_line[\"author\"]\n",
    "        if \"body\" in json_line and json_line[\"score\"] >= 0:\n",
    "            comment_body.append(json_line[\"body\"])\n",
    "    return comment_body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer(\"english\")\n",
    "stopwords_set = set(stopwords.words('english'))\n",
    "words = set(nltk.corpus.words.words())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_comments(comment_body):\n",
    "    comment_list = []\n",
    "    cache = ''\n",
    "    for comment in comment_body:\n",
    "        comment = stemmer.stem(comment)\n",
    "        comment_cleaned = \" \".join(w for w in nltk.wordpunct_tokenize(comment) if w.lower() in words and len(w) > 1 and w not in stopwords_set)\n",
    "        comment_list.append(stemmer.stem(comment_cleaned))\n",
    "        cache += (\" \" + stemmer.stem(comment_cleaned))\n",
    "    return cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start processing 12 months' files...\n",
      "loading data for month 01\n",
      "loading data for month 02\n",
      "loading data for month 03\n",
      "loading data for month 04\n",
      "loading data for month 05\n",
      "loading data for month 06\n",
      "loading data for month 07\n",
      "loading data for month 08\n",
      "loading data for month 09\n",
      "loading data for month 10\n",
      "loading data for month 11\n",
      "loading data for month 12\n",
      "Done processing data\n"
     ]
    }
   ],
   "source": [
    "print(\"Start processing 12 months' files...\")\n",
    "all_documents = []\n",
    "for i in [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"08\", \"09\", \"10\", \"11\", \"12\"]:\n",
    "    print(\"loading data for month \" + str(i))\n",
    "    all_documents.append(clean_comments(parseBzData('RC_2008-' + i + '.bz2')))\n",
    "\n",
    "print(\"Done processing data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting TFIDF For Positive Comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract TF/IDF, we need to calculate IDF for each word and create a dictionary which stores the word as key and its IDF result as the value. The IDF value will decrease the weight for commonly used words and increase the weight for words that are not used very much in the whole year of 2008. For each month, we calculated the TF/IDF by multiplying the frequency of each word in the month and the IDF value of the word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateWordCount(data):\n",
    "    word_count = defaultdict(int)\n",
    "    counter = 0\n",
    "    for d in data:\n",
    "        counter += 1\n",
    "        print(\"loading data for month \" + str(counter))\n",
    "        c = set(list(tb(d).words))\n",
    "        for w in c:\n",
    "            word_count[w] += 1\n",
    "    return word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data for month 1\n",
      "loading data for month 2\n",
      "loading data for month 3\n",
      "loading data for month 4\n",
      "loading data for month 5\n",
      "loading data for month 6\n",
      "loading data for month 7\n",
      "loading data for month 8\n",
      "loading data for month 9\n",
      "loading data for month 10\n",
      "loading data for month 11\n",
      "loading data for month 12\n"
     ]
    }
   ],
   "source": [
    "word_count = generateWordCount(all_documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68469"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words = [tb(d) for d in all_documents]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getIdf(term, words):\n",
    "    term_counter = word_count[term]\n",
    "    if term_counter == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return math.log10(len(words) / term_counter)\n",
    "\n",
    "def getTfidf(term, review, words):\n",
    "    tf = review.words.count(term)\n",
    "    tfidf = tf * word_idf[term]\n",
    "    return tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on idf: 5000\n",
      "working on idf: 10000\n",
      "working on idf: 15000\n",
      "working on idf: 20000\n",
      "working on idf: 25000\n",
      "working on idf: 30000\n",
      "working on idf: 35000\n",
      "working on idf: 40000\n",
      "working on idf: 45000\n",
      "working on idf: 50000\n",
      "working on idf: 55000\n",
      "working on idf: 60000\n",
      "working on idf: 65000\n"
     ]
    }
   ],
   "source": [
    "word_idf = defaultdict(int)\n",
    "counter = 0\n",
    "for w in word_count.keys():\n",
    "    counter += 1\n",
    "    if counter % 5000 == 0:\n",
    "        print(\"working on idf: \" + str(counter))\n",
    "    word_idf[w] = getIdf(w, all_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateMonthlyTfidf(index):\n",
    "    print(\"generating tf-idf for index: \" + str(index))\n",
    "    words = all_words[index]\n",
    "    tfidf_dict = defaultdict(int)\n",
    "    counter = 0\n",
    "    for w in word_count.keys():\n",
    "        \n",
    "        tfidf_dict[w] = getTfidf(w, words, all_words)\n",
    "    print(\"finish generating tf-idf for index: \" + str(index))\n",
    "    return tfidf_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_apply_result(input_list):\n",
    "    result_list = []    \n",
    "    for i in range(len(input_list)):\n",
    "        result_list.append(input_list[i].get())\n",
    "    return result_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating tf-idf for index: 0\n",
      "generating tf-idf for index: 2\n",
      "generating tf-idf for index: 1\n",
      "generating tf-idf for index: 3\n",
      "generating tf-idf for index: 4\n",
      "finish generating tf-idf for index: 1\n",
      "generating tf-idf for index: 5\n",
      "finish generating tf-idf for index: 2\n",
      "generating tf-idf for index: 6\n",
      "finish generating tf-idf for index: 0\n",
      "generating tf-idf for index: 7\n",
      "finish generating tf-idf for index: 3\n",
      "generating tf-idf for index: 8\n",
      "finish generating tf-idf for index: 4\n",
      "generating tf-idf for index: 9\n",
      "finish generating tf-idf for index: 5\n",
      "generating tf-idf for index: 10\n",
      "finish generating tf-idf for index: 7\n",
      "generating tf-idf for index: 11\n",
      "finish generating tf-idf for index: 6\n",
      "finish generating tf-idf for index: 8\n",
      "finish generating tf-idf for index: 9\n",
      "finish generating tf-idf for index: 10\n",
      "finish generating tf-idf for index: 11\n"
     ]
    }
   ],
   "source": [
    "tf_idf_work_pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()-7)\n",
    "tf_idf_work_result_temp = []\n",
    "\n",
    "for i in range(0,12):\n",
    "    sample_result = tf_idf_work_pool.apply_async(generateMonthlyTfidf, (i, ))\n",
    "    tf_idf_work_result_temp.append(sample_result)\n",
    "\n",
    "tf_idf_work_pool.close()\n",
    "tf_idf_work_pool.join()\n",
    "\n",
    "tf_idf_work_result = transform_apply_result(tf_idf_work_result_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_tfidf_sorted_list = []\n",
    "for i in range(0,12):\n",
    "    word_tfidf_sorted_list.append(sorted(tf_idf_work_result[i].items(), key=lambda kv: -kv[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorted top frequent words of each month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have extracted all of our words following TFIDF method, we will sort the result and print out the top 50 for each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lutetium', 11.672268755754654),\n",
       " ('netsuke', 10.837079843903323),\n",
       " ('cusec', 10.79181246047625),\n",
       " ('bogard', 9.712631214428624),\n",
       " ('inflector', 9.337815004603723),\n",
       " ('amoralist', 9.030899869919436),\n",
       " ('mensal', 8.633449968380999),\n",
       " ('attestation', 8.588182584953923),\n",
       " ('vardy', 8.55966375422008),\n",
       " ('triolet', 7.003361253452793),\n",
       " ('gur', 6.996569250064796),\n",
       " ('lecker', 6.475087476285749),\n",
       " ('tulle', 6.475087476285749),\n",
       " ('strick', 6.463591109097303),\n",
       " ('transvaal', 6.225210003069149),\n",
       " ('tusked', 6.225210003069149),\n",
       " ('bobbin', 5.987102807893162),\n",
       " ('carty', 5.395906230238125),\n",
       " ('herbalism', 5.383913738767463),\n",
       " ('rennet', 4.942746142250878),\n",
       " ('parasitosis', 4.915747326700727),\n",
       " ('antitax', 4.3167249841904995),\n",
       " ('paramagnet', 4.3167249841904995),\n",
       " ('overcount', 4.3167249841904995),\n",
       " ('japonica', 4.3167249841904995),\n",
       " ('quag', 4.214419939295737),\n",
       " ('milkweed', 4.214419939295737),\n",
       " ('varna', 4.182323658827666),\n",
       " ('repealer', 3.8907562519182184),\n",
       " ('borehole', 3.8907562519182184),\n",
       " ('rumney', 3.8907562519182184),\n",
       " ('abac', 3.8907562519182184),\n",
       " ('katydid', 3.8169700377572995),\n",
       " ('kik', 3.80211241711606),\n",
       " ('uncommit', 3.745331296533887),\n",
       " ('ontogeny', 3.612359947967774),\n",
       " ('junt', 3.612359947967774),\n",
       " ('rishi', 3.612359947967774),\n",
       " ('unsilenced', 3.612359947967774),\n",
       " ('miscount', 3.5112480905005192),\n",
       " ('kaffiyeh', 3.339848783037637),\n",
       " ('shipboard', 3.339848783037637),\n",
       " ('accommodative', 3.2375437381428744),\n",
       " ('earthquaking', 3.2375437381428744),\n",
       " ('unie', 3.2375437381428744),\n",
       " ('anticlerical', 3.2375437381428744),\n",
       " ('polyploid', 3.2375437381428744),\n",
       " ('hydatid', 3.2375437381428744),\n",
       " ('zoosperm', 3.2375437381428744),\n",
       " ('bota', 3.2375437381428744)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tfidf_sorted_list[0][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('unfirm', 91.0436962948863),\n",
       " ('unpledged', 10.533744271501558),\n",
       " ('hydrazine', 10.23501985257536),\n",
       " ('anosmic', 8.633449968380999),\n",
       " ('alcalde', 8.633449968380999),\n",
       " ('ruga', 7.554268722333374),\n",
       " ('urticaria', 7.224719895935548),\n",
       " ('poesy', 7.224013592520515),\n",
       " ('ilex', 7.003361253452793),\n",
       " ('moksha', 6.679697566075274),\n",
       " ('bours', 6.622659904607587),\n",
       " ('nonfederal', 6.475087476285749),\n",
       " ('fidicinal', 6.475087476285749),\n",
       " ('micropsia', 6.475087476285749),\n",
       " ('streck', 6.475087476285749),\n",
       " ('superdelegate', 6.04616974230396),\n",
       " ('quadriplegia', 6.020599913279624),\n",
       " ('stereopsis', 5.7254550566359494),\n",
       " ('nudibranch', 5.322957383962485),\n",
       " ('grantee', 4.942746142250878),\n",
       " ('crossbill', 4.3167249841904995),\n",
       " ('bookless', 4.3167249841904995),\n",
       " ('scandent', 4.3167249841904995),\n",
       " ('decapitalize', 4.3167249841904995),\n",
       " ('eyetooth', 4.3167249841904995),\n",
       " ('whitesmith', 4.3167249841904995),\n",
       " ('mense', 4.3167249841904995),\n",
       " ('histidine', 4.214419939295737),\n",
       " ('rennet', 4.182323658827666),\n",
       " ('anosmia', 3.8169700377572995),\n",
       " ('superdeleg', 3.745331296533887),\n",
       " ('ethnocracy', 3.612359947967774),\n",
       " ('hydroscopic', 3.612359947967774),\n",
       " ('waterspout', 3.612359947967774),\n",
       " ('willey', 3.5218251811136247),\n",
       " ('xylitol', 3.373345888424098),\n",
       " ('untasty', 3.339848783037637),\n",
       " ('ubiety', 3.2375437381428744),\n",
       " ('alkene', 3.2375437381428744),\n",
       " ('ivybells', 3.2375437381428744),\n",
       " ('tearage', 3.2375437381428744),\n",
       " ('hyperborean', 3.2375437381428744),\n",
       " ('trioxide', 3.2375437381428744),\n",
       " ('innominate', 3.2375437381428744),\n",
       " ('untellable', 3.2375437381428744),\n",
       " ('sitosterol', 3.2375437381428744),\n",
       " ('elkhorn', 3.2375437381428744),\n",
       " ('clockface', 3.2375437381428744),\n",
       " ('seigneurial', 3.2375437381428744),\n",
       " ('carper', 3.1696426630022625)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "word_tfidf_sorted_list[1][:50]"
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('heiau', 22.87827967046257),\n",
       " ('silane', 18.346081182809623),\n",
       " ('kayles', 12.450420006138298),\n",
       " ('tantrik', 11.870993706523874),\n",
       " ('outstretch', 10.115966254987367),\n",
       " ('bubo', 10.115966254987367),\n",
       " ('circumcenter', 9.712631214428624),\n",
       " ('phonautogram', 7.554268722333374),\n",
       " ('destructivity', 7.554268722333374),\n",
       " ('boatman', 7.003361253452793),\n",
       " ('pelorian', 6.475087476285749),\n",
       " ('anaphora', 6.020599913279624),\n",
       " ('collinear', 6.020599913279624),\n",
       " ('surah', 5.458829030726118),\n",
       " ('aluminothermic', 5.418539921951662),\n",
       " ('reductional', 5.395906230238125),\n",
       " ('croucher', 5.395906230238125),\n",
       " ('amusia', 5.395906230238125),\n",
       " ('divisionist', 4.668907502301861),\n",
       " ('diamondiferous', 4.3167249841904995),\n",
       " ('visitatorial', 4.3167249841904995),\n",
       " ('ocellated', 4.3167249841904995),\n",
       " ('precertification', 4.3167249841904995),\n",
       " ('dedifferentiate', 4.3167249841904995),\n",
       " ('sunniness', 4.3167249841904995),\n",
       " ('sawt', 4.3167249841904995),\n",
       " ('phonautograph', 4.3167249841904995),\n",
       " ('diuresis', 4.3167249841904995),\n",
       " ('causey', 4.294091292476962),\n",
       " ('colitis', 4.27578728657174),\n",
       " ('quatre', 4.214419939295737),\n",
