sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook I will be examining the sentiment of the NBA headlines and tweets that were collected. Generally we should expect that the media will try to be neutral, but it's worth examining so that we might try to uncover if there is unfair bias levied against a certain player or team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:07.535399Z",
     "start_time": "2021-06-07T21:54:52.094398Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from textblob import TextBlob, Word, Blobber\n",
    "import flair\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:07.544621Z",
     "start_time": "2021-06-07T21:55:07.539182Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:08.149826Z",
     "start_time": "2021-06-07T21:55:07.550250Z"
    }
   },
   "outputs": [],
   "source": [
    "#import data\n",
    "\n",
    "#this data was cleaned in a separate notebook, only null rows and duplicated need to be dropped\n",
    "headline_df = pd.read_csv('data/espn_df_recluster.csv')\n",
    "tweet_df = pd.read_csv('data/nba_twitter.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:08.180807Z",
     "start_time": "2021-06-07T21:55:08.152462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headlines</th>\n",
       "      <th>posted_time</th>\n",
       "      <th>tokens</th>\n",
       "      <th>labels</th>\n",
       "      <th>four_labels</th>\n",
       "      <th>five_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wade matches King for 42nd on scoring list in win</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>['wade', 'match', 'king', '42nd', 'scoring', '...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chick Hearn's widow, Marge Hearn, dies at 98</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['chick', 'hearns', 'widow', 'marge', 'hearn',...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wittman to miss 2 games after brother's death</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['wittman', 'miss', '2', 'game', 'brother', 'd...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LeBron atones for wardrobe mismatch vs. Spurs</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['lebron', 'atones', 'wardrobe', 'mismatch', '...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Source: Rockets petition to rescind Howard's t...</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['source', 'rocket', 'petition', 'rescind', 'h...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           headlines posted_time  \\\n",
       "0  Wade matches King for 42nd on scoring list in win  2016-02-01   \n",
       "1       Chick Hearn's widow, Marge Hearn, dies at 98  2016-01-31   \n",
       "2      Wittman to miss 2 games after brother's death  2016-01-31   \n",
       "3      LeBron atones for wardrobe mismatch vs. Spurs  2016-01-31   \n",
       "4  Source: Rockets petition to rescind Howard's t...  2016-01-31   \n",
       "\n",
       "                                              tokens  labels  four_labels  \\\n",
       "0  ['wade', 'match', 'king', '42nd', 'scoring', '...       0            3   \n",
       "1  ['chick', 'hearns', 'widow', 'marge', 'hearn',...       0            3   \n",
       "2  ['wittman', 'miss', '2', 'game', 'brother', 'd...       0            3   \n",
       "3  ['lebron', 'atones', 'wardrobe', 'mismatch', '...       0            3   \n",
       "4  ['source', 'rocket', 'petition', 'rescind', 'h...       0            3   \n",
       "\n",
       "   five_labels  \n",
       "0            3  \n",
       "1            3  \n",
       "2            3  \n",
       "3            4  \n",
       "4            3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview ESPN headlines\n",
    "headline_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:08.197528Z",
     "start_time": "2021-06-07T21:55:08.183233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tweet</th>\n",
       "      <th>source</th>\n",
       "      <th>tweet_tokens</th>\n",