       " ('charism', 3.8907562519182184),\n",
       " ('muscadine', 3.8907562519182184),\n",
       " ('unpledged', 3.745331296533887),\n",
       " ('spitz', 3.745331296533887),\n",
       " ('karyotype', 3.612359947967774),\n",
       " ('gratu', 3.612359947967774),\n",
       " ('sortition', 3.498284625032398),\n",
       " ('revote', 3.498284625032398),\n",
       " ('cretinism', 3.3113299523037933),\n",
       " ('macaw', 3.3113299523037933),\n",
       " ('lovelock', 3.248407151815798),\n",
       " ('organismal', 3.2375437381428744),\n",
       " ('perjurous', 3.2375437381428744),\n",
       " ('superavit', 3.2375437381428744),\n",
       " ('deerhound', 3.2375437381428744),\n",
       " ('tambor', 3.2375437381428744),\n",
       " ('tanh', 3.2375437381428744),\n",
       " ('tilter', 3.2375437381428744),\n",
       " ('spode', 3.2375437381428744)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "word_tfidf_sorted_list[2][:50]"
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mogo', 12.882273877430885),\n",
       " ('strucken', 7.554268722333374),\n",
       " ('spaid', 7.003361253452793),\n",
       " ('scops', 6.475087476285749),\n",
       " ('reflog', 6.020599913279624),\n",
       " ('unintimidated', 5.447058752685505),\n",
       " ('tiza', 5.395906230238125),\n",
       " ('subrogation', 5.248333801916287),\n",
       " ('redback', 4.942746142250878),\n",
       " ('charism', 4.668907502301861),\n",
       " ('kayles', 4.668907502301861),\n",
       " ('bolero', 4.515449934959718),\n",
       " ('crepuscule', 4.3167249841904995),\n",
       " ('fastidiousness', 4.3167249841904995),\n",
       " ('caranna', 4.3167249841904995),\n",
       " ('oxyacetylene', 4.3167249841904995),\n",
       " ('canty', 4.3167249841904995),\n",
       " ('corsage', 4.214419939295737),\n",
       " ('lave', 4.214419939295737),\n",
       " ('diffusely', 3.8907562519182184),\n",
       " ('realest', 3.8169700377572995),\n",
       " ('unpledged', 3.745331296533887),\n",
       " ('tania', 3.697916440169306),\n",
       " ('flagellum', 3.6232233616406977),\n",
       " ('acquirement', 3.612359947967774),\n",
       " ('schlieren', 3.612359947967774),\n",
       " ('alar', 3.339848783037637),\n",
       " ('shab', 3.2375437381428744),\n",
       " ('pedunculate', 3.2375437381428744),\n",
       " ('durbar', 3.2375437381428744),\n",
       " ('matureness', 3.2375437381428744),\n",
       " ('palaeological', 3.2375437381428744),\n",
       " ('photoimpression', 3.2375437381428744),\n",
       " ('virescence', 3.2375437381428744),\n",
       " ('asway', 3.2375437381428744),\n",
       " ('gleet', 3.2375437381428744),\n",
       " ('verjuice', 3.2375437381428744),\n",
       " ('contriteness', 3.2375437381428744),\n",
       " ('lipodystrophy', 3.2375437381428744),\n",
       " ('counterpane', 3.2375437381428744),\n",
       " ('raphides', 3.2375437381428744),\n",
       " ('salpa', 3.2375437381428744),\n",
       " ('enigmatically', 3.2375437381428744),\n",
       " ('horsy', 3.2375437381428744),\n",
       " ('blessedness', 3.2375437381428744),\n",
       " ('craniosacral', 3.2375437381428744),\n",
       " ('chummery', 3.2375437381428744),\n",
       " ('amylose', 3.2375437381428744),\n",
       " ('heptagon', 3.2375437381428744),\n",
       " ('toolmaking', 3.2375437381428744)]"
      ]
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "word_tfidf_sorted_list[3][:50]"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('palfrey', 20.36523892490301),\n",
       " ('ingle', 15.05149978319906),\n",
       " ('plugboard', 9.712631214428624),\n",
       " ('yark', 9.712631214428624),\n",
       " ('percolation', 7.781512503836437),\n",
       " ('huddler', 7.224719895935548),\n",
       " ('gladiatrix', 6.475087476285749),\n",
       " ('glioma', 5.447058752685505),\n",
       " ('stereogram', 5.418539921951662),\n",
       " ('doubleton', 5.395906230238125),\n",
       " ('mnemonist', 5.395906230238125),\n",
       " ('tarpon', 4.942746142250878),\n",
       " ('filly', 4.816479930623699),\n",
       " ('melos', 4.3167249841904995),\n",
       " ('dactylic', 4.3167249841904995),\n",
       " ('unpartitioned', 4.3167249841904995),\n",
       " ('intussusception', 4.3167249841904995),\n",
       " ('dural', 4.3167249841904995),\n",
       " ('explanative', 4.3167249841904995),\n",
       " ('cicerone', 4.3167249841904995),\n",
       " ('laminitis', 4.3167249841904995),\n",
       " ('endocranial', 4.3167249841904995),\n",
       " ('rima', 4.294091292476962),\n",
       " ('mycologist', 4.214419939295737),\n",
       " ('tuath', 4.214419939295737),\n",
       " ('leaven', 4.213497708600623),\n",
       " ('semiannual', 3.8907562519182184),\n",
       " ('antinomy', 3.8907562519182184),\n",
       " ('sialic', 3.8907562519182184),\n",
       " ('dioxane', 3.8907562519182184),\n",
       " ('belton', 3.8907562519182184),\n",
       " ('krome', 3.8907562519182184),\n",
       " ('errorist', 3.8907562519182184),\n",
       " ('anharmonic', 3.8907562519182184),\n",
       " ('directivity', 3.8907562519182184),\n",
       " ('polythene', 3.80211241711606),\n",
       " ('atchison', 3.80211241711606),\n",
       " ('ladybird', 3.80211241711606),\n",
       " ('problematize', 3.612359947967774),\n",
       " ('congruential', 3.612359947967774),\n",
       " ('kob', 3.612359947967774),\n",
       " ('hemmer', 3.612359947967774),\n",
       " ('olecranon', 3.612359947967774),\n",
       " ('tatu', 3.612359947967774),\n",
       " ('merryman', 3.339848783037637),\n",
       " ('kago', 3.339848783037637),\n",
       " ('scrobble', 3.339848783037637),\n",
       " ('eluvium', 3.339848783037637),\n",
       " ('scaleless', 3.2375437381428744),\n",
       " ('dominium', 3.2375437381428744)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "word_tfidf_sorted_list[4][:50]"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('caban', 12.950174952571498),\n",
       " ('caryophyllene', 10.89411750537101),\n",
       " ('citrate', 10.03720176617383),\n",
       " ('brachialis', 9.712631214428624),\n",
       " ('oxyhydrogen', 9.632959861247398),\n",
       " ('callipygian', 9.50528104279015),\n",
       " ('squirrelfish', 9.156745470895425),\n",
       " ('borsch', 7.224719895935548),\n",
       " ('knighthood', 7.0224961810010385),\n",
       " ('misleader', 6.475087476285749),\n",
       " ('designable', 6.475087476285749),\n",
       " ('trunnion', 6.475087476285749),\n",
       " ('coker', 6.225210003069149),\n",
       " ('sloop', 6.0833798673856965),\n",
       " ('geoduck', 5.852080150834198),\n",
       " ('kinase', 5.447058752685505),\n",
       " ('nostic', 5.447058752685505),\n",
       " ('paraboloid', 5.447058752685505),\n",
       " ('peiser', 5.418539921951662),\n",
       " ('wealthiness', 5.395906230238125),\n",
       " ('lunes', 5.395906230238125),\n",
       " ('thujone', 5.322957383962485),\n",
       " ('arrestor', 5.248333801916287),\n",
       " ('banky', 5.248333801916287),\n",
       " ('lory', 5.248333801916287),\n",
       " ('boric', 4.771212547196624),\n",
       " ('hadrosaur', 4.668907502301861),\n",
       " ('haunter', 4.668907502301861),\n",
       " ('tillage', 4.562534900539273),\n",
       " ('phosphocreatine', 4.3167249841904995),\n",
       " ('switchel', 4.3167249841904995),\n",
       " ('tripos', 4.3167249841904995),\n",
       " ('callipygous', 4.3167249841904995),\n",
       " ('everydayness', 4.3167249841904995),\n",
       " ('unliftable', 4.3167249841904995),\n",
       " ('preambulatory', 4.214419939295737),\n",
       " ('hadden', 4.214419939295737),\n",
       " ('uracil', 4.214419939295737),\n",
       " ('chrysalis', 4.214419939295737),\n",
       " ('rennet', 4.182323658827666),\n",
       " ('wordle', 4.182323658827666),\n",
       " ('fortis', 4.182323658827666),\n",
       " ('tala', 3.9133899436317554),\n",
       " ('protuberance', 3.8907562519182184),\n",
       " ('alexandrite', 3.8907562519182184),\n",
       " ('minot', 3.697916440169306),\n",
       " ('trekk', 3.612359947967774),\n",
       " ('prefatory', 3.612359947967774),\n",
       " ('moissanite', 3.421901175404454),\n",
       " ('capybara', 3.339848783037637)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "word_tfidf_sorted_list[5][:50]"
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('awning', 14.279075568035443),\n",
       " ('glub', 9.363328241334717),\n",
       " ('opalesque', 8.633449968380999),\n",
       " ('jazzer', 8.633449968380999),\n",
       " ('aspartate', 8.428839878591473),\n",
       " ('concentrator', 8.426995417201246),\n",
       " ('coprolalia', 7.633940075514599),\n",
       " ('monkish', 6.679697566075274),\n",
       " ('phosphorescence', 6.622659904607587),\n",
       " ('boxwood', 6.475087476285749),\n",
       " ('insole', 6.475087476285749),\n",
       " ('paramorphism', 6.475087476285749),\n",
       " ('bouldering', 6.321629908943605),\n",
       " ('blatter', 6.225210003069149),\n",
       " ('waterflood', 6.225210003069149),\n",
       " ('pectus', 6.202576311355612),\n",
       " ('endometrial', 6.020599913279624),\n",
       " ('plasmodium', 5.418539921951662),\n",
       " ('solvation', 5.395906230238125),\n",
       " ('jigginess', 5.395906230238125),\n",
       " ('implead', 5.395906230238125),\n",
       " ('selenide', 5.395906230238125),\n",
       " ('unbraced', 5.395906230238125),\n",
       " ('riddler', 5.328187085405365),\n",
       " ('eucharistic', 5.248333801916287),\n",
       " ('committeeman', 4.816479930623699),\n",
       " ('conceptus', 4.771212547196624),\n",
       " ('dendritic', 4.771212547196624),\n",
       " ('methylene', 4.771212547196624),\n",
       " ('epigenetic', 4.754463994503394),\n",
       " ('chined', 4.668907502301861),\n",
       " ('pilonidal', 4.562534900539273),\n",
       " ('hyperpigmentation', 4.3167249841904995),\n",
       " ('icebound', 4.3167249841904995),\n",
       " ('pitchstone', 4.3167249841904995),\n",
       " ('percher', 4.3167249841904995),\n",
       " ('syzygium', 4.3167249841904995),\n",
       " ('girdling', 4.3167249841904995),\n",
       " ('petrosa', 4.3167249841904995),\n",
       " ('killdeer', 4.3167249841904995),\n",
       " ('batfish', 4.3167249841904995),\n",
       " ('unmended', 4.3167249841904995),\n",
       " ('overspeculation', 4.294091292476962),\n",
       " ('skidder', 4.214419939295737),\n",
       " ('loach', 4.214419939295737),\n",
       " ('asherah', 4.214419939295737),\n",
       " ('croydon', 4.213497708600623),\n",
       " ('arginine', 4.182323658827666),\n",
       " ('grantee', 4.182323658827666),\n",
       " ('hedonics', 4.182323658827666)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "word_tfidf_sorted_list[6][:50]"
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  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('perchlorate', 24.68445964444646),\n",
       " ('saddleback', 15.968872151887453),\n",
       " ('rakan', 15.108537444666748),\n",
       " ('associational', 15.108537444666748),\n",
       " ('baku', 10.64591476792497),\n",
       " ('antireligion', 10.536049848239342),\n",
       " ('spitz', 10.065577859434821),\n",
       " ('cockily', 9.712631214428624),\n",
       " ('leal', 7.826779887263511),\n",
       " ('perchlor', 7.781512503836437),\n",
       " ('bentonite', 7.633940075514599),\n",
       " ('anode', 7.60422483423212),\n",
       " ('beal', 7.60422483423212),\n",
       " ('wawah', 7.554268722333374),\n",
       " ('babby', 7.3687693734329525),\n",
       " ('nothingism', 7.224719895935548),\n",
       " ('easement', 6.5153765850602054),\n",
       " ('coatee', 6.475087476285749),\n",
       " ('pandan', 6.225210003069149),\n",
       " ('winglet', 6.225210003069149),\n",
       " ('mogo', 6.202576311355612),\n",
       " ('steerable', 6.020599913279624),\n",
       " ('awakener', 5.447058752685505),\n",
       " ('perfective', 5.447058752685505),\n",
       " ('doser', 5.395906230238125),\n",
       " ('nulliparous', 5.395906230238125),\n",
       " ('binaural', 5.122488200940297),\n",
       " ('vitiligo', 4.816479930623699),\n",
       " ('fagging', 4.816479930623699),\n",
       " ('northbound', 4.754463994503394),\n",
       " ('aerodrome', 4.668907502301861),\n",
       " ('slackness', 4.668907502301861),\n",
       " ('curlew', 4.668907502301861),\n",
       " ('plosive', 4.668907502301861),\n",
       " ('shuttlecock', 4.515449934959718),\n",
       " ('unbudging', 4.3167249841904995),\n",
       " ('adsorbent', 4.3167249841904995),\n",
       " ('vespertine', 4.3167249841904995),\n",
       " ('roentgen', 4.3167249841904995),\n",
       " ('haku', 4.3167249841904995),\n",
       " ('rebounder', 4.3167249841904995),\n",
       " ('unhittable', 4.3167249841904995),\n",
       " ('limbus', 4.3167249841904995),\n",
       " ('controllably', 4.3167249841904995),\n",
       " ('unstamped', 4.3167249841904995),\n",
       " ('unpretenti', 4.3167249841904995),\n",
       " ('voile', 4.3167249841904995),\n",
       " ('broomrape', 4.294091292476962),\n",
       " ('kukri', 4.294091292476962),\n",
       " ('gude', 4.214419939295737)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "word_tfidf_sorted_list[7][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('diverticulosis', 32.682352516113035),\n",
       " ('palisading', 16.341176258056517),\n",
       " ('actinic', 16.341176258056517),\n",
       " ('focally', 16.341176258056517),\n",
       " ('dermatopathology', 16.341176258056517),\n",
       " ('okapi', 15.968872151887453),\n",
       " ('borosilicate', 15.05149978319906),\n",
       " ('undelayed', 13.847379800543136),\n",
       " ('reviewable', 12.041199826559248),\n",
       " ('colley', 11.870993706523874),\n",
       " ('fortis', 11.026126009636576),\n",
       " ('adenomatous', 10.973788858552236),\n",
       " ('entopic', 10.79181246047625),\n",
       " ('chicane', 10.019546349112911),\n",
       " ('dorsum', 10.019546349112911),\n",
       " ('dermis', 9.831494653401453),\n",
       " ('chrom', 9.14483173523474),\n",
       " ('squirrelfish', 8.980654211839743),\n",
       " ('homopolar', 8.633449968380999),\n",
       " ('pinax', 8.588182584953923),\n",
       " ('squamous', 7.5719241393942935),\n",
       " ('detainable', 7.554268722333374),\n",
       " ('noncompli', 7.156818820794936),\n",
       " ('euphonious', 7.003361253452793),\n",
       " ('precancerous', 6.923689900271568),\n",
       " ('babby', 6.915306642760155),\n",
       " ('resection', 6.622659904607587),\n",
       " ('conservatorship', 6.622659904607587),\n",
       " ('melamine', 6.621753040239896),\n",
       " ('hemm', 6.475087476285749),\n",
       " ('thiocyanide', 6.475087476285749),\n",
       " ('coltsfoot', 6.475087476285749),\n",
       " ('steamship', 6.463591109097303),\n",
       " ('matsu', 6.463591109097303),\n",
       " ('neuroma', 6.321629908943605),\n",
       " ('carlie', 6.225210003069149),\n",
       " ('piratery', 6.020599913279624),\n",
       " ('escapement', 6.020599913279624),\n",
       " ('beshear', 5.70316862567409),\n",
       " ('zapatero', 5.7010497154289865),\n",
       " ('polyp', 5.61799694480083),\n",
       " ('centum', 5.418539921951662),\n",
       " ('scoter', 5.395906230238125),\n",
       " ('ontologic', 5.395906230238125),\n",
       " ('hoptoad', 5.395906230238125),\n",
       " ('oread', 5.395906230238125),\n",
       " ('unreadiness', 5.395906230238125),\n",
       " ('muslin', 5.384324731238488),\n",
       " ('dovecot', 5.322957383962485),\n",
       " ('dermal', 5.149830532734095)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tfidf_sorted_list[8][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('diverticulosis', 65.36470503222607),\n",
       " ('palisading', 32.682352516113035),\n",
       " ('actinic', 32.682352516113035),\n",
       " ('focally', 32.682352516113035),\n",
       " ('dermatopathology', 32.682352516113035),\n",
       " ('jiff', 25.046903045570367),\n",
       " ('stingo', 20.47003970515072),\n",
       " ('adenomatous', 20.039092698225822),\n",
       " ('dorsum', 20.039092698225822),\n",
       " ('dermis', 19.662989306802906),\n",
       " ('squamous', 15.496030796899948),\n",
       " ('delusory', 14.029356198619123),\n",
       " ('resection', 13.847379800543136),\n",
       " ('melamine', 13.368444817088092),\n",
       " ('theriaca', 12.950174952571498),\n",
       " ('precancerous', 12.64325981788721),\n",
       " ('neuroma', 12.64325981788721),\n",
       " ('awee', 12.405152622711224),\n",
       " ('polyp', 9.831494653401453),\n",
       " ('dermal', 9.831494653401453),\n",
       " ('freath', 9.712631214428624),\n",
       " ('folksiness', 9.632959861247398),\n",
       " ('muslin', 9.185024541524479),\n",
       " ('epithelial', 7.924106657505655),\n",
       " ('misogynic', 7.826779887263511),\n",
       " ('coble', 7.826779887263511),\n",
       " ('malm', 7.554268722333374),\n",
       " ('coistril', 7.554268722333374),\n",
       " ('tuath', 7.224719895935548),\n",
       " ('nidal', 7.224719895935548),\n",
       " ('exteriorization', 7.156818820794936),\n",
       " ('krona', 7.1215079866730955),\n",
       " ('carcinoma', 7.047130898238609),\n",
       " ('folksi', 6.843802350808908),\n",
       " ('belvedere', 6.78841297496767),\n",
       " ('cyanuric', 6.622659904607587),\n",
       " ('parah', 6.622659904607587),\n",
       " ('interbank', 6.5153765850602054),\n",
       " ('harn', 6.475087476285749),\n",
       " ('saponin', 6.475087476285749),\n",
       " ('compossible', 6.475087476285749),\n",
       " ('babby', 6.386266790308558),\n",
       " ('airedale', 6.225210003069149),\n",
       " ('glazier', 6.0833798673856965),\n",
       " ('feticide', 6.020599913279624),\n",
       " ('kronur', 6.020599913279624),\n",
       " ('doggon', 5.7254550566359494),\n",
       " ('reformism', 5.719569917615643),\n",
       " ('internode', 5.70316862567409),\n",
       " ('overlying', 5.622243147373497)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tfidf_sorted_list[9][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('awee', 128.34561751958918),\n",
       " ('keratinization', 28.627275283179745),\n",
       " ('melamine', 28.111215736867482),\n",
       " ('alanyl', 20.504443674904874),\n",
       " ('valyl', 16.187718690714373),\n",
       " ('leucyl', 15.108537444666748),\n",
       " ('telestial', 15.108537444666748),\n",
       " ('nonya', 12.950174952571498),\n",
       " ('narwhal', 12.243996187613392),\n",
       " ('syncope', 10.79181246047625),\n",
       " ('podesta', 10.115966254987367),\n",
       " ('circumciser', 10.019546349112911),\n",
       " ('aesthetical', 9.632959861247398),\n",
       " ('narwh', 9.50528104279015),\n",
       " ('undrained', 9.337815004603723),\n",
       " ('passageway', 9.030899869919436),\n",
       " ('prolyl', 8.633449968380999),\n",
       " ('glycolaldehyde', 8.633449968380999),\n",
       " ('melamin', 8.588182584953923),\n",
       " ('reformism', 8.428839878591473),\n",
       " ('monogamic', 7.781512503836437),\n",
       " ('mia', 7.781512503836437),\n",
       " ('hypospadias', 7.554268722333374),\n",
       " ('providentialism', 7.554268722333374),\n",
       " ('burao', 7.554268722333374),\n",
       " ('noddy', 7.224719895935548),\n",
       " ('gerontocracy', 7.003361253452793),\n",
       " ('bassoon', 6.843802350808908),\n",
       " ('madrasah', 6.78841297496767),\n",
       " ('babby', 6.612998155644957),\n",
       " ('tomogram', 6.475087476285749),\n",
       " ('chloroquine', 6.475087476285749),\n",
       " ('butyrate', 6.475087476285749),\n",
       " ('glycyl', 6.475087476285749),\n",
       " ('kiva', 6.2553184377623605),\n",
       " ('guttersnipe', 6.225210003069149),\n",
       " ('voidable', 6.225210003069149),\n",
       " ('standish', 6.225210003069149),\n",
       " ('flutist', 6.225210003069149),\n",
       " ('scena', 6.202576311355612),\n",
       " ('manticore', 6.202576311355612),\n",
       " ('callus', 6.086163356867567),\n",
       " ('eliminative', 6.0833798673856965),\n",
       " ('constitutor', 6.020599913279624),\n",
       " ('astigmatism', 5.61799694480083),\n",
       " ('vagus', 5.458829030726118),\n",
       " ('aspartyl', 5.447058752685505),\n",
       " ('carse', 5.447058752685505),\n",
       " ('tiddi', 5.447058752685505),\n",
       " ('tablespoonful', 5.418539921951662)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tfidf_sorted_list[10][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('prorogue', 93.88876840614337),\n",
       " ('tharm', 19.42526242885725),\n",
       " ('narwhal', 17.11660691533709),\n",
       " ('saturnalia', 14.449439791871097),\n",
       " ('dartos', 13.847379800543136),\n",
       " ('eutectic', 13.847379800543136),\n",
       " ('rickey', 13.83651638687021),\n",
       " ('backwardation', 13.83651638687021),\n",
       " ('cummingtonite', 12.950174952571498),\n",
       " ('prorogation', 12.64325981788721),\n",
       " ('nonmoral', 11.870993706523874),\n",
       " ('parhelic', 11.870993706523874),\n",
       " ('fawny', 10.115966254987367),\n",
       " ('mulk', 9.712631214428624),\n",
       " ('graupel', 9.712631214428624),\n",
       " ('diaconate', 9.712631214428624),\n",
       " ('snozzle', 9.712631214428624),\n",
       " ('shaitan', 9.337815004603723),\n",
       " ('keratinization', 9.065303839673586),\n",
       " ('learnedness', 8.633449968380999),\n",
       " ('melamine', 8.620772825972695),\n",
       " ('scrapy', 8.55966375422008),\n",
       " ('buckie', 8.428839878591473),\n",
       " ('narwh', 7.9844360759437265),\n",
       " ('supernature', 7.633940075514599),\n",
       " ('greensand', 7.554268722333374),\n",
       " ('yair', 7.554268722333374),\n",
       " ('saturnalian', 7.554268722333374),\n",
       " ('superordinate', 7.554268722333374),\n",
       " ('conky', 7.52574989159953),\n",
       " ('eggnog', 7.395832880338612),\n",
       " ('noncontradiction', 7.003361253452793),\n",
       " ('glioma', 7.003361253452793),\n",
       " ('solstic', 6.867559103171568),\n",
       " ('underconsumption', 6.843802350808908),\n",
       " ('meddler', 6.679697566075274),\n",
       " ('metrification', 6.679697566075274),\n",
       " ('melamin', 6.679697566075274),\n",
       " ('gumma', 6.475087476285749),\n",
       " ('hypogeum', 6.475087476285749),\n",
       " ('alizarin', 6.475087476285749),\n",
       " ('mutism', 6.225210003069149),\n",
       " ('moissanite', 6.0833798673856965),\n",
       " ('cancan', 6.020599913279624),\n",
       " ('parlamento', 6.020599913279624),\n",
       " ('physicalism', 5.987102807893162),\n",
       " ('aborigine', 5.780230961476612),\n",
       " ('deceivingly', 5.7254550566359494),\n",
       " ('corpuscular', 5.447058752685505),\n",
       " ('thermography', 5.418539921951662)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tfidf_sorted_list[11][:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting TFIDF for Negative Comments "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will repeat the process for negative comments.\n",
    "\n",
    "However, due to the size of the data set and the analysis required, the TFIDF method takes a really long time to rerun. Because we ran the method on a separate file to save time. We will manually save the results here instead of rerunning the program for negative comments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below is the same as the one above except we have changed the scoring comparison to < 0 in order to only look at negative comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseBzData(f):\n",
    "    source_file = bz2.BZ2File(f, \"r\")\n",
    "    comment_body = []\n",
    "    for line in source_file:\n",
    "        json_line = json.loads(line.decode())\n",
    "        # Drop user name of comment author\n",
    "        if \"author\" in json_line:\n",
    "            del json_line[\"author\"]\n",
    "        if \"body\" in json_line and json_line[\"score\"] < 0:\n",
    "            comment_body.append(json_line[\"body\"])\n",
    "    return comment_body"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the above change, we ran the same functions and retrieved the top 50 frequent words for negative comments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing and Analyzing the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepping the output data from each month\n",
    "\n",
    "First, let us quantify our outputs into variables for convenience. This is mostly done due to the size of our data set and the processing power required to run the given data. It will be too time (and resource!) intensive to rerun our data so we will simply save the output directly into variables for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan_pos_2008 = [('lutetium', 11.672268755754654), ('netsuke', 10.837079843903323), ('cusec', 10.79181246047625), ('bogard', 9.712631214428624), ('inflector', 9.337815004603723), ('amoralist', 9.030899869919436), ('mensal', 8.633449968380999), ('attestation', 8.588182584953923), ('vardy', 8.55966375422008), ('triolet', 7.003361253452793), ('gur', 6.996569250064796),\n",
    " ('lecker', 6.475087476285749), ('tulle', 6.475087476285749), ('strick', 6.463591109097303), ('transvaal', 6.225210003069149), ('tusked', 6.225210003069149), ('bobbin', 5.987102807893162), ('carty', 5.395906230238125), ('herbalism', 5.383913738767463), ('rennet', 4.942746142250878), ('parasitosis', 4.915747326700727), ('antitax', 4.3167249841904995), ('paramagnet', 4.3167249841904995), ('overcount', 4.3167249841904995), ('japonica', 4.3167249841904995), ('quag', 4.214419939295737), ('milkweed', 4.214419939295737), ('varna', 4.182323658827666), ('repealer', 3.8907562519182184), ('borehole', 3.8907562519182184), ('rumney', 3.8907562519182184), ('abac', 3.8907562519182184), ('katydid', 3.8169700377572995), ('kik', 3.80211241711606),\n",
    " ('uncommit', 3.745331296533887), ('ontogeny', 3.612359947967774), ('junt', 3.612359947967774), ('rishi', 3.612359947967774), ('unsilenced', 3.612359947967774), ('miscount', 3.5112480905005192), ('kaffiyeh', 3.339848783037637), ('shipboard', 3.339848783037637), ('accommodative', 3.2375437381428744), ('earthquaking', 3.2375437381428744), ('unie', 3.2375437381428744), ('anticlerical', 3.2375437381428744), ('polyploid', 3.2375437381428744), ('hydatid', 3.2375437381428744), ('zoosperm', 3.2375437381428744), ('bota', 3.2375437381428744)] \n",
    "\n",
    "feb_pos_2008 = [('unfirm', 91.0436962948863), ('unpledged', 10.533744271501558), ('hydrazine', 10.23501985257536), ('anosmic', 8.633449968380999), ('alcalde', 8.633449968380999), ('ruga', 7.554268722333374),\n",
    " ('urticaria', 7.224719895935548), ('poesy', 7.224013592520515), ('ilex', 7.003361253452793),\n",
    " ('moksha', 6.679697566075274), ('bours', 6.622659904607587), ('nonfederal', 6.475087476285749), ('fidicinal', 6.475087476285749), ('micropsia', 6.475087476285749), ('streck', 6.475087476285749), ('superdelegate', 6.04616974230396), ('quadriplegia', 6.020599913279624), ('stereopsis', 5.7254550566359494), ('nudibranch', 5.322957383962485), ('grantee', 4.942746142250878), ('crossbill', 4.3167249841904995), ('bookless', 4.3167249841904995), ('scandent', 4.3167249841904995), ('decapitalize', 4.3167249841904995), ('eyetooth', 4.3167249841904995), ('whitesmith', 4.3167249841904995), ('mense', 4.3167249841904995), ('histidine', 4.214419939295737), ('rennet', 4.182323658827666), ('anosmia', 3.8169700377572995), ('superdeleg', 3.745331296533887), ('ethnocracy', 3.612359947967774), ('hydroscopic', 3.612359947967774), ('waterspout', 3.612359947967774), ('willey', 3.5218251811136247), ('xylitol', 3.373345888424098), ('untasty', 3.339848783037637), ('ubiety', 3.2375437381428744), ('alkene', 3.2375437381428744), ('ivybells', 3.2375437381428744), ('tearage', 3.2375437381428744), ('hyperborean', 3.2375437381428744), ('trioxide', 3.2375437381428744), ('innominate', 3.2375437381428744), ('untellable', 3.2375437381428744), ('sitosterol', 3.2375437381428744), ('elkhorn', 3.2375437381428744), ('clockface', 3.2375437381428744), ('seigneurial', 3.2375437381428744), ('carper', 3.1696426630022625)] \n",