       "      <th>dual_labels</th>\n",
       "      <th>trip_labels</th>\n",
       "      <th>quad_labels</th>\n",
       "      <th>five_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-26 12:35:27</td>\n",
       "      <td>Obi Toppin is expected to participate in the N...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['obi', 'toppin', 'expected', 'participate', '...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-25 23:08:17</td>\n",
       "      <td>MPJ SLAM</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['mpj', 'slam', '']</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-25 17:48:50</td>\n",
       "      <td>Timberwolves G Malik Beasley has been suspende...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['timberwolves', 'g', 'malik', 'beasley', 'sus...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-25 13:40:20</td>\n",
       "      <td>Luka's game-winner against the Celtics vs. Luk...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['lukas', 'gamewinner', 'celtic', 'v', 'lukas'...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-25 13:10:50</td>\n",
       "      <td>It's time to stop underrating the Utah Jazz Fr...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['time', 'stop', 'underrating', 'utah', 'jazz'...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date                                              tweet  \\\n",
       "0  2021-02-26 12:35:27  Obi Toppin is expected to participate in the N...   \n",
       "1  2021-02-25 23:08:17                                          MPJ SLAM    \n",
       "2  2021-02-25 17:48:50  Timberwolves G Malik Beasley has been suspende...   \n",
       "3  2021-02-25 13:40:20  Luka's game-winner against the Celtics vs. Luk...   \n",
       "4  2021-02-25 13:10:50  It's time to stop underrating the Utah Jazz Fr...   \n",
       "\n",
       "  source                                       tweet_tokens  dual_labels  \\\n",
       "0  Yahoo  ['obi', 'toppin', 'expected', 'participate', '...            0   \n",
       "1  Yahoo                                ['mpj', 'slam', '']            1   \n",
       "2  Yahoo  ['timberwolves', 'g', 'malik', 'beasley', 'sus...            1   \n",
       "3  Yahoo  ['lukas', 'gamewinner', 'celtic', 'v', 'lukas'...            1   \n",
       "4  Yahoo  ['time', 'stop', 'underrating', 'utah', 'jazz'...            1   \n",
       "\n",
       "   trip_labels  quad_labels  five_labels  \n",
       "0            0            3            1  \n",
       "1            2            1            2  \n",
       "2            2            1            2  \n",
       "3            2            2            2  \n",
       "4            2            1            2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview Twitter data\n",
    "tweet_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below functions will be used to iterate through the list of headlines/tweets and count instances of players or teams. The `count_player_mentions` function will look for variations of a player's names. Fortunately, there are some unique names amongst NBA superstars but it is important to note that a more common name would require a more complex conditional statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:08.214469Z",
     "start_time": "2021-06-07T21:55:08.200008Z"
    }
   },
   "outputs": [],
   "source": [
    "def count_player_mentions(text):\n",
    "    text_string = text\n",
    "    \n",
    "    lebron_count = 0\n",
    "    curry_count = 0\n",
    "    harden_count = 0\n",
    "    kd_count = 0\n",
    "    kyrie_count = 0\n",
    "    dame_count = 0\n",
    "    luka_count = 0\n",
    "    zion_count = 0\n",
    "    ja_count = 0\n",
    "    giannis_count = 0\n",
    "    jokic_count = 0\n",
    "    embiid_count = 0\n",
    "    cp3_count = 0\n",
    "    \n",
    "    if text_string.count('LeBron') > 0 or text_string.count('LBJ') > 0 or text_string.count('King James') > 0:\n",
    "        lebron_count += 1\n",
    "    \n",
    "    if text_string.count('Curry') > 0 or text_string.count('Stephen') > 0 or text_string.count('Steph') > 0:\n",
    "        curry_count += 1\n",
    "    \n",
    "    if text_string.count('James Harden') > 0 or text_string.count('Harden') > 0:\n",
    "        harden_count += 1\n",
    "        \n",
    "    if text_string.count('Kevin Durant') > 0 or text_string.count('Durant') > 0 or text_string.count('KD') > 0:\n",
    "        kd_count += 1\n",
    "        \n",
    "    if (text_string.count('Kyrie Irving') > 0 or text_string.count('Irving') > 0 \n",
    "        or text_string.count('Kyrie') > 0):\n",
    "        kyrie_count += 1\n",
    "        \n",
    "    if (text_string.count('Damian Lillard') > 0 or text_string.count('Lillard') > 0 \n",
    "        or text_string.count('Dame') > 0):\n",
    "        dame_count += 1\n",
    "    \n",
    "    if text_string.count('Luka Doncic') > 0 or text_string.count('Luka') > 0 or text_string.count('Doncic') > 0:\n",
    "        luka_count += 1\n",
    "        \n",
    "    if (text_string.count('Zion Williamson') > 0 or text_string.count('Williamson') > 0 \n",
    "        or text_string.count('Zion') >0):\n",
    "        zion_count += 1\n",
    "        \n",
    "    if text_string.count('Ja Morant') > 0 or text_string.count('Morant') > 0 or text_string.count('Ja '):\n",
    "        ja_count += 1\n",
    "        \n",
    "    if (text_string.count('Giannis') > 0 or text_string.count('Antetokounmpo') > 0 \n",
    "        or text_string.count('Greek Freak') > 0):\n",
    "        giannis_count += 1\n",
    "    \n",
    "    if (text_string.count('Nikola') > 0 or text_string.count('Jokic') > 0 \n",
    "        or text_string.count('Joker') > 0):\n",
    "        jokic_count += 1\n",
    "    \n",
    "    if (text_string.count('Joel')> 0 or text_string.count('Embiid') > 0):\n",
    "        embiid_count += 1\n",
    "        \n",
    "    if (text_string.count('Chris Paul') > 0 or text_string.count('CP3') > 0):\n",
    "        cp3_count += 1\n",
    "        \n",
    "    return [lebron_count, curry_count, harden_count, kd_count, \n",
    "            kyrie_count, dame_count, luka_count, zion_count, ja_count, \n",
    "            giannis_count, jokic_count, embiid_count, cp3_count]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:08.258918Z",
     "start_time": "2021-06-07T21:55:08.217127Z"
    }
   },
   "outputs": [],
   "source": [
    "def count_team_mentions(text):\n",
    "    text_str = text\n",
    "    \n",
    "    atl_count = 0\n",
    "    boston_count = 0\n",
    "    brooklyn_count = 0\n",
    "    charlotte_count = 0\n",
    "    chicago_count = 0\n",
    "    cleveland_count = 0\n",
    "    dallas_count = 0\n",
    "    denver_count = 0\n",
    "    detroit_count = 0\n",
    "    gsw_count = 0\n",
    "    houston_count = 0\n",
    "    indiana_count = 0\n",
    "    clippers_count = 0\n",
    "    lakers_count = 0\n",
    "    memphis_count = 0\n",
    "    miami_count = 0\n",
    "    bucks_count = 0\n",
    "    minn_count = 0\n",
    "    pelicans_count = 0\n",
    "    knicks_count = 0\n",
    "    okc_count = 0\n",
    "    orlando_count = 0\n",
    "    philly_count = 0\n",
    "    phoenix_count = 0\n",
    "    portland_count = 0\n",
    "    sac_count = 0\n",
    "    spurs_count = 0\n",
    "    raptors_count = 0\n",
    "    utah_count = 0\n",
    "    wizards_count = 0\n",
    "    \n",
    "    if text_str.count('Atlanta') > 0 or text_str.count('ATL') > 0 or text_str.count('Hawks') > 0:\n",
    "        atl_count += 1\n",
    "        \n",
    "    if text_str.count('Boston') > 0 or text_str.count('Celtics') > 0:\n",
    "        boston_count += 1\n",
    "        \n",
    "    if text_str.count('Brooklyn') > 0 or text_str.count('Nets') > 0:\n",
    "        brooklyn_count += 1\n",
    "        \n",
    "    if text_str.count('Charlotte') > 0 or text_str.count('Hornets') > 0:\n",
    "        charlotte_count += 1\n",
    "        \n",
    "    if text_str.count('Chicago') > 0 or text_str.count('Bulls') > 0 or text_str.count('Windy City') > 0:\n",