    "\n",
    "mar_pos_2008 = [('heiau', 22.87827967046257), ('silane', 18.346081182809623), ('kayles', 12.450420006138298), ('tantrik', 11.870993706523874), ('outstretch', 10.115966254987367), ('bubo', 10.115966254987367), ('circumcenter', 9.712631214428624), ('phonautogram', 7.554268722333374), ('destructivity', 7.554268722333374), ('boatman', 7.003361253452793), ('pelorian', 6.475087476285749), ('anaphora', 6.020599913279624), ('collinear', 6.020599913279624), ('surah', 5.458829030726118), ('aluminothermic', 5.418539921951662), ('reductional', 5.395906230238125), ('croucher', 5.395906230238125), ('amusia', 5.395906230238125), ('divisionist', 4.668907502301861), ('diamondiferous', 4.3167249841904995), ('visitatorial', 4.3167249841904995), ('ocellated', 4.3167249841904995), ('precertification', 4.3167249841904995), ('dedifferentiate', 4.3167249841904995), ('sunniness', 4.3167249841904995), ('sawt', 4.3167249841904995), ('phonautograph', 4.3167249841904995), ('diuresis', 4.3167249841904995), ('causey', 4.294091292476962), ('colitis', 4.27578728657174), ('quatre', 4.214419939295737), ('charism', 3.8907562519182184), ('muscadine', 3.8907562519182184), ('unpledged', 3.745331296533887), ('spitz', 3.745331296533887), ('karyotype', 3.612359947967774), ('gratu', 3.612359947967774), ('sortition', 3.498284625032398), ('revote', 3.498284625032398), ('cretinism', 3.3113299523037933), ('macaw', 3.3113299523037933), ('lovelock', 3.248407151815798), ('organismal', 3.2375437381428744), ('perjurous', 3.2375437381428744), ('superavit', 3.2375437381428744), ('deerhound', 3.2375437381428744), ('tambor', 3.2375437381428744), ('tanh', 3.2375437381428744), ('tilter', 3.2375437381428744), ('spode', 3.2375437381428744)] \n",
    "\n",
    "apr_pos_2008 = [('mogo', 12.882273877430885), ('strucken', 7.554268722333374), ('spaid', 7.003361253452793), ('scops', 6.475087476285749), ('reflog', 6.020599913279624), ('unintimidated', 5.447058752685505), ('tiza', 5.395906230238125),\n",
    " ('subrogation', 5.248333801916287), ('redback', 4.942746142250878), ('charism', 4.668907502301861), ('kayles', 4.668907502301861), ('bolero', 4.515449934959718), ('crepuscule', 4.3167249841904995), ('fastidiousness', 4.3167249841904995), ('caranna', 4.3167249841904995), ('oxyacetylene', 4.3167249841904995), ('canty', 4.3167249841904995), ('corsage', 4.214419939295737), ('lave', 4.214419939295737),\n",
    " ('diffusely', 3.8907562519182184), ('realest', 3.8169700377572995), ('unpledged', 3.745331296533887), ('tania', 3.697916440169306), ('flagellum', 3.6232233616406977), ('acquirement', 3.612359947967774), ('schlieren', 3.612359947967774), ('alar', 3.339848783037637),\n",
    " ('shab', 3.2375437381428744), ('pedunculate', 3.2375437381428744), ('durbar', 3.2375437381428744), ('matureness', 3.2375437381428744), ('palaeological', 3.2375437381428744), ('photoimpression', 3.2375437381428744), ('virescence', 3.2375437381428744), ('asway', 3.2375437381428744), ('gleet', 3.2375437381428744), ('verjuice', 3.2375437381428744), ('contriteness', 3.2375437381428744), ('lipodystrophy', 3.2375437381428744), ('counterpane', 3.2375437381428744), ('raphides', 3.2375437381428744), ('salpa', 3.2375437381428744), ('enigmatically', 3.2375437381428744), ('horsy', 3.2375437381428744), ('blessedness', 3.2375437381428744), ('craniosacral', 3.2375437381428744), ('chummery', 3.2375437381428744), ('amylose', 3.2375437381428744), ('heptagon', 3.2375437381428744), ('toolmaking', 3.2375437381428744)] \n",
    "\n",
    "may_pos_2008 = [('palfrey', 20.36523892490301), ('ingle', 15.05149978319906), ('plugboard', 9.712631214428624), ('yark', 9.712631214428624),\n",
    " ('percolation', 7.781512503836437), ('huddler', 7.224719895935548), ('gladiatrix', 6.475087476285749), ('glioma', 5.447058752685505), ('stereogram', 5.418539921951662), ('doubleton', 5.395906230238125), ('mnemonist', 5.395906230238125), ('tarpon', 4.942746142250878), ('filly', 4.816479930623699), ('melos', 4.3167249841904995), ('dactylic', 4.3167249841904995), ('unpartitioned', 4.3167249841904995), ('intussusception', 4.3167249841904995), ('dural', 4.3167249841904995), ('explanative', 4.3167249841904995), ('cicerone', 4.3167249841904995), ('laminitis', 4.3167249841904995), ('endocranial', 4.3167249841904995), ('rima', 4.294091292476962),\n",
    " ('mycologist', 4.214419939295737), ('tuath', 4.214419939295737), ('leaven', 4.213497708600623), ('semiannual', 3.8907562519182184), ('antinomy', 3.8907562519182184), ('sialic', 3.8907562519182184), ('dioxane', 3.8907562519182184), ('belton', 3.8907562519182184), ('krome', 3.8907562519182184), ('errorist', 3.8907562519182184), ('anharmonic', 3.8907562519182184), ('directivity', 3.8907562519182184), ('polythene', 3.80211241711606), ('atchison', 3.80211241711606), ('ladybird', 3.80211241711606), ('problematize', 3.612359947967774), ('congruential', 3.612359947967774), ('kob', 3.612359947967774),\n",
    " ('hemmer', 3.612359947967774), ('olecranon', 3.612359947967774), ('tatu', 3.612359947967774),\n",
    " ('merryman', 3.339848783037637), ('kago', 3.339848783037637),\n",
    " ('scrobble', 3.339848783037637), ('eluvium', 3.339848783037637), ('scaleless', 3.2375437381428744), ('dominium', 3.2375437381428744)] \n",
    "\n",
    "jun_pos_2008 = [('caban', 12.950174952571498), ('caryophyllene', 10.89411750537101), ('citrate', 10.03720176617383), ('brachialis', 9.712631214428624), ('oxyhydrogen', 9.632959861247398), ('callipygian', 9.50528104279015), ('squirrelfish', 9.156745470895425), ('borsch', 7.224719895935548), ('knighthood', 7.0224961810010385), ('misleader', 6.475087476285749), ('designable', 6.475087476285749), ('trunnion', 6.475087476285749), ('coker', 6.225210003069149), ('sloop', 6.0833798673856965), ('geoduck', 5.852080150834198), ('kinase', 5.447058752685505), ('nostic', 5.447058752685505), ('paraboloid', 5.447058752685505), ('peiser', 5.418539921951662), ('wealthiness', 5.395906230238125), ('lunes', 5.395906230238125), ('thujone', 5.322957383962485), ('arrestor', 5.248333801916287), ('banky', 5.248333801916287), ('lory', 5.248333801916287),\n",
    " ('boric', 4.771212547196624), ('hadrosaur', 4.668907502301861), ('haunter', 4.668907502301861), ('tillage', 4.562534900539273), ('phosphocreatine', 4.3167249841904995), ('switchel', 4.3167249841904995), ('tripos', 4.3167249841904995), ('callipygous', 4.3167249841904995), ('everydayness', 4.3167249841904995), ('unliftable', 4.3167249841904995), ('preambulatory', 4.214419939295737), ('hadden', 4.214419939295737), ('uracil', 4.214419939295737), ('chrysalis', 4.214419939295737), ('rennet', 4.182323658827666), ('wordle', 4.182323658827666), ('fortis', 4.182323658827666), ('tala', 3.9133899436317554), ('protuberance', 3.8907562519182184), ('alexandrite', 3.8907562519182184), ('minot', 3.697916440169306), ('trekk', 3.612359947967774), ('prefatory', 3.612359947967774), ('moissanite', 3.421901175404454), ('capybara', 3.339848783037637)] \n",
    "\n",
    "jul_pos_2008 = [('awning', 14.279075568035443), ('glub', 9.363328241334717),\n",
    " ('opalesque', 8.633449968380999), ('jazzer', 8.633449968380999), ('aspartate', 8.428839878591473), ('concentrator', 8.426995417201246), ('coprolalia', 7.633940075514599), ('monkish', 6.679697566075274), ('phosphorescence', 6.622659904607587), ('boxwood', 6.475087476285749), ('insole', 6.475087476285749), ('paramorphism', 6.475087476285749), ('bouldering', 6.321629908943605), ('blatter', 6.225210003069149), ('waterflood', 6.225210003069149), ('pectus', 6.202576311355612), ('endometrial', 6.020599913279624), ('plasmodium', 5.418539921951662), ('solvation', 5.395906230238125), ('jigginess', 5.395906230238125), ('implead', 5.395906230238125), ('selenide', 5.395906230238125), ('unbraced', 5.395906230238125), ('riddler', 5.328187085405365), ('eucharistic', 5.248333801916287), ('committeeman', 4.816479930623699), ('conceptus', 4.771212547196624), ('dendritic', 4.771212547196624), ('methylene', 4.771212547196624), ('epigenetic', 4.754463994503394), ('chined', 4.668907502301861), ('pilonidal', 4.562534900539273), ('hyperpigmentation', 4.3167249841904995), ('icebound', 4.3167249841904995), ('pitchstone', 4.3167249841904995), ('percher', 4.3167249841904995), ('syzygium', 4.3167249841904995), ('girdling', 4.3167249841904995), ('petrosa', 4.3167249841904995), ('killdeer', 4.3167249841904995), ('batfish', 4.3167249841904995), ('unmended', 4.3167249841904995), ('overspeculation', 4.294091292476962), ('skidder', 4.214419939295737), ('loach', 4.214419939295737), ('asherah', 4.214419939295737), ('croydon', 4.213497708600623), ('arginine', 4.182323658827666), ('grantee', 4.182323658827666), ('hedonics', 4.182323658827666)] \n",
    "\n",
    "aug_pos_2008 = [('perchlorate', 24.68445964444646), ('saddleback', 15.968872151887453), ('rakan', 15.108537444666748), ('associational', 15.108537444666748), ('baku', 10.64591476792497), ('antireligion', 10.536049848239342), ('spitz', 10.065577859434821), ('cockily', 9.712631214428624), ('leal', 7.826779887263511), ('perchlor', 7.781512503836437), ('bentonite', 7.633940075514599), ('anode', 7.60422483423212), ('beal', 7.60422483423212),\n",
    " ('wawah', 7.554268722333374), ('babby', 7.3687693734329525), ('nothingism', 7.224719895935548), ('easement', 6.5153765850602054), ('coatee', 6.475087476285749), ('pandan', 6.225210003069149), ('winglet', 6.225210003069149), ('mogo', 6.202576311355612), ('steerable', 6.020599913279624), ('awakener', 5.447058752685505), ('perfective', 5.447058752685505), ('doser', 5.395906230238125), ('nulliparous', 5.395906230238125), ('binaural', 5.122488200940297), ('vitiligo', 4.816479930623699), ('fagging', 4.816479930623699), ('northbound', 4.754463994503394), ('aerodrome', 4.668907502301861), ('slackness', 4.668907502301861), ('curlew', 4.668907502301861), ('plosive', 4.668907502301861), ('shuttlecock', 4.515449934959718), ('unbudging', 4.3167249841904995), ('adsorbent', 4.3167249841904995), ('vespertine', 4.3167249841904995), ('roentgen', 4.3167249841904995), ('haku', 4.3167249841904995), ('rebounder', 4.3167249841904995), ('unhittable', 4.3167249841904995), ('limbus', 4.3167249841904995), ('controllably', 4.3167249841904995), ('unstamped', 4.3167249841904995), ('unpretenti', 4.3167249841904995), ('voile', 4.3167249841904995), ('broomrape', 4.294091292476962), ('kukri', 4.294091292476962), ('gude', 4.214419939295737)] \n",
    "\n",
    "sep_pos_2008 = [('diverticulosis', 32.682352516113035), ('palisading', 16.341176258056517), ('actinic', 16.341176258056517), ('focally', 16.341176258056517), ('dermatopathology', 16.341176258056517), ('okapi', 15.968872151887453), ('borosilicate', 15.05149978319906), ('undelayed', 13.847379800543136), ('reviewable', 12.041199826559248), ('colley', 11.870993706523874), ('fortis', 11.026126009636576), ('adenomatous', 10.973788858552236), ('entopic', 10.79181246047625), ('chicane', 10.019546349112911), ('dorsum', 10.019546349112911), ('dermis', 9.831494653401453), ('chrom', 9.14483173523474),\n",
    " ('squirrelfish', 8.980654211839743), ('homopolar', 8.633449968380999), ('pinax', 8.588182584953923), ('squamous', 7.5719241393942935), ('detainable', 7.554268722333374), ('noncompli', 7.156818820794936), ('euphonious', 7.003361253452793), ('precancerous', 6.923689900271568), ('babby', 6.915306642760155), ('resection', 6.622659904607587), ('conservatorship', 6.622659904607587), ('melamine', 6.621753040239896), ('hemm', 6.475087476285749),\n",
    " ('thiocyanide', 6.475087476285749), ('coltsfoot', 6.475087476285749), ('steamship', 6.463591109097303), ('matsu', 6.463591109097303), ('neuroma', 6.321629908943605), ('carlie', 6.225210003069149), ('piratery', 6.020599913279624), ('escapement', 6.020599913279624), ('beshear', 5.70316862567409), ('zapatero', 5.7010497154289865), ('polyp', 5.61799694480083),\n",
    " ('centum', 5.418539921951662), ('scoter', 5.395906230238125), ('ontologic', 5.395906230238125), ('hoptoad', 5.395906230238125), ('oread', 5.395906230238125), ('unreadiness', 5.395906230238125), ('muslin', 5.384324731238488), ('dovecot', 5.322957383962485), ('dermal', 5.149830532734095)] \n",
    "\n",
    "oct_pos_2008 = [('diverticulosis', 65.36470503222607), ('palisading', 32.682352516113035), ('actinic', 32.682352516113035), ('focally', 32.682352516113035), ('dermatopathology', 32.682352516113035), ('jiff', 25.046903045570367), ('stingo', 20.47003970515072), ('adenomatous', 20.039092698225822), ('dorsum', 20.039092698225822), ('dermis', 19.662989306802906), ('squamous', 15.496030796899948), ('delusory', 14.029356198619123), ('resection', 13.847379800543136), ('melamine', 13.368444817088092), ('theriaca', 12.950174952571498), ('precancerous', 12.64325981788721), ('neuroma', 12.64325981788721), ('awee', 12.405152622711224), ('polyp', 9.831494653401453), ('dermal', 9.831494653401453), ('freath', 9.712631214428624), ('folksiness', 9.632959861247398), ('muslin', 9.185024541524479), ('epithelial', 7.924106657505655), ('misogynic', 7.826779887263511), ('coble', 7.826779887263511), ('malm', 7.554268722333374), ('coistril', 7.554268722333374), ('tuath', 7.224719895935548), ('nidal', 7.224719895935548), ('exteriorization', 7.156818820794936), ('krona', 7.1215079866730955), ('carcinoma', 7.047130898238609), ('folksi', 6.843802350808908), ('belvedere', 6.78841297496767), ('cyanuric', 6.622659904607587), ('parah', 6.622659904607587), ('interbank', 6.5153765850602054), ('harn', 6.475087476285749), ('saponin', 6.475087476285749), ('compossible', 6.475087476285749), ('babby', 6.386266790308558), ('airedale', 6.225210003069149), ('glazier', 6.0833798673856965), ('feticide', 6.020599913279624), ('kronur', 6.020599913279624), ('doggon', 5.7254550566359494), ('reformism', 5.719569917615643), ('internode', 5.70316862567409), ('overlying', 5.622243147373497)] \n",