    "        chicago_count += 1\n",
    "        \n",
    "    if text_str.count('Cleveland') > 0 or text_str.count('Cavaliers') > 0 or text_str.count('Cavs') > 0:\n",
    "        cleveland_count += 1\n",
    "        \n",
    "    if text_str.count('Dallas') > 0 or text_str.count('Mavericks') > 0 or text_str.count('Mavs') > 0:\n",
    "        dallas_count += 1\n",
    "        \n",
    "    if text_str.count('Denver') > 0 or text_str.count('Nuggets') > 0:\n",
    "        denver_count += 1\n",
    "        \n",
    "    if text_str.count('Detroit') > 0 or text_str.count('Pistons') > 0 or text_str.count('Motor City') > 0:\n",
    "        detroit_count += 1\n",
    "        \n",
    "    if text_str.count('Golden State') > 0 or text_str.count('Warriors') > 0 or text_str.count('GSW') > 0:\n",
    "        gsw_count += 1\n",
    "        \n",
    "    if text_str.count('Houston') > 0 or text_str.count('Rockets') > 0 or text_str.count('H-Town') > 0:\n",
    "        houston_count += 1\n",
    "        \n",
    "    if text_str.count('Indiana') > 0 or text_str.count('Pacers') > 0:\n",
    "        indiana_count += 1\n",
    "        \n",
    "    if text_str.count('Clippers') > 0 or text_str.count('LAC') > 0 or text_str.count('Clips') > 0:\n",
    "        clippers_count += 1\n",
    "        \n",
    "    if text_str.count('Lakers') > 0 or text_str.count('LAL') > 0:\n",
    "        lakers_count += 1\n",
    "        \n",
    "    if text_str.count('Memphis') > 0 or text_str.count('Grizzlies') > 0:\n",
    "        memphis_count += 1\n",
    "        \n",
    "    if text_str.count('Miami') > 0 or text_str.count('Heat') > 0:\n",
    "        miami_count += 1\n",
    "        \n",
    "    if text_str.count('Milwaukee') > 0 or text_str.count('Bucks') > 0:\n",
    "        bucks_count += 1\n",
    "        \n",
    "    if (text_str.count('Minnesota') > 0 or text_str.count('Timberwolves') > 0 or text_str.count(\"T'Wolves\") > 0\n",
    "       or text_str.count('Wolves') > 0):\n",
    "        minn_count += 1\n",
    "        \n",
    "    if text_str.count('New Orleans') > 0 or text_str.count('Pelicans') > 0 or text_str.count('NOP') > 0:\n",
    "        pelicans_count += 1\n",
    "        \n",
    "    if text_str.count('NYK') > 0 or text_str.count('Knicks') > 0 or text_str.count('New York') > 0:\n",
    "        knicks_count += 1\n",
    "        \n",
    "    if (text_str.count('OKC') > 0 or text_str.count('Thunder') > 0 or \n",
    "        text_str.count('Oklahoma City Thunder')) > 0:\n",
    "        okc_count += 1\n",
    "        \n",
    "    if text_str.count('Orlando') > 0 or text_str.count('Magic') > 0:\n",
    "        orlando_count += 1\n",
    "        \n",
    "    if (text_str.count('Philadelphia') > 0 or text_str.count('76ers') > 0 or text_str.count('Sixers') > 0 \n",
    "        or text_str.count('Philly') > 0):\n",
    "        philly_count += 1\n",
    "        \n",
    "    if text_str.count('Phoenix') > 0 or text_str.count('Suns') > 0:\n",
    "        phoenix_count += 1\n",
    "        \n",
    "    if text_str.count('Portland') > 0 or text_str.count('Trailblazers') > 0 or text_str.count('Blazers'):\n",
    "        portland_count += 1\n",
    "        \n",
    "    if text_str.count('Sacramento') > 0 or text_str.count('Kings') > 0:\n",
    "        sac_count += 1\n",
    "        \n",
    "    if text_str.count('San Antonio') > 0 or text_str.count('Spurs') > 0:\n",
    "        spurs_count += 1\n",
    "        \n",
    "    if text_str.count('Toronto') > 0 or text_str.count('Raptors') > 0 or text_str.count('Jurassic Park') > 0:\n",
    "        raptors_count += 1\n",
    "        \n",
    "    if text_str.count('Utah') > 0 or text_str.count('Jazz') > 0 or text_str.count('Salt Lake City') > 0:\n",
    "        utah_count += 1\n",
    "        \n",
    "    if text_str.count('Washington') > 0 or text_str.count('Wizards') > 0 or text_str.count('DC') > 0:\n",
    "        wizards_count += 1\n",