    "\n",
    "nov_pos_2008 = [('awee', 128.34561751958918), ('keratinization', 28.627275283179745), ('melamine', 28.111215736867482), ('alanyl', 20.504443674904874), ('valyl', 16.187718690714373), ('leucyl', 15.108537444666748), ('telestial', 15.108537444666748), ('nonya', 12.950174952571498), ('narwhal', 12.243996187613392), ('syncope', 10.79181246047625), ('podesta', 10.115966254987367), ('circumciser', 10.019546349112911), ('aesthetical', 9.632959861247398), ('narwh', 9.50528104279015), ('undrained', 9.337815004603723), ('passageway', 9.030899869919436), ('prolyl', 8.633449968380999), ('glycolaldehyde', 8.633449968380999), ('melamin', 8.588182584953923), ('reformism', 8.428839878591473), ('monogamic', 7.781512503836437), ('mia', 7.781512503836437),\n",
    " ('hypospadias', 7.554268722333374), ('providentialism', 7.554268722333374), ('burao', 7.554268722333374), ('noddy', 7.224719895935548), ('gerontocracy', 7.003361253452793), ('bassoon', 6.843802350808908), ('madrasah', 6.78841297496767), ('babby', 6.612998155644957), ('tomogram', 6.475087476285749), ('chloroquine', 6.475087476285749), ('butyrate', 6.475087476285749), ('glycyl', 6.475087476285749), ('kiva', 6.2553184377623605), ('guttersnipe', 6.225210003069149), ('voidable', 6.225210003069149), ('standish', 6.225210003069149), ('flutist', 6.225210003069149), ('scena', 6.202576311355612), ('manticore', 6.202576311355612), ('callus', 6.086163356867567), ('eliminative', 6.0833798673856965), ('constitutor', 6.020599913279624), ('astigmatism', 5.61799694480083), ('vagus', 5.458829030726118), ('aspartyl', 5.447058752685505), ('carse', 5.447058752685505), ('tiddi', 5.447058752685505), ('tablespoonful', 5.418539921951662)] \n",
    "\n",
    "dec_pos_2008 = [('prorogue', 93.88876840614337), ('tharm', 19.42526242885725), ('narwhal', 17.11660691533709), ('saturnalia', 14.449439791871097), ('dartos', 13.847379800543136), ('eutectic', 13.847379800543136), ('rickey', 13.83651638687021), ('backwardation', 13.83651638687021), ('cummingtonite', 12.950174952571498), ('prorogation', 12.64325981788721), ('nonmoral', 11.870993706523874), ('parhelic', 11.870993706523874), ('fawny', 10.115966254987367), ('mulk', 9.712631214428624),\n",
    " ('graupel', 9.712631214428624), ('diaconate', 9.712631214428624), ('snozzle', 9.712631214428624), ('shaitan', 9.337815004603723), ('keratinization', 9.065303839673586), ('learnedness', 8.633449968380999), ('melamine', 8.620772825972695), ('scrapy', 8.55966375422008), ('buckie', 8.428839878591473), ('narwh', 7.9844360759437265), ('supernature', 7.633940075514599), ('greensand', 7.554268722333374), ('yair', 7.554268722333374),\n",
    " ('saturnalian', 7.554268722333374), ('superordinate', 7.554268722333374), ('conky', 7.52574989159953),\n",
    " ('eggnog', 7.395832880338612), ('noncontradiction', 7.003361253452793), ('glioma', 7.003361253452793), ('solstic', 6.867559103171568), ('underconsumption', 6.843802350808908), ('meddler', 6.679697566075274), ('metrification', 6.679697566075274), ('melamin', 6.679697566075274), ('gumma', 6.475087476285749), ('hypogeum', 6.475087476285749), ('alizarin', 6.475087476285749), ('mutism', 6.225210003069149), ('moissanite', 6.0833798673856965), ('cancan', 6.020599913279624), ('parlamento', 6.020599913279624), ('physicalism', 5.987102807893162), ('aborigine', 5.780230961476612), ('deceivingly', 5.7254550566359494), ('corpuscular', 5.447058752685505), ('thermography', 5.418539921951662)] \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets do the same thing for the negative comments in 2008. We will make variables for the top frequency words in the negative comments and save them for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan_neg_2008 = [('dudley', 42.7983187711004), ('wizened', 20.911618294138332), ('biddy', 12.450420006138298), ('didle', 8.633449968380999), ('squatter', 7.633940075514599), ('oxidation', 6.225210003069149), ('willey', 5.447058752685505), ('huck', 5.149830532734095), ('concussion', 4.816479930623699), ('kik', 4.771212547196624),\n",
    " ('recount', 4.6716935168098646), ('humidity', 4.562534900539273), ('dada', 4.3167249841904995), ('flax', 4.3167249841904995), ('ullage', 4.3167249841904995), ('corrie', 4.182323658827666), ('deceiv', 3.8169700377572995), ('bulldozer', 3.5218251811136247), ('tinker', 3.5218251811136247), ('rasping', 3.2375437381428744), ('spital', 3.2375437381428744), ('sledge', 3.2375437381428744), ('dietetic', 3.2375437381428744), ('untroubled', 3.2375437381428744), ('linn', 3.2375437381428744), ('dehumidifier', 3.2375437381428744), ('payor', 3.2375437381428744), ('unassigned', 3.2375437381428744), ('rax', 3.2375437381428744), ('suttee', 3.2375437381428744), ('fluorination', 3.2375437381428744), ('terrace', 3.2375437381428744), ('plenum', 3.2375437381428744), ('tusked', 3.2375437381428744), ('clearinghouse', 3.2375437381428744), ('minot', 3.1126050015345745), ('sherry', 3.1126050015345745), ('param', 3.1126050015345745), ('televise', 3.1126050015345745), ('strictness', 3.1126050015345745)]\n",
    "\n",
    "feb_neg_2008 = [('willey', 27.235293763427528), ('grantee', 7.554268722333374), ('idempot', 5.395906230238125), ('nonfederal', 5.395906230238125), ('marzipan', 4.3167249841904995), ('fizz', 4.214419939295737),\n",
    " ('dianetics', 3.9133899436317554), ('cordon', 3.8907562519182184), ('superdelegate', 3.8169700377572995), ('rancor', 3.8169700377572995), ('murmur', 3.80211241711606), ('rotor', 3.612359947967774), ('subcontractor', 3.2375437381428744), ('breathable', 3.010299956639812), ('ump', 3.010299956639812),\n",
    " ('naturalism', 2.8627275283179747), ('thence', 2.8627275283179747), ('demagogu', 2.6614786919812423), ('roi', 2.4987747321659985),\n",
    " ('atonement', 2.4082399653118496), ('attic', 2.4082399653118496), ('skater', 2.385606273598312), ('wynn', 2.3344537511509307), ('alms', 2.3344537511509307), ('notability', 2.3344537511509307), ('hydration', 2.3344537511509307), ('disrespectfully', 2.3344537511509307), ('hypothermia', 2.3344537511509307), ('kaiser', 2.289186367723856), ('deposition', 2.2812674502696364), ('valentine', 2.2812674502696364), ('decre', 2.1583624920952498), ('aspartic', 2.1583624920952498), ('reinsert', 2.1583624920952498), ('celeri', 2.1583624920952498), ('lactobacillus', 2.1583624920952498), ('paginate', 2.1583624920952498), ('trounce', 2.1583624920952498), ('parapleg', 2.1583624920952498), ('oner', 2.1583624920952498)] \n",
    "\n",
    "mar_neg_2008 = [('lina', 17.266899936761998), ('kayles', 14.449439791871097), ('asok', 9.712631214428624),\n",
    " ('homoeopathy', 5.447058752685505), ('silica', 5.418539921951662), ('lancet', 5.11750992628768), ('inhabitant', 4.771212547196624), ('colitis', 4.3167249841904995), ('integrative', 4.3167249841904995), ('presentational', 4.3167249841904995), ('donna', 4.213497708600623), ('causey', 3.612359947967774), ('hypercube', 3.2375437381428744), ('formaldehyd', 3.2375437381428744), ('nonresident', 3.2375437381428744), ('clop', 3.2375437381428744), ('cubicl', 3.2375437381428744), ('ulcerative', 3.2375437381428744), ('reductional', 3.2375437381428744), ('diuretic', 3.1126050015345745), ('mew', 3.1126050015345745),\n",
    " ('catheter', 3.1126050015345745), ('tantric', 3.1126050015345745), ('osteopathy', 3.1126050015345745), ('bungee', 3.1126050015345745), ('demagogu', 3.0416899336928482), ('vertigo', 3.010299956639812), ('superdelegate', 2.8627275283179747), ('unlivable', 2.8627275283179747), ('retry', 2.8627275283179747), ('hubble', 2.6614786919812423), ('normalization', 2.6614786919812423), ('recorder', 2.6413688858352184), ('streaker', 2.4082399653118496), ('ascending', 2.4082399653118496), ('wellness', 2.4082399653118496), ('lowy', 2.385606273598312),\n",
    " ('unpledged', 2.3344537511509307), ('denotative', 2.3344537511509307), ('repaint', 2.3344537511509307)] \n",
    "\n",
    "may_neg_2008 = [('topper', 83.09695594566712), ('mattress', 19.003499051429955), ('ingle', 15.108537444666748), ('serta', 12.950174952571498), ('foam', 12.356859410833719), ('palfrey', 11.870993706523874), ('rearm', 11.870993706523874), ('cutler', 8.55966375422008), ('coriander', 7.554268722333374), ('registrant', 7.156818820794936), ('patina', 6.622659904607587), ('bilk', 6.622659904607587), ('reassert', 6.622659904607587), ('federalize', 6.622659904607587), ('liang', 6.475087476285749), ('revival', 5.852080150834198), ('suid', 5.395906230238125), ('hark', 5.248333801916287), ('reinvestment', 5.149830532734095), ('austerity', 5.149830532734095), ('quadrillion', 4.562534900539273), ('updo', 4.3167249841904995), ('checkerboard', 4.3167249841904995), ('cutthroat', 3.9133899436317554), ('unappreciative', 3.8907562519182184), ('occupancy', 3.8169700377572995), ('kyle', 3.745331296533887), ('newcomer', 3.612359947967774), ('audition', 3.612359947967774), ('automaton', 3.339848783037637), ('barton', 3.339848783037637), ('swooning', 3.3113299523037933), ('topp', 3.2375437381428744), ('sycamore', 3.2375437381428744), ('amnesic', 3.2375437381428744), ('untraditional', 3.2375437381428744), ('decathlon', 3.2375437381428744), ('swirly', 3.2375437381428744), ('atchison', 3.2375437381428744), ('sunt', 3.2375437381428744)] \n",
    "\n",
    "jun_neg_2008 = [('cookbook', 10.533744271501558), ('austerity', 10.29966106546819), ('ectopic', 9.632959861247398), ('citrate', 8.55966375422008), ('speculator', 7.633940075514599), ('unmounted', 6.475087476285749), ('truc', 6.225210003069149), ('swarming', 5.497304410765197), ('pitfall', 5.447058752685505), ('oneism', 5.447058752685505), ('ingot', 4.668907502301861), ('iterable', 4.668907502301861), ('impairment', 4.562534900539273), ('electrolyzer', 4.3167249841904995), ('augment', 4.3167249841904995), ('ajar', 4.3167249841904995), ('starboard', 4.3167249841904995), ('intrastate', 4.3167249841904995), ('beget', 4.3167249841904995), ('keyhole', 4.3167249841904995), ('rupture', 4.294091292476962), ('escalator', 4.214419939295737), ('chatty', 3.8907562519182184), ('allopathic', 3.8169700377572995), ('antonym', 3.8169700377572995), ('selenium', 3.80211241711606), ('hearken', 3.800699810285991), ('prius', 3.703278967161176), ('showdown', 3.6423373181907417), ('teacup', 3.612359947967774), ('triton', 3.421901175404454), ('megalomaniac', 3.4047935800478673), ('bac', 3.3457339220579434), ('waka', 3.2375437381428744), ('aspirate', 3.2375437381428744), ('transmogrify', 3.2375437381428744), ('zucchini', 3.2375437381428744), ('tented', 3.2375437381428744), ('uncrowd', 3.2375437381428744), ('unshared', 3.2375437381428744)] \n",
    "\n",
    "jul_neg_2008 = [('clive', 42.08806859585737), ('ventriloquist', 32.31795554548651), ('eavesdropping', 13.83651638687021), ('austerity', 12.406409919768501), ('quis', 10.837079843903323), ('demagogic', 9.030899869919436), ('eros', 9.030899869919436),\n",
    " ('urna', 8.55966375422008),\n",
    " ('renegotiate', 8.428839878591473), ('aenean', 7.781512503836437), ('vestibulum', 7.781512503836437), ('bale', 7.724745799101142),\n",
    " ('pulvinar', 7.003361253452793), ('dictum', 6.622659904607587), ('barbarous', 6.554329768934302), ('tod', 6.475087476285749),\n",
    " ('undisput', 6.475087476285749), ('corruptly', 5.447058752685505), ('deindustrialize', 5.447058752685505), ('riddler', 5.447058752685505), ('necktie', 5.447058752685505), ('dolor', 5.418539921951662), ('botfly', 5.395906230238125), ('airway', 4.668907502301861), ('jiggy', 4.668907502301861), ('lacinia', 4.668907502301861), ('photosensitive', 4.3167249841904995), ('amortization', 4.3167249841904995), ('oceanology', 4.3167249841904995), ('hippocampus', 4.3167249841904995), ('adiabatic', 4.3167249841904995), ('hitless', 4.3167249841904995), ('injector', 4.3167249841904995), ('thither', 4.294091292476962), ('sware', 4.294091292476962), ('porta', 4.214419939295737), ('parallelization', 3.8907562519182184), ('cursus', 3.8907562519182184), ('carbide', 3.8907562519182184), ('freemasonry', 3.8169700377572995)] \n",
    "\n",
    "aug_neg_2008 = [('gog', 13.83651638687021), ('vestibulum', 9.337815004603723), ('berserker', 8.633449968380999), ('occultism', 7.781512503836437), ('babby', 7.490662593067774), ('snare', 7.224719895935548), ('separatist', 6.867559103171568), ('manta', 6.475087476285749), ('consular', 6.475087476285749), ('nestable', 6.475087476285749), ('vivisection', 6.225210003069149), ('gymnast', 6.202576311355612), ('toner', 5.7254550566359494), ('thrip', 5.447058752685505), ('justo', 5.447058752685505), ('perchlorate', 5.395906230238125), ('measles', 5.248333801916287), ('marrow', 5.248333801916287), ('cobalt', 4.816479930623699), ('lettuce', 4.754463994503394), ('polygynous', 4.668907502301861), ('cursus', 4.668907502301861), ('polygyny', 4.3167249841904995), ('gyroscope', 4.3167249841904995), ('transuranic', 4.3167249841904995), ('quis', 4.214419939295737), ('trig', 4.214419939295737), ('juror', 4.182323658827666), ('rewind', 4.182323658827666), ('kremlin', 3.9794145025672547), ('fermentum', 3.8907562519182184), ('coil', 3.8169700377572995), ('omnibus', 3.80211241711606), ('chester', 3.612359947967774), ('skinning', 3.612359947967774), ('subatomic', 3.612359947967774), ('dolor', 3.612359947967774), ('eros', 3.612359947967774), ('acme', 3.421901175404454), ('swimmer', 3.3113299523037933)] \n",
    "\n",