    "        \n",
    "    return [atl_count, boston_count, brooklyn_count, charlotte_count , chicago_count, cleveland_count,\n",
    "            dallas_count, denver_count, detroit_count, gsw_count, houston_count, indiana_count,\n",
    "            clippers_count, lakers_count, memphis_count, miami_count, bucks_count, minn_count, pelicans_count,\n",
    "            knicks_count, okc_count, orlando_count, philly_count, phoenix_count, portland_count, sac_count,\n",
    "            spurs_count, raptors_count, utah_count, wizards_count]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:08.558565Z",
     "start_time": "2021-06-07T21:55:08.262936Z"
    }
   },
   "outputs": [],
   "source": [
    "#drop duplicate rows\n",
    "headline_df.drop_duplicates(inplace=True)\n",
    "tweet_df.drop_duplicates(inplace=True)\n",
    "\n",
    "#drop null rows for tweet/headline column\n",
    "headline_df.dropna(subset=['headlines'], inplace=True)\n",
    "tweet_df.dropna(subset=['tweet'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:09.739216Z",
     "start_time": "2021-06-07T21:55:08.563720Z"
    }
   },
   "outputs": [],
   "source": [
    "#count player mentions from headlines and store informatiion into a dataframe\n",
    "headline_list = list(headline_df['headlines'])\n",
    "\n",
    "headline_player_mention_count = [count_player_mentions(x) for x in headline_list]\n",
    "\n",
    "headline_player_mentions_df = pd.DataFrame(headline_player_mention_count, \n",
    "                                  columns=['LeBron', 'Steph', 'Harden', 'KD', \n",
    "                                           'Kyrie','Dame','Luka','Zion','Ja',\n",
    "                                           'Giannis', 'Jokic', 'Embiid','CP'])\n",
    "\n",
    "#count team mentions from headlines and store information into a dataframe\n",
    "headline_team_mention_count = [count_team_mentions(x) for x in headline_list]\n",
    "\n",
    "headline_team_mentions_df = pd.DataFrame(headline_team_mention_count, \n",
    "                                          columns=['ATL', 'BOS', 'BKN', 'CHA', 'CHI', 'CLE', 'DAL','DEN', \n",
    "                                                 'DET', 'GSW', 'HOU', 'IND', 'LAC', 'LAL', 'MEM', 'MIA', \n",
    "                                                 'MIL', 'MIN', 'NOP', 'NYK', 'OKC','ORL', 'PHI', 'PHX', \n",
    "                                                 'POR', 'SAC', 'SAT', 'TOR', 'UTAH', 'WAS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:13.935678Z",
     "start_time": "2021-06-07T21:55:09.742884Z"
    }
   },
   "outputs": [],
   "source": [
    "#count player mentions from tweets and store information into a dataframe\n",
    "tweet_list = list(tweet_df['tweet'])\n",
    "\n",
    "tweets_player_mention_count = [count_player_mentions(y) for y in tweet_list]\n",
    "\n",
    "tweets_player_mentions_df = pd.DataFrame(tweets_player_mention_count,\n",
    "                                        columns=['LeBron', 'Steph', 'Harden', 'KD', \n",
    "                                           'Kyrie','Dame','Luka','Zion','Ja',\n",
    "                                           'Giannis', 'Jokic', 'Embiid','CP'])\n",
    "\n",
    "#count team mentions from tweets and store information into a dataframe\n",
    "tweets_team_mention_count = [count_team_mentions(y) for y in tweet_list]\n",
    "\n",
    "tweets_team_mentions_df = pd.DataFrame(tweets_team_mention_count, \n",
    "                                      columns=['ATL', 'BOS', 'BKN', 'CHA', 'CHI', 'CLE', 'DAL','DEN', \n",
    "                                                 'DET', 'GSW', 'HOU', 'IND', 'LAC', 'LAL', 'MEM', 'MIA', \n",
    "                                                 'MIL', 'MIN', 'NOP', 'NYK', 'OKC','ORL', 'PHI', 'PHX', \n",
    "                                                 'POR', 'SAC', 'SAT', 'TOR', 'UTAH', 'WAS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:13.962257Z",
     "start_time": "2021-06-07T21:55:13.937477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LeBron</th>\n",