    "sep_neg_2008 = [('chrome', 29.58333152135445), ('ventriloquist', 15.968872151887453), ('custodial', 15.108537444666748), ('trig', 12.64325981788721), ('pyrex', 8.633449968380999), ('chrom', 8.588182584953923), ('fey', 7.5719241393942935), ('mambo', 7.554268722333374), ('austerity', 7.490662593067774), ('levee', 7.0224961810010385), ('headroom', 7.003361253452793), ('wearable', 7.003361253452793), ('babby', 6.78841297496767), ('mae', 6.339285326004525),\n",
    " ('collier', 6.225210003069149), ('refueling', 5.7254550566359494), ('pist', 5.418539921951662), ('bearskin', 5.395906230238125), ('lockbox', 5.395906230238125), ('demagogic', 4.816479930623699), ('beau', 4.816479930623699), ('tangling', 4.3167249841904995), ('dermis', 4.3167249841904995), ('diverticulosis', 4.3167249841904995), ('eccentricity', 4.3167249841904995), ('nonce', 4.3167249841904995), ('frontiersman', 4.3167249841904995), ('crevasse', 4.3167249841904995), ('unreleased', 4.214419939295737), ('swam', 4.213497708600623), ('acorn', 4.213497708600623), ('diddy', 4.182323658827666), ('wicket', 3.8907562519182184), ('enhancer', 3.8907562519182184), ('binder', 3.612359947967774), ('sambo', 3.612359947967774), ('fabled', 3.612359947967774), ('anchorage', 3.421901175404454), ('tetanus', 3.421901175404454), ('uninstalled', 3.421901175404454)] \n",
    "\n",
    "oct_neg_2008 = [('vaulted', 104.0124335288864), ('acorn', 44.24172594030654), ('apperception', 19.42526242885725), ('bark', 17.344949448234487), ('planetarium', 16.187718690714373), ('plumber', 13.559026947287455), ('ventriloquist', 10.265703526213363), ('licensure', 8.11106133023426), ('phenomenological', 7.224719895935548), ('mae', 5.811011548837481),\n",
    " ('doctrinaire', 5.7254550566359494), ('austerity', 5.61799694480083), ('redistributive', 5.447058752685505), ('ticky', 5.395906230238125), ('unexperienced', 5.395906230238125), ('acetate', 5.395906230238125), ('mitre', 5.395906230238125), ('leek', 5.395906230238125),\n",
    " ('gynecologist', 5.322957383962485), ('rejoin', 4.942746142250878), ('babby', 4.915747326700727), ('pollster', 4.816479930623699), ('edifice', 4.816479930623699), ('radon', 4.668907502301861), ('firestone', 4.668907502301861), ('fey', 4.578372735447712),\n",
    " ('pringle', 4.3167249841904995), ('overrepresentation', 4.3167249841904995), ('indubitable', 4.3167249841904995), ('plann', 4.294091292476962), ('secessionist', 4.294091292476962), ('calibration', 4.182323658827666), ('blanca', 3.8907562519182184), ('registrant', 3.8169700377572995), ('radium', 3.612359947967774), ('demagogic', 3.612359947967774), ('chrome', 3.5218251811136247), ('singh', 3.421901175404454), ('disallow', 3.3457339220579434), ('lowy', 3.339848783037637)]\n",
    "\n",
    "nov_neg_2008 = [('adroitly', 21.78823501074202), ('awee', 16.341176258056517), ('spanky', 16.341176258056517), ('vagus', 11.870993706523874), ('warrantee', 9.712631214428624), ('bark', 9.636083026796937), ('ventriloquist', 6.843802350808908), ('swayable', 6.475087476285749), ('metronome', 6.475087476285749), ('courtship', 6.321629908943605), ('keratinization', 6.225210003069149), ('ascension', 5.70316862567409), ('initiation', 5.6349202897818), ('dynamo', 5.447058752685505), ('tau', 5.418539921951662),\n",
    " ('alp', 5.418539921951662),\n",
    " ('paresis', 5.395906230238125), ('wasabi', 5.248333801916287), ('plumber', 4.930555253559074), ('bumpy', 4.915747326700727), ('acorn', 4.915747326700727), ('knockout', 4.816479930623699), ('glans', 4.681664120667358), ('grits', 4.668907502301861), ('rogan', 4.562534900539273), ('galactose', 4.3167249841904995), ('unidimensional', 4.3167249841904995), ('snoop', 4.232318819612773), ('garnett', 4.214419939295737), ('socialite', 4.214419939295737), ('begat', 4.214419939295737), ('narwh', 4.214419939295737), ('melamine', 4.182323658827666), ('chrome', 4.050098958280668), ('tapestry', 3.8907562519182184), ('collectible', 3.8907562519182184), ('acquiescence', 3.8169700377572995), ('adulation', 3.8169700377572995), ('sura', 3.8169700377572995), ('fanfare', 3.8169700377572995)] \n",
    "\n",
    "dec_neg_2008 = [('brig', 13.359395132150548), ('partridge', 12.950174952571498), ('nativity', 9.065303839673586), ('merri', 7.60422483423212),\n",
    " ('alpine', 7.156818820794936), ('subordination', 7.003361253452793), ('utilitarianism', 6.923689900271568), ('congestion', 6.554329768934302), ('isosceles', 6.475087476285749), ('shoeless', 6.475087476285749), ('chrome', 5.458829030726118), ('physicalism', 5.447058752685505), ('sunder', 5.395906230238125), ('storehouse', 5.395906230238125), ('bailiff', 4.771212547196624), ('bloc', 4.6716935168098646), ('manna', 4.668907502301861), ('tickler', 4.668907502301861), ('billiards', 4.668907502301861), ('niggard', 4.668907502301861), ('breastfeeding', 4.578372735447712), ('cauterization', 4.3167249841904995), ('sixfold', 4.3167249841904995), ('equilateral', 4.3167249841904995), ('pheasant', 4.3167249841904995), ('incontinently', 4.3167249841904995), ('thrower', 4.214419939295737), ('iodine', 4.214419939295737), ('separatist', 4.050098958280668), ('reorganization', 3.8907562519182184), ('alphabetic', 3.8907562519182184), ('searing', 3.8169700377572995), ('sultan', 3.80211241711606), ('narwhal', 3.80211241711606), ('babby', 3.745331296533887), ('relativist', 3.612359947967774), ('physicalist', 3.612359947967774), ('boa', 3.612359947967774),\n",
    " ('blackbird', 3.612359947967774), ('pounder', 3.612359947967774)] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the most frequent words across 2008 comments\n",
    "\n",
    "To see if trends have impacted the most popular words across the months, we shall look at most frequently occuring words across the months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the most frequent words of each month, we will add together the most frequent words of positive and negative comments into a combined DataFrame. From there, we can easily extract the top words of each month and visualize them with a bar graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan_2008_pd = pd.DataFrame(jan_pos_2008 + jan_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "feb_2008_pd = pd.DataFrame(feb_pos_2008 + feb_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "mar_2008_pd = pd.DataFrame(mar_pos_2008 + mar_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "apr_2008_pd = pd.DataFrame(apr_pos_2008, columns=[\"word\", \"frequency\"])\n",
    "may_2008_pd = pd.DataFrame(may_pos_2008 + may_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "jun_2008_pd = pd.DataFrame(jun_pos_2008 + jun_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "jul_2008_pd = pd.DataFrame(jul_pos_2008 + jul_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "aug_2008_pd = pd.DataFrame(aug_pos_2008 + aug_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "sep_2008_pd = pd.DataFrame(sep_pos_2008 + sep_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "oct_2008_pd = pd.DataFrame(oct_pos_2008 + oct_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "nov_2008_pd = pd.DataFrame(nov_pos_2008 + nov_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "dec_2008_pd = pd.DataFrame(dec_pos_2008 + dec_neg_2008, columns=[\"word\", \"frequency\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "January most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efd7387b8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jan_2008_sorted = jan_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "jan_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of January\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "February most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efd8725c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XEXEUcFSvhx8ENup6jszMrC0eKWpmVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjVRKqBLWlbShZLukXS3pE0lLSfpakn357+vX9CZNTOzvpUtof8YuDIi3gasB9wNHAlcExFrAdfk+2ZmNkhaBnRJrwO2AH4BEBGvRsRzwE7A2fllZwM7L6hMmplZa2VK6G8BZgJnSpom6eeSlgJWiojHAfLfFZttLOkASVMkTZk5c2bXMm5mZvMqE9BHAOOAUyNifeAlKlSvRMTpEbFBRGwwZsyYNrNpZmatlAnoM4AZETE537+QFOCfkPRGgPz3yQWTRTMzK6NlQI+IfwKPSVo7P7QN8FfgUmCv/NhewCULJIdmZlbKiJKvOxg4V9JiwIPA3qSTwQWS9gUeBT6+YLJoZmZllAroETEd2KDJU9t0NztmZtYujxQ1M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6uJ0gFd0nBJ0yRdlu+vLmmypPsl/UbSYgsum2Zm1kqVEvohwN2F+98DToiItYBngX27mTEzM6umVECXtArwIeDn+b6ArYEL80vOBnZeEBk0M7NyypbQfwQcDszJ95cHnouIWfn+DOBNzTaUdICkKZKmzJw5s6PMmplZ31oGdEkfBp6MiKnFh5u8NJptHxGnR8QGEbHBmDFj2symmZm1MqLEazYDdpS0AzAKeB2pxL6spBG5lL4K8I8Fl00zM2ulZQk9Ir4aEatExFhgd+DaiPgkMAH4WH7ZXsAlCyyXZmbWUif90I8ADpP0AKlO/RfdyZKZmbWjTJXLXBFxHXBd/v9BYKPuZ8nMzNrhkaJmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY1MWKwM9Cpo285uv/nN+r/eTOzunAJ3cysJhzQzcxqwgHdzKwmHNDNzGrCAd3MrCYc0M3MasIB3cysJhzQzcxqwgHdzKwmHNDNzGrCAd3MrCYc0M3MaqJlQJf0ZkkTJN0t6S5Jh+THl5N0taT789/XL/jsmplZX8qU0GcBX4qItwObAJ+XtA5wJHBNRKwFXJPvm5nZIGkZ0CPi8Yi4Lf//InA38CZgJ+Ds/LKzgZ0XVCbNzKy1SnXoksYC6wOTgZUi4nFIQR9YsY9tDpA0RdKUmTNndpZbMzPrU+mALmlp4CLg0Ih4oex2EXF6RGwQERuMGTOmnTyamVkJpQK6pJGkYH5uRPwuP/yEpDfm598IPLlgsmhmZmWU6eUi4BfA3RHxw8JTlwJ75f/3Ai7pfvbMzKysMmuKbgZ8CrhT0vT82NeA7wIXSNoXeBT4+ILJopmZldEyoEfEJEB9PL1Nd7NjZmbt8khRM7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7OacEA3M6sJB3Qzs5pwQDczq4kyKxbV2tG3HN36NRv1/5pupGFm1imX0M3MasIB3cysJhzQzcxqYpGvQx8qWtXDl6mD70YaZrbwcgndzKwmXEK3udzjx2zh5oBuQ46rn8za44Bu1oSvVmxh5IBuNoQNhauVgTi5+aqrOxzQzWyRMRRObguSA7qZ2QBakFca7rZoZlYTDuhmZjXhgG5mVhMO6GZmNeGAbmZWEw7oZmY14YBuZlYTDuhmZjXhgG5mVhMdBXRJH5R0r6QHJB3ZrUyZmVl1bQd0ScOBnwD/BawDjJe0TrcyZmZm1XRSQt8IeCAiHoyIV4FfAzt1J1tmZlaVIqK9DaWPAR+MiP3y/U8BG0fEQb1edwBwQL67NnBvP8muADzVVoaGVhpDIQ9DJY2hkIehksZQyMNQSWMo5GGopFFm+9UiYkyrhDqZbVFNHpvv7BARpwOnl0pQmhIRG3SQpyGRxlDIw1BJYyjkYaikMRTyMFTSGAp5GCppdCMPDZ1UucwA3ly4vwrwj86yY2Zm7eokoN8KrCVpdUmLAbsDl3YnW2ZmVlXbVS4RMUvSQcAfgeHAGRFxV4f5KVU1sxCkMRTyMFTSGAp5GCppDIU8DJU0hkIehkoa3cgD0EGjqJmZDS0eKWpmVhMO6GZmNeGAbmZWEwt9QJf0YUkL/ecAkLRU8bNIGiZpycHM08JM0nJdSGPFJo+t3Wm61h5JY5s8tuHA52RoGhKNopJeT+rTPrfXTUTcVnLbXwGbAhcBZ0bE3RXfe9f+no+I35VMZyXgOGDliPivPK/NphHxiwp5uRnYNiL+le8vDVwVEe8tm0bebnNgrYg4U9IYYOmIeKjktgI+CbwlIo6RtCrwhoi4pUoeclorAqMa9yPi0YFMQ9L9wHTgTOCKaONgl3Qv8I2IuCDf/xKwb0SUnrcoH99rMe/nmFhh+zWAGRHxiqStgHWBcyLiuRLbjuvv+Va/M0kn0WTAYGH7L7TKQyGtMcARpLmfit/F1hXSuA34SET8Pd/fEjg5It5VcvuuHd+dkLQZMD0iXpK0BzAO+HFEPNJRuoMd0CX9L/Bp4G/0HDhRcSe/DhgP7J3TOBM4PyJeLLHtmfnfFYH3Atfm++8HrouIfgN+IZ0r8vv+T0SsJ2kEMK3sgZbTmB4R7271WIs0jgI2ANaOiLdKWhn4bURsVnL7U4E5wNYR8fYcjK6KiNKlIEk7Aj8AVgaeBFYD7o6IdwxwGgK2BfYhzT30G+CsiLivQhpvJHUrexlYCbgb+FLjpFti+/2AQ0gD76YDmwA3VTy+p5P26VhSN+FLSft3hxLbTujn6Za/M0l79fd8RJzdKg+FtK4i7YMvAwcCewEzI+KICmlsCJwCfIQUBI8jBfjHSm7fjeN7E+Ak4O3AYqRu2y9FxOsqpHEHsB7p5PxL4BfArhGxZdk0moqIQb2R5nZZrAvprAAcCjwMXAHcDxxcYfvLgDcW7r8R+F2F7W/Nf6cVHpte8TP8GRhXuP8e0o+/ShrTSdMyFPNxR4Xtb2vyOW6vmIfbgeUbaZBOjqcPdBq90ns/8HfgOeB60tVT2W0/TxoZ/SiwWcX3vZNUGp2e778N+E3FNBr75CuNY7q4fxaWGzA1/72j8Nj1baSzKXAHcAswps3vspPjewqwJjCNFMz3Bo5tMx/fJF3xzX2sk1snc7l0y1+AZUmlsMokfYRUAluDdKbbKCKezHXPd5POpGWMjYjHC/efAN5aISsvSVqefJWRz+LPV9ge0gnpt5IaUyi8EfhExTRejYiQ1MjHUhW3fy1PjdzYfgypRFMpjYh4OrcBDIuICZK+N9Bp5P2xB/Ap0v48mFS6fTfwW2D1EmlcDTwOvJNUyj5D0sSI+HLJbLwcES9LQtLiEXFPG3Xwr0kaTyrRfiQ/NrJiGkh6J/NXd5xTctuOq0uA1/LfxyV9iDRVyCol3/8PzFv1syTp9/ULSUTEjmXz0IXjm4h4QNLwiJgNnCnpxopJvCjpq6Tjc4ucp8r7tLehENC/A0yT9BfglcaDFXbQx4EToledZET8W9I+FfJxnaQ/AueTdvbuQH+Xq70dRgoWa0j6MzAG+FiF7YmIWyW9jTQrpYB7IuK1Fpv1doGknwLLStqfdLL7WYXtTwR+D6wk6VjSZ/h6xTw8l+v/JwLnSnoSmDUIadxEOsnvHBEzCo9PkXRayTR+EhEXF/L0XuCrFfIwQ9KywMXA1ZKepfqcR3uTqiiOjYiHJK0O/KpKArkqbitSQL6ctI7BJKBUQAfOJVWXfIhCdUmVPADfljQa+BKpoPU64Isltz2+4nv1pXF8r1g4vr9RMY1/5+lOpkv6PumEX7Xg9Angv0ml83/muvz/VzGN+QyFOvS7gJ+SLk3nnikj4voKaaxGagT8k6QlgBFRov68STq7AFvkuxMj4vcVtx9BTzC+t2wwlrR1RFzbVwNtlGyYLaT3AWC7nI8/RsTVFbd/G7BN3v6aqN7QvBSpzrnRADUaODcinh7gNBT5AFfqPbR0RLxQ+oP0pNOt42tL0ue4MtIaAmW2GQ6cHRF7VH2/XuncSaqznRapjWcl4OcR8ZEWmza2nxoR75F0R0Ssmx+7Pjqt860on8wej4iX8/0lgJUi4uEKaXR6fK9GqlEYSTohjQZOiYgHqqSzIAyFEvpTEXFiuxvnUugBwHKkapdVgNNIO6yq24AX8w93SUnLVPzhbkRquBoBjMuXgmVKQFuSGmOb/bgCqBTQcwCvFMR7WQH4d+ReMpJWj5K9ZPL7v1S4W7rRrNtpkEr2BwKzganAaEk/jIjSJaFuHF85KK8ENL7DN5Dq41uKiNl5HyxW9iTQh/9ExBxJs3IngieBt1TYvpPqksMj4vvqo8dMVOgpQ6oqK/b6mp0fK9WoKemXEfEp4J4mj5USPT1R/gN8q+x2+b0mRcTmkl5k3u9CKenyDavNDIWAPlXSd0jVFcUql1LdFkkNVhsBk/N296tJ3+FWmvxw30SFH66kX+btppMOMkg7rGVAj4ij8t+9q+a78P69D5De71HqQCn2kiH12hlJurwv1Uumn7w8T2pM+lJEPNjPtt084NeJiBckfZJUzXAEKbBXubTt6PiSdDBwFKkOv3EFGqTeDWU9DPxZ0qXA3BNdRPywQhpTctXPz0jfwb9IjYpldVJd0igBT6nwfn0ZUTyxRcSrufqjrHl6SeWT7XvKbCjpgojYLV/tNDsxtdynEbF5/rtMuexWMxQC+vr57yaFxwIo29jySt6pwNxqj3bqkTo9MWxACiBt12HlH9ye9JTyyXlpWYJpHCCSjgH+Sao7blRXVDl4diHtk9tyuv+QVPXg+yGpBHdezsPupFLpvcAZpLrcvj5HNw/4kZJGAjuT+iq/1mgsrqDT4+sQUhfD0lVFTfwj34ZRbV8Cc7tvfidSv/XTJF0JvC4i7iibRkRclv99ntRjqLSI+EP+O/dKq4MqsJmSdoyIS3M6O1FitaDcAPk1YAlJL8DcBXpepfxsh4fkvx+uluWm+Wl7bEG/Ouki0+mNdIDu1mEa3yftqHuAD5AaPCp1IcrpTI5CdyZSQK3S3e+3FLo9tvlZbiQFw71JjU57AXu18zlaPdbP9rfkv41uVUtV+R76ycPN+W/LLmL5uPhLF46vL5C6Kl5O+gGvBtwwkMcXqWF9RKefpQvfxdQOtx+Tv4fTSSflM0hTZldJ4zxSyX6p/H0+DnylYhprADcDj5GqrW4E1qyw/XcGe1/kfEzPMWZN0hicE4DLO013UEvoker0DgIu6CCZI4F9SY2qnyH9eH/eRjrXS2qcwT8AfA74Q4XtVwD+KukW2uutAzAqIg6r8PpmZucqhl+TSpLj6akCKqNZL5mq3+ccSbsBF+b7xd4+LUu3+bi4XdKq0cbo0kI6J5J6NTQ8IqlS6ZI2jy9Jjf34IKkH1f8x73FRurpEaXBQs0v8Kl0Gb5a0YUTcWmGbokuAG4A/Ue14Kuq4Ciwi/gZskntAKSo2TkfEV9X5yN22qxQL5kRaU2IX4EcRcZKkaWXz0Gfe8tli0Ej6Bqlx4TfMWz/4zADnYxjphzu3dwipF0CpLyj3YJhPVOut80VS3eZlzPvjL/1dKM1wqx5kAAAZYElEQVR18WNSnXeQBisdGtV6AXTaS+YtOQ+b5jzcTKpv/TvwnoiYVCKNa0kNXbcw73FR+gSpLkzH0K7cFtGniCjdmCapWMc7CvgoMCsiDq+Qxl9J7SIPk77PRptEqbp8VRyx3Ecad5HGAJxHqgK7XtLtEbFehTQ62qfqzsjdb9F3leJnI2KrEmlMBn4E/A9ppOtDkv4SEe8sm4+m6Q6BgN6s90RERL8t8H01TBQSqNLoNCRI+jxwLGk0Y+OztfwuupyH70WvodjNHhuAfHTjBNn2dAwL4vjqoN64WVqVugzmrnbziZJzh0j6NnBjRFxe9j2bpPEFUqn8dlJ/9lWBX0XE+yqk0dEUG3m/bkiqAnx37sL4rYgoPYBP0uSI2LjXYzdHxCZlT1D5RHQg6WRyvlJ3zE9ExHfL5qNpuoMd0NvV1wHaUPZALaTX7AfcuIz6dvTRqNXNXhmS/gZsHBEtG3mabNuVSZQk3RYR43o9dkeVAKY0P06zKoIqA706JunWiNhQ0rSIWD8/Vqqk2a3jS9J5pB/u3K6TQNWuk8VZI4eRemWcGBGVRpyqs0nbXiTVfb9C6sLYlW52kkZEROkBY53s017bTyf91l6pevUh6SZSnXexSvGwHNCr5GUx0lQQQRq30km3VGAQe7mow8E0VQN2CVeQfnTn5fu7578vAGfRvI840d1eGXcB/25z2466hEn6LKnd4C1KEwc1LEOqtqnissL/o0g9ZyqNjlQXJkCig+kYunh8daPr5FTSZxBptOxDpOrB0tRhd9RuHN95XxwFbE76PJOAY4AqPYA6nWKjGyN3P0mqUjyFnirFPZQGOR1UJgGlvvynkRpEBawu6TMRcUXFvMyr01bVdm/A0fnvmU1uLVvPgRdJwbb37UXghTby8+e+HgPuLLH9L4B3N/uMFfLwe+A+0sjZRoPeiQO0P0aTukueT+oN0rgt14W0hwHXVtym2QRIx1VMYxzpZPR8/nsfsG7JbSf1cZxVOr5IJ+mRpF5QW+bHKvUa6tL+bWvSNuBthe9yvlvFPFxNGma/er59HfjTQO3TJmltCexIFyYHbOO976HQO4fUe+eeTtMdzF4uz+a/v4gSjWS9Rfc75i8taeOImAwgaSNg6fxcmUvC7YH3SDohevrb7ggcXSEPF+db29TmJEoR8TzpRzJePSMbR5C+l6Wjg94mpB4Fq1bdKDqcACkibst18ZWnY4juXXn9lNQQeTswMVflVJq0Takv/WfpmZbiOuCnZT9L1u6kbYeRBtz9oMlzVcaLQCoc/G/h/rcl7Vx249wGMYoUiCvv05xGsfrqzvy3Ur2zpLcCp5KmHHinpHWBHSPi2xWSeTLmnSrgQdqcoHAeA31mKpyRGtOJtjVlJGlgBKSRnfPd2khvQ9IOfoj0A7yDNNBoKUr0lScNxHkdqavjT0jBsO0pToHX00bJA7iKdDl+N+nAPwP4XoXtDyIN1Lgrfx93Uq0/vkgjIoul2vuAj1b8HBNJVS3nkPqCf5Hq05yOJPVFvzDfDgJGDsTxXcjD4k2+n+UrpvFz0vQHW+fbmaQeWFXS+DLp5PIgsD9p4rLS00t36bs4nlSVOSzfdiM1SFZJo9J00k22f5hUtfoUqapnNmlq5NtIPbDKpHF9jg3Fq51K4yZIJ4TLSWtB7EWqpvwBsCtpXvS2Pt+gNYpKOp/UrW0MqR5p7lOU6E4l6bKI+HDuJdOoX2yIaLNniNLwZkXFEVu9GmmOJg1CeWOVfEi6jlSqH0G6RJ5Jmi+6dN90dTiJkqQHSI1FbY9sbNaw2kYaq5GGyy9GmxMgSfo5Kag3rpg+BcyOiP06yVsVSv3Pd4rc8Ke0YMZlEVFquHneZr6eE1W7++Vt2u6Omntgndv4XSj15R4fEadUSKPRsNqYAmEYPV1SI0q0j+Qug3eQ1iqoHLyUZtn8fUT8Md/fDvggaSzMj6NX75U+0uioYTa//sx+no5oswPBoFW5RMR4SW8g9feuMvimsX1j+O0kUmnuhoi4p59NmlLPAJDejzfep+wAkEsLeTta0hTS5WoVoyM1oO1HWk7vqF4NlGW0PYlS9hjV53Hv7UZ1NogFUgnq1Uiz6n0rVwMtXjGNDXsFvWsl3d5BntpxMXChpI+Sllm8lFRarmK2pDUiDapp9POvPLgnOpu0bf+I+EkhrWeVBp6VDujRnWrSw0gnhVmSGrNxljoZZBtExIGFPF0l6biIOExS2ePrKaWh+43qq4+RRr2WFh3M29SfwR4p+k/SlJ6dOJPUan5SPtCnkYL7j0tu3zjI1iZVuzQC80dIJ4pSIk+wVbh/GfP29ihjRC7B7UYacNCOTiZRgi6MbCRVC3xW0sO0MYglu4a0fFxjqbclSNVJVdZX7Uog7ERE/Cx3T7uY1Oj8mYiouhjCV4AJkhojEMeSGolbatKdtnf+ygbCYdI80xEPJ109VaK0tODctoDomSOmlC6cFJ6RdARpJDWkecmfzZ+n7EIXnydNgfA2SX8nVdNWmt5Y0iqk32djAOAk4JCYd97+ygZ9ci6lxVKPJvWoGEHPj79UVUWkro/Xk4Lx+0l9ft9J6lZUZvtv5XxcRWq1fzHfP5rUM6Hs52jWze5fETG6bBqkLlx/JPWuuTUHoPsrbE90MIlS9mi+LUYbP9jsv9rcrmhUFNbtjIh/Ka1CVUXbgbBTva78RCqdTycNW9+k4gnyz6T678bMnz8l1YG3FN2btO2PpGkhTiMFoAOBKytsj6Tvkn6n5+aHDpG0eUQcWSGNayJim1aP9eO/SV0nG50PJuXHhpMKUi1FGtq/bW5YHhZtzI1PKoieR1qgB9IJ4UxSVW3bBn1gkaR7SCXIqRRKT2XrcCVdQ7oEu4k018SkiKjcWpzzsV5EvJLvL05qhHtbye2nkBp8fkvq77snaRDH16rmpROSziad6Yt1nT9ot05usCit+nRw5GmUlYa/nxwRm1ZIYxTpSqXxY7+atLrVy93Ob5P37ubQ/wtIjcuNQDgeeH1EfLzvreZLo9noxvke62f7YaS5bBoLQ1xFapgtfcWTqw/fHRFz8v3hpIbFlldueV8uSZrsbCt62sxeB1wREW8vm4+c3tJRcqHvwjb9VqFWOUk3q3OvWg/fzKCX0IHno7PO9HeQRs69k1QqfU7STRHxn4rp/BK4RdLvSSWQXSi/PBfQeTe7Ll2GrVts0M11nev3t0GvPIwBDifNG93u2pHd0I31Vc8hBcJGV7nxpP1cOhC2q3fAVpqCOKoGkWztXm0BE9poC+ho0rYchE/Nt04sCzTmJqpy9foZ0jGxMqnwJ9LneBE4uWwiSksI/pzUJXlVSeuRqsE+V2LzbnaVfkrSHqRxH5D2RydTLCcxgN2Wmt2A75JGzW1KmwMWcjpLkxYBfoQ0h3U7eRlHmrjnEGD9itt2o5vd1aQqgRH59mng6opp3E4qvTXuL0eJgVGF13fU7bHLx8ZI0on6XbTR3bDZ9191n3ThM7yT1K7zSL5NBd5RMY2zgE0K9zcm9fipksZY0oyJT5F6T11MWhi91XYX5L93kgpP89wq5mF8/g7OIvU8egjYvWIa36Sny/I3SIPxSscL0noHb6aDLoddOi5WJbXXzST1P78YWK3TdIdClcuE/G8jI4069FIlQqXpd99HKqU/Qk+Pl2vbyEsnc110o5tdx5dhkvYkLWLcmGfi46T5u39ZcvtBXTtSXVxfVdJZwGkRcXO+vzFpfvkypbGuyFdp/xMRE/L9rUgjXls27qpnfqGRpEb7R/P91YC/Rocz85Uh6Y0R8bg6n9xLpN5Ws0j16CLNm//Pivm5IyLWzb/V40h9t78W5auOJkfExr26HFad8bExm+gmpP1xE/DFKDdt7gI1FKpcrmvyWJWzzBKkRSGmRoVJfnpTB3Nd5LrAYyMt5PsyFdcZLOj4Miwizsn1+VuTfjS7RsRfKyTRabfHTm1J99ZX3RjYU1JjlOuqwN2NQBkDMyPnUo1gTnrT61R+lGbHK+M0qM3RjRHxeP7b0dw2ERGSLo7U//7Slhv0rVFN9CHSyfqS3IGhrMdytUvk3kdfoGeJvLLOIw0e3CXf3530my11UoGujTadP90hUEL/UuHuKNJBfHcM/Mx808lLrxXO3KVnGZT0R9K8xm3PmCZpVVJ9YGMe8RtJdegtf0yad0jzfKLknOqSPkxqXH4zPd0evxV5ya+BoiYLUzd7rEUaTUuVDZ0GqZJ5+D1pFGLjCmkPUl/o0kPeu5SP60m9fn5aOL5bzr/dxW6PSPoJcFZ0MD5B0mWkefW3JV2V/4e0ylapErakFUil623padw9JCoMpOujgfnmiNikr22apNHW/mhl0EvoETHPHBGSjqezM3i72p3rouFhOlzIN9J8KZUHWWXFGfmgVxUWJVZ4z1caa0Xq+thut8duuYjUplF0ISUX9IWBCdgl7EO6YruItC8mktpGBtqSEXGLVBxQ3XqOouhet0dIx9NnJD1C++MTdiON7Dw+Ip5TGrfxlbIbR5qa+pMV3q+ZCZKOpKeB+RPA/zUKVSULT23tj1YGPaA3sSQlgs8C0GzptZ9V2L6jhXxhbg+T/Zl/keiWVysRsXohneXotcRWGRExW2ngxwlVtusmpQUH3gGM7lWP/joqfp4hYg3S1c4w0j7dhlQdNtALsHQ6unH7XqXSU5VW3fl+hTQ6Hp8QEf+mUO2Wq4Rafg51ab2ArNHb6jO9Ht+HkoUnujDatJlBD+iad2GJ4aS5XY4Z6HxExPFKc128QKpH/2ZUmOsiKvQr7kfH6zaq+RJbN9LTF7uVGyWdzPxLAt7WTn7asDap2m1Z5q1Hf5F0slvYnEsa6v8Xyo9EXBCajW6sUlLtdK1aSF1P74qewXvLkGYFHYgrqY7WCygqFp460On+aGoo1KEX6zlnAU900rg50CT9gf7P/FXWwOzGuo0dLbHVaa+jbshVP0dExHED9Z4LivKKVoP4/odExI8lbRYRf1aboxvVnbVqp5G6GDZKpcOAKdHhRG7tkLRURLzU+pXzbNOVHlj5c38sIi5od3/0ZdBL6INdz9lPo0/ZSX+Oz393JS0U+6t8fzypXr2KyyTtEB2s2wi8HBEvS0LS4hFxj6QqS5Vdxvx18S9IendETO8gX6Xlqp8PkLqlLeyOUpr18RrmnRunSm+dTuxNCsQnkYJppSDWkAP3Th3mZe5cMDnNOUprgg4YSZuSFqNpZ2BRV3pg5c99EKmPf1v7oy+DXkKvC0kTI2KLVo+1SKPjdRtzr4q9SaPqtiYtJDIyInYouf15pO6bl+b3/xBwK2ntw99GRJU607ZJOpbUl3+wqn66QtKvSN/dXfRUucRA9eJS59NUHx4R3++rDrpK3bOk35G6KTdGm34OeP9A9vjJ9f4fAy5tp3dJsXTdYT6+Qeqh0/v4LtUbrc90HdB7SBpHYb3DiJhWYdu7gQ9FHlygtIr35VF9jon5GjSjwkr3vdLakhQUryzbnTJ3v/xo5CHqkpYm9S7ZhdTXf5128lLVUKj66QZJd0bJFekXYB76nKa61RWypKcjYnlJh9Kzylhx+7ObbNZXWiuSllXcmrRfryFV23S+Uk/5PHRjYFGlglofaTTrfhvR5joODYNe5TJUSPomaVRl47LpLEm/jfId/b9Imna2OLPfARXz0GmD5jzaPBGsChSD/2ukIcn/kfRKH9ssCNc1eWxhLH3cLGmdqDa4q6uis2mqn8jtXHvTYTfWHLh3b/nCBasbA4uulvRlOihdd6lhdT4uoWe5hL1+5Jn4lFbwvq1KCVtphsbG7Iz3RJ65scL2HTVodkO+FNyF1OMGUn3hpaQh1qdHRMct8SXzMSQGnHUqH1drkHoxvEJ7fa+7kY+2pqmWdDCpauQtpAE9c58qs31Oo2vVNp3q0sCijkvXSlNBHwasGhEHSFqLNAlb1TUU5k3XAT2RdAVpSa3GtLPLAr+KnpWRWm0/hTSR1fkRMd+lack0GktbTSctA/dKN3q+tJGP95CqnkSqeupal6925ZPlpRGx/WDnpQp1OAdKF/PR6TTVp0bEZ9t8749ExB8k7UXzgF5pVtM6kPQb0r7YM9LQ/yVI66Uu9NPnDhWvAHdJupp00H0AmCTpRChVitiddFl6aw7uZwJXRbUz5ox8IrmYdFn3LGmw0oCKiKmkg20oGawBZx0Z7F5cBR1NU91uMM/b/iH/+1fga8w7cC6oOE11O7oxsKiv7oqFNKr0XFojIj4haXze9j/SvMNG2+GA3uP3+dZwXZWNI82q+D+5yuLDpNL6HElnkBafbVm/FhGNyX6Ozo2Co6m4KkxdDJUBZzUyQdL/I7URFbtPDmSvoV+RhunfycAPsmpcZW5GGsz0m3z/45QvvDS6K65IWgqxMaPr+0nxokpAfzWXyht98tegsF/a5SqXLlKaMW0f0hDnP5JGCW4OfGqgq00Wdgv7gLOhptBrqGhAew0N9iCrnIcJwHYR8Vq+P5J0JV26wVdpgrD9I89EqTSfzE8iot8SfK80tiOtG7wOqR5/M2DvKMzM2Q4H9KzdRqPC9lOB50irofyu2CAq6XdVdrZZHUnahjTgbrAGWSHpXmDTxhWz0hKNN0dE6cF3vfut577pd5Tty17YbnlSTzblPDxVZfumaTqgJ11oNFqHNP1u44TQ2N7VBDboJI0mLY7c6D99PXBMRDw/gHkY1EFWOQ97kwpujZLwlsDRFfvTn0waK3I+qcpkd+CBiDi4QhqdLnbdPF0H9EQVFsztY/srSSX025j3hPCDPjcyGyCSLiJNENYIXJ8iLYo+YFeOgz3IKjc6rkIaW9H4rVdeNSmntStppTSAiRHx+/5eX9iuq4tdz5e+A3oi6bukxre2Go2qDB82G2jNur8OdJdYST8DThjMQVbKSywO4vsfQs9i13+HeRa7Pj0iftJJ+u7l0qNxxm7s7MYXXbbR6EZJ74qIO7ueM7PO/UfS5hExCea2Gf1ngPOwObBXHpgzWIOsbpa0YXS2atImpMnO3k5aQ3g48FKUmHMpIn4M/DiPTP9RRLyQe8aNI61N2hEH9B7XNXms5eVLoXvdCGDvPPR/0EYEmvXhs8DZuS4d0rwsew1wHj44wO/XzPuBAyU9TPurJp1Mqjf/LWkiuz2BNSvm42MRcYzSYtcfII3EPpUK65I244De41+F/+cONS+xXdcW8jVbgO4mrS60BmnxkOeBnYE7BioDQ2SQVcerJkEadyJpeETMBs6UdGPFJDpd7LopB/Ssd+OlSq5tOkQOUrNWLqGn0f7vLV5bWxHxSC4VrxURZyot+7h0xWT+nSf2mi7p+6Sl46quQfx3pSUvtwW+l6e2GFYxjfm4UbQPuX/qLRGx1mDnxaxTbrRPJB1FqiZZOyLeKmll0jz/m1VIYzXgSWAkqavzaOCUPFq8bBpLkqqg7oyI+/PgpHdFxFUVPs786TqgJ30NNY+IkwcvV2bdIel04KRFvdE+T3y3Pmkm1cZ86HfUpa3LVS49inXhHmpudbM58OlB7mEyFLwaESGpMYdK1aqSxvS5zWaNHPTJ4xzQM9eFW811pTGwBi7IddfLStqfNPfSzyqmsUHh/1GkCb6W61L+OuIqFzNbpCgtQL5dvntVRFzdhTQHfeIxcAndzBY9dwKNqWsrtykorT3cMIxUYl+mO1nrjEvoZrbIUFq395ukucxFmpzrmIg4o0IaE+ipQ58FPAwcHxH3dTe31Tmgm9kiI0+f+97GLKp5CtsbK06f+yVSQG9MrDVPEI2IH3Ypu5W5ysXMFiUzSBNhNbwIPFYxjfeQFnO/hBTUPwJMbCOdrnMJ3cwWGZLOAd5FCsYB7ATcAtwH5UrXkq4CPhoRL+b7y5AGJw36XDUuoZvZouRv+dZwSf5bpVFzVeDVwv1XSQtfDzoHdDNbZETEt7qQzC+BWyT9nlTK34WehUMGlatczGyR0auHylxVF8vOXReLKxZN60L2OuaAbmaLDEnF1YpGAR8FZkXE4YOUpa5yQDezRZqk6yNiy8HORze4Dt3MFhmSinOuNEZ5vmGQstN1DuhmtiiZSs+goNdIozz3HcwMdVPHK2SYmS1EjgDeHRGrk3qrvAT8e3Cz1D0O6Ga2KPl6RLxQWJz5LNLizLXggG5mi5L5FmcGFhvE/HSVA7qZLUoaizPvBlzercWZhwp3WzSzRcaCWpx5qHBANzOridpcapiZLeoc0M3MasIB3awkSVtJumyw82HWFwd0sz5IGj7YeTCrwgHdaknS4ZK+kP8/QdK1+f9tJP1K0nhJd0r6i6TvFbb7l6RjJE0GNpX0QUn3SJoE7Do4n8asHAd0q6uJ9MxXvQGwtKSRwObA/cD3gK2BdwMbSto5v3Yp4C8RsTEwBfgZac3I91GjSZysnhzQra6mAu/J6z2+AtxECuzvA54DrouImRExCzgX2CJvNxu4KP//NuChiLg/Uv/eXw3kBzCrygHdaikiGjPp7Q3cCNwAvB9YA3i0n01fjojZhfseqGELDQd0q7OJwJfz3xuAA4HpwM3AlpJWyA2f44Hrm2x/D7C6pDXy/fELPstm7XNAtzq7AXgjcFNEPAG8DNwQEY8DXwUmALcDt+VJmuYRES8DBwD/lxtFHxmwnJu1wUP/zcxqwiV0M7OacEA3M6sJB3Qzs5pwQDczqwkHdDOzmnBANzOrCQd0M7Oa+P8s2mhD32jU5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feb_2008_sorted = feb_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "feb_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of February\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "March most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efdbd1320>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mar_2008_sorted = mar_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "mar_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of March\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "April most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efdc79c50>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apr_2008_sorted = apr_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "apr_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of April\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "May most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efdc120b8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "may_2008_sorted = may_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "may_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of May\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "June most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efddc2358>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jun_2008_sorted = jun_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "jun_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of June\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "July most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efee32710>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "jul_2008_sorted = jul_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "jul_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of July\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "August most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efddc2940>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aug_2008_sorted = aug_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "aug_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of August\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "September most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17efef88e80>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sep_2008_sorted = sep_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "sep_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of September\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "October most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff03b198>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oct_2008_sorted = oct_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "oct_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of October\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "November most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff0e5128>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nov_2008_sorted = nov_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "nov_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of November\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "December most frequently used 20 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff19a470>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dec_2008_sorted = dec_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "dec_2008_sorted[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Words of December\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, combine all the postive comments into a positive Dataframe, then combine all the negative comments into a negative DataFrame. Finally, combine all comments together into a total DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_words_2008_pd = pd.DataFrame(jan_pos_2008 + feb_pos_2008 + mar_pos_2008 + apr_pos_2008 + may_pos_2008 + jun_pos_2008 + jul_pos_2008 + aug_pos_2008 + sep_pos_2008 + oct_pos_2008 + nov_pos_2008 + dec_pos_2008, columns=[\"word\", \"frequency\"])\n",
    "\n",
    "neg_words_2008_pd = pd.DataFrame(jan_neg_2008 + feb_neg_2008 + mar_neg_2008 + may_neg_2008 + jun_neg_2008 + jul_neg_2008 + aug_neg_2008 + sep_neg_2008 + oct_neg_2008 + nov_neg_2008 + dec_neg_2008, columns=[\"word\", \"frequency\"])\n",
    "\n",
    "total_words_2008_pd = pd.concat([pos_words_2008_pd, neg_words_2008_pd])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract the top 20 words from positive comments and plot them in a bar graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff237470>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_words_2008_sorted = pos_words_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "pos_words_2008_sorted.iloc[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Top 20 Words From Positive Comments\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract the top 20 words from negative comments and plot them in a bar graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff2e83c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neg_words_2008_sorted = neg_words_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "neg_words_2008_sorted.iloc[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Top 20 Words From Negative Comments\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, lets see what the top 20 words are from all comments in 2008. Then we will plot them in a bar graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff394b70>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_words_2008_sorted = total_words_2008_pd.sort_values(by=[\"frequency\"], ascending=False)\n",