       "      <th>Steph</th>\n",
       "      <th>Harden</th>\n",
       "      <th>KD</th>\n",
       "      <th>Kyrie</th>\n",
       "      <th>Dame</th>\n",
       "      <th>Luka</th>\n",
       "      <th>Zion</th>\n",
       "      <th>Ja</th>\n",
       "      <th>Giannis</th>\n",
       "      <th>Jokic</th>\n",
       "      <th>Embiid</th>\n",
       "      <th>CP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LeBron  Steph  Harden  KD  Kyrie  Dame  Luka  Zion  Ja  Giannis  Jokic  \\\n",
       "0       0      0       0   0      0     0     0     0   0        0      0   \n",
       "1       0      0       0   0      0     0     0     0   0        0      0   \n",
       "2       0      0       0   0      0     0     0     0   0        0      0   \n",
       "3       1      0       0   0      0     0     0     0   0        0      0   \n",
       "4       0      0       0   0      0     0     0     0   0        0      0   \n",
       "\n",
       "   Embiid  CP  \n",
       "0       0   0  \n",
       "1       0   0  \n",
       "2       0   0  \n",
       "3       0   0  \n",
       "4       0   0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline_player_mentions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:13.979227Z",
     "start_time": "2021-06-07T21:55:13.965585Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeBron     1535\n",
       "Steph       842\n",
       "KD          750\n",
       "Harden      643\n",
       "Giannis     540\n",
       "Kyrie       518\n",
       "Embiid      434\n",
       "Dame        341\n",
       "Luka        282\n",
       "Zion        236\n",
       "Jokic       195\n",
       "CP          162\n",
       "Ja           73\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline_player_mentions_df.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The biggest suprise in this list is obviously the low count for Chris Paul.  Chris Paul has been in the league since the 2005-2006 season and has been one of the top point guards over the course of his career. Oftentimes, we see players referred to by their last name, which would be 'Paul' in this case. However, if we chose to inclue the name 'Paul' in the conditional statement then we would need to have addtitional conditions where we don't count instances like \"Paul George\" or \"Paul Millsap\". Paul George and Paul Millsap are both excellent players who have played significant roles for their respective teams so it we would want to avoid overinflating the number of mentions for Chris Paul because of a common name.\n",
    "\n",
    "What is not surprising here is that LeBron James and Stephen Curry lead the pack in terms of player mentions. LeBron has arguably been the best player in the league for more than a decade and Stephen Curry will likely go down as the greatest long distance shooter in the history of the NBA. Kevin Durant's decision to join the Golden State Warriors was highly polarizing and garnered much discussed in the summer of 2016. I started collecting data in January of 2016, so we could expect to see Kevin Durant mentioned many times because of his free agency decision in addition to his stellar play. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:13.986962Z",
     "start_time": "2021-06-07T21:55:13.982204Z"
    }
   },
   "outputs": [],
   "source": [
    "headline_player_names = list(headline_player_mentions_df.sum().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.311817Z",
     "start_time": "2021-06-07T21:55:13.989296Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Player Mentions in ESPN Headlines')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x=headline_player_names, y=headline_player_mentions_df.sum().sort_values(ascending=False).values)\n",
    "plt.xlabel('Players')\n",
    "plt.ylabel('Mention Count')\n",
    "plt.title('Player Mentions in ESPN Headlines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.337010Z",
     "start_time": "2021-06-07T21:55:14.314183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATL</th>\n",
       "      <th>BOS</th>\n",
       "      <th>BKN</th>\n",
       "      <th>CHA</th>\n",
       "      <th>CHI</th>\n",
       "      <th>CLE</th>\n",
       "      <th>DAL</th>\n",
       "      <th>DEN</th>\n",
       "      <th>DET</th>\n",
       "      <th>GSW</th>\n",
       "      <th>...</th>\n",
       "      <th>OKC</th>\n",
       "      <th>ORL</th>\n",
       "      <th>PHI</th>\n",
       "      <th>PHX</th>\n",
       "      <th>POR</th>\n",
       "      <th>SAC</th>\n",
       "      <th>SAT</th>\n",
       "      <th>TOR</th>\n",
       "      <th>UTAH</th>\n",
       "      <th>WAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ATL  BOS  BKN  CHA  CHI  CLE  DAL  DEN  DET  GSW  ...  OKC  ORL  PHI  PHX  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "\n",
       "   POR  SAC  SAT  TOR  UTAH  WAS  \n",
       "0    0    0    0    0     0    0  \n",
       "1    0    0    0    0     0    0  \n",
       "2    0    0    0    0     0    0  \n",
       "3    0    0    1    0     0    0  \n",
       "4    0    0    0    0     0    0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline_team_mentions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.346665Z",
     "start_time": "2021-06-07T21:55:14.339401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GSW     2064\n",
       "CLE     1917\n",
       "LAL     1814\n",
       "BOS     1601\n",
       "HOU     1496\n",
       "PHI     1417\n",
       "LAC     1323\n",
       "TOR     1318\n",
       "OKC     1304\n",
       "MIL     1301\n",
       "NYK     1250\n",
       "MIA     1246\n",
       "SAT     1240\n",
       "CHI     1239\n",
       "UTAH    1175\n",
       "BKN     1142\n",
       "POR     1130\n",
       "NOP     1121\n",
       "ATL     1096\n",
       "DAL     1091\n",
       "DEN     1088\n",
       "SAC     1070\n",
       "ORL     1065\n",
       "IND     1049\n",
       "MIN     1043\n",
       "PHX     1034\n",
       "DET     1034\n",
       "WAS     1021\n",
       "MEM      984\n",
       "CHA      921\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline_team_mentions_df.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There isn't too much that is surprising in terms of team mentions.  The most successful teams are the ones most mentioned by the media as well as teams that are in big markets. The smallest market teams that stand out to me are Cleveland and Oklahoma City.  Cleveland made the NBA Finals four straight seasons and won their first championship in June 2016. Oklahoma City was in the top half of team mentions for a variety of reasons:\n",
    "- Kevin Durant's departure in 2016 caused lots of discussion\n",
    "- The lack of success of the big 3 of Russell Westbrook, Paul George, and Carmelo Anthony\n",
    "- The unexpected success of the Thunder in the 2019-2020 season, led by Chris Paul\n",
    "\n",
    "The New York Knicks are a big market team but were subpar in terms of performance (prior to the 20-21 season). Their inability to make the playoffs and their often maligned owner likely helped drive their numbers up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.354282Z",
     "start_time": "2021-06-07T21:55:14.348803Z"
    }
   },
   "outputs": [],
   "source": [
    "#create list of team names based on espn headlines\n",
    "headline_team_names = list(headline_team_mentions_df.sum().sort_values(ascending=False).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.361195Z",
     "start_time": "2021-06-07T21:55:14.356286Z"
    }
   },
   "outputs": [],
   "source": [
    "team_colors = {'GSW':'gold', 'LAL':'blueviolet', 'CLE':'maroon', 'BOS':'green', \n",
    "               'HOU':'red', 'PHI':'mediumblue', 'TOR':'darkviolet', 'NYK':'darkorange', \n",
    "               'OKC':'dodgerblue', 'LAC':'mediumblue', 'SAT':'dimgray', 'MIA':'red', \n",
    "               'MIL':'darkolivegreen', 'CHI':'red', 'BKN':'black', 'WAS':'navy', \n",