    "total_words_2008_sorted.iloc[0:20].plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent 20 Words From All Comments\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will extract the most frequent word for positive and negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word            awee\n",
      "frequency    128.346\n",
      "Name: 500, dtype: object\n",
      "word         vaulted\n",
      "frequency    104.012\n",
      "Name: 320, dtype: object\n"
     ]
    }
   ],
   "source": [
    "pos_most_freq = pos_words_2008_pd.loc[pos_words_2008_pd[\"frequency\"].idxmax()]\n",
    "\n",
    "neg_most_freq = neg_words_2008_pd.loc[neg_words_2008_pd[\"frequency\"].idxmax()]\n",
    "\n",
    "print(pos_most_freq)\n",
    "print(neg_most_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also quickly visualize this frequency in a final bar graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff4472e8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\"word\": [pos_most_freq[0], neg_most_freq[0]], \"frequency\": [pos_most_freq[1], neg_most_freq[1]]}\n",
    "top_freq = pd.DataFrame(data)\n",
    "top_freq.plot(x=\"word\", y=\"frequency\", kind=\"bar\", title=\"Most Frequent Positive and Negative\", colormap=\"Accent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding frequency spread across 2008 comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see if there is a spike in popularity of certain key words, we will take a look at the average difference in frequency of the top frequent words in each month.\n",
    "This way, we can essentially capture the spread of each month and see if there are any months with notably larger spread. Months with larger difference could indicate that they have certain words with much larger frequency that may result from trends or the internet topic of the month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This method calculates the average difference of the df up to count index\n",
    "def average_freq_diff(df, count):\n",
    "    prev_word_row = df.iloc[0]\n",
    "    avg_diff = 0\n",
    "    for i in range(1, count):\n",
    "        curr_row = df.iloc[i]\n",
    "        avg_diff += abs(prev_word_row.frequency - curr_row.frequency)\n",
    "        prev_word_row = curr_row\n",
    "    return (avg_diff / count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [average_freq_diff(jan_2008_sorted, 20),\n",
    "average_freq_diff(feb_2008_sorted, 20),\n",
    "average_freq_diff(mar_2008_sorted, 20),\n",
    "average_freq_diff(apr_2008_sorted, 20),\n",
    "average_freq_diff(may_2008_sorted, 20),\n",
    "average_freq_diff(jun_2008_sorted, 20),\n",
    "average_freq_diff(jul_2008_sorted, 20),\n",
    "average_freq_diff(aug_2008_sorted, 20),\n",
    "average_freq_diff(sep_2008_sorted, 20),\n",
    "average_freq_diff(oct_2008_sorted, 20),\n",
    "average_freq_diff(nov_2008_sorted, 20),\n",
    "average_freq_diff(dec_2008_sorted, 20)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will put the average difference in a DataFrame and plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17eff5fe128>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spread_2008_df = pd.DataFrame(data, columns=[\"Average Frequency Difference\"], index=[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"])\n",
    "spread_2008_df.plot(y=\"Average Frequency Difference\", kind=\"bar\", title=\"Average Difference in Word Frequency\", colormap=\"autumn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the overlaps between the positive and negative comments of each month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we want to couple the positive and negative data of each month, so it will be easy to iterate through."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set_list = [(jan_pos_2008,jan_neg_2008),(feb_pos_2008,feb_neg_2008),(mar_pos_2008,mar_neg_2008),\n",
    "                (apr_pos_2008,None),(may_pos_2008,may_neg_2008),(jun_pos_2008,jun_neg_2008),\n",
    "                (jul_pos_2008,jul_neg_2008),(aug_pos_2008,aug_neg_2008),(sep_pos_2008,sep_neg_2008),\n",
    "                (oct_pos_2008,oct_neg_2008),(nov_pos_2008,nov_neg_2008),(dec_pos_2008,dec_neg_2008)]\n",
    "months = [\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"]\n",
    "overlap_dictionary = {}\n",
    "for i in months:\n",
    "    overlap_dictionary[i] = set()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then compared the number of words that appeared in both the positive and negative lists for each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "overlap_counts = []\n",
    "month_counter = -1\n",
    "for i in data_set_list:\n",
    "    month_counter += 1\n",
    "    count = 0\n",
    "    if i[1] == None:\n",
    "        overlap_counts.append(count)\n",
    "        continue\n",
    "    for j in range(40):\n",
    "        for k in range(40):\n",
    "            if i[0][j][0] == i[1][k][0]:\n",
    "                count += 1\n",
    "                overlap_dictionary[months[month_counter]].add(i[0][j][0])\n",
    "    overlap_counts.append(count)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided that a bar graph was the best way to plot the data, as it easily shows which months had larger amounts of overlap than other months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122e4a668>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(overlap_counts, index=months)\n",
    "df.columns = [\"Overlap\"]\n",
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also wanted to create a dictionary showing the words that overlaped, to see if they have any significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'January': {'kik', 'tusked'},\n",
       " 'February': {'grantee', 'nonfederal', 'superdelegate', 'willey'},\n",
       " 'March': {'causey', 'colitis', 'kayles', 'reductional', 'unpledged'},\n",
       " 'April': set(),\n",
       " 'May': {'atchison', 'ingle', 'palfrey'},\n",
       " 'June': {'citrate'},\n",
       " 'July': {'riddler'},\n",
       " 'August': {'babby', 'perchlorate'},\n",
       " 'September': {'babby', 'chrom', 'dermis', 'diverticulosis'},\n",
       " 'October': set(),\n",
       " 'November': {'awee', 'keratinization', 'melamine', 'narwh'},\n",
       " 'December': {'narwhal'}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap_dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis & Results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regarding months with large frequency spikes\n",
    "As presented in our graphs, most months have a consistent frequency amongst top 20 words with a few exceptions. In the graph for average difference, we can see a significant spike in average frequency for the months of February, May, October, November, and December. This is an indicator that perhaps there is a certain popular topic or keyword that motivated a greater frequency of usage in those months.\n",
    "For the month of December, the most popular word is “prorogue” with a frequency of 93 over the second most popular word “tharm” with a frequency of 19.  Research reveals that during December of 2008, the Canadian parliament underwent prorogation that ended parliamentary session without dissolving parliament in advance of new elections. This was a key event in Canadian political history and no doubt generated a ton of reporting and chatter all over the internet. For all the other months, the spiking frequency word revealed nonsensical results without a solid connection to current events. In October 2008, “krona” appeared one of the top fifty words. In late 2008, Iceland faced a financial crisis which resulting defaulting major banks in Iceland. \n",
    "\n",
    "#### Regarding words with most overlaps\n",
    "\n",
    "When analysing words that overlapped between positive and negative comments, we decided to see how many of the top 40 words overlapped for each month. We hypothesized that words that overlap are either for controversial topics, or topics that are large enough to span positive and negative comments. Therefore, months that have more overlap, in theory, could have more controversy, or more popular topics. Although this might be the case, we found it very hard to find conclusive results with the number of overlaps alone, therefore we decided to single out words that did overlap, and words that did not. For example, the word “prorogue” did not overlap despite being very popular in the positive data set for December. We can likely conclude that the event that corresponded with prorogue was generally seen as a positive event, as it was popular with the positive comments and not the negative. If we look at the words that overlap in February however, the words “superdelegate” and “nonfederal” show up. These topics both deal with elections and government. This might conclude that topics relating the government are controversial. Since we didn’t have any other data set to relate to, none of our analysis is conclusive, but the connotation of words can be inferred through the investigation of the overlap between the positive and negative comments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Privacy/Ethics Considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have considered privacy and ethics issues surrounding several parts of our data science process:\n",
    "\n",
    "#### Possible Data Concerns: \n",
    "The data was collected using Reddit’s public API, so we do have  permission to use this data. The data is stored on the website given above in zip files. This website has Reddit data since 2005. This may raise a privacy concern, because if a user deleted their account or a specific post after the data had been collected, their posts will still be found on the website. \n",
    "\n",
    "The dataset does not contain email, age, or gender of the user, but it does contain the reddit usernames, so there is not complete anonymity in this dataset. The usernames could possibly be used to track down the individual’s account and find out private information about them. To avoid this issue, we first removed the usernames from the dataset to ensure the privacy of the users. The resulting data has no link to a specific user that posted the comment.\n",
    "    \n",
    "#### Bias in Data:\n",
    "When analyzing text data to find the most frequently used words, it is possible that outliers may appear with stopwords such as “the”, “and”, “that”, etc. These are words that are very common and will appear in every single document, and therefore may skew our data. In order to mitigate this bias, we used an algorithm to remove the stopwords in the beginning, so that we are only left with the important words. Furthermore, our algorithm for finding the most/least commonly used words compares the frequency of each word in a specific month to the entire year worth of reddit posts. This way, we are able to find only the words that are frequent and unique to that specific month. This part of the algorithm helps us eliminate words that may not be stopwords but are still quite popular in reddit posts, such as common abbreviations “LOL”, “omg”, etc. However, a consequence of the TF-IDF algorithm was that words that appeared in more than one month usually were not able to score high enough, so words that were popular in a 2-3 months only did not show up as significant. This caused our data to be skewed towards words that only showed up in one month. These words are pretty obscure, as unique words in a dataset as large as ours, are rare. \n",
    "    \n",
    "#### Equitable Impact Concerns:\n",
    "In general, there shouldn't be any equiatable impact from our analysis. Our analysis takes in great consideration in protecting the authors and the data is acquired through public means. The resulting analysis cannot cause any harm by avoiding all traces of personal identification. Hence, it should be equitable.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, some popular words that appear tend to concentrate around a certain field or discipline. For example, in the month of November, several chemistry terms dominate the top frequency of positive comments. Similarly, in other months, there will be increases in words related to interior decorating or horse riding. This is likely due to the nature and structure of Reddit where posts and comments are concentrated in Subreddits dedicated to certain topics. Hence, the conversations tend to be more specialized.\n",
    "\n",
    "While there are many words that reflect significant or contemporary events, most of the words, in fact, do not reflect them. Despite many cases of notable correlation to current events of 2008, such as the prorogation  of the Canadian parliament, most of the top words do not reveal a direct connection with other words of the month. Taken individually, the words reveal very little about the popular topic of conversation.\n",
    "\n",
    "Regrettably, we do not have another data set or metric to decisively conclude the correlation of current topic versus the words that were used. However, based on the frequency and the TFIDF method, the popular words that we gathered certainly define the conversation on Reddit at that time. Therefore, the most popular words in itself is not enough to consistently reveal the trend or significant milestones of that month.\n",
    "\n",
    "### Possible Improvements\n",
    "\n",
    "To come to a more rigorous conclusion in regarding this research topic, it will be beneficial to have more analytical resources. While the 2008 Reddit data is enormous, it is still a lot more skewed due to how the population of early Reddit is smaller and specialized. Modern data, such as the ones in 2016 and 2018, will likely have a stronger correlation between frequent words and current topic. To analyze such data, however, will require astronomical computing power. "
   ]
  }
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