    "               'MIN':'limegreen', 'NOP':'darkred', 'ORL':'blue', 'POR':'red', \n",
    "               'DAL':'mediumblue', 'ATL':'red', 'SAC':'purple', 'PHX':'orange', \n",
    "               'UTAH':'sandybrown', 'IND':'yellow', 'DEN':'deepskyblue', \n",
    "               'MEM':'midnightblue', 'DET':'red', 'CHA':'darkcyan'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.817626Z",
     "start_time": "2021-06-07T21:55:14.363743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Most Mentioned Teams in ESPN Headlines')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=headline_team_names, \n",
    "            y=headline_team_mentions_df.sum().sort_values(ascending=False).values,\n",
    "           palette=team_colors)\n",
    "plt.xlabel('Teams')\n",
    "plt.ylabel('Mention Count')\n",
    "plt.title('Most Mentioned Teams in ESPN Headlines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.831760Z",
     "start_time": "2021-06-07T21:55:14.819971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LeBron</th>\n",
       "      <th>Steph</th>\n",
       "      <th>Harden</th>\n",
       "      <th>KD</th>\n",
       "      <th>Kyrie</th>\n",
       "      <th>Dame</th>\n",
       "      <th>Luka</th>\n",
       "      <th>Zion</th>\n",
       "      <th>Ja</th>\n",
       "      <th>Giannis</th>\n",
       "      <th>Jokic</th>\n",
       "      <th>Embiid</th>\n",
       "      <th>CP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LeBron  Steph  Harden  KD  Kyrie  Dame  Luka  Zion  Ja  Giannis  Jokic  \\\n",
       "0       0      0       0   0      0     0     0     0   0        0      0   \n",
       "1       0      0       0   0      0     0     0     0   0        0      0   \n",
       "2       0      0       0   0      0     0     0     0   0        0      0   \n",
       "3       0      0       0   0      0     0     1     0   0        0      0   \n",
       "4       0      0       0   0      0     0     0     0   0        0      0   \n",
       "\n",
       "   Embiid  CP  \n",
       "0       0   0  \n",
       "1       0   0  \n",
       "2       0   0  \n",
       "3       0   0  \n",
       "4       0   0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_player_mentions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.841847Z",
     "start_time": "2021-06-07T21:55:14.834102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeBron     6654\n",
       "KD         3447\n",
       "Steph      3363\n",
       "Kyrie      1905\n",
       "Harden     1695\n",
       "Giannis    1531\n",
       "Embiid     1116\n",
       "CP          991\n",
       "Zion        926\n",
       "Dame        864\n",
       "Luka        753\n",
       "Jokic       561\n",
       "Ja          252\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_player_mentions_df.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at player mentions in the collected tweets, it appears that Chris Paul is better represented.  Ja Morant is the least mentioned, which meets our expectations given his lack of time in the league and how his peer in Zion Williamson was a more highlty touted player.  Kevin Durant barely edges out Stephen Curry, likely due to his free agency decisions in 2016 and 2019. Stephen Curry was injured for most of the 2019-2020 season but despite not being able to play, he still garnered a lot of attention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:14.850080Z",
     "start_time": "2021-06-07T21:55:14.844025Z"
    }
   },
   "outputs": [],
   "source": [
    "twitter_player_names = list(tweets_player_mentions_df.sum().sort_values(ascending=False).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.141622Z",
     "start_time": "2021-06-07T21:55:14.852148Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Player Mentions in Tweets from Various NBA Media Outlets')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x=twitter_player_names, y=tweets_player_mentions_df.sum().sort_values(ascending=False).values)\n",
    "plt.xlabel('Players')\n",
    "plt.ylabel('Mention Count')\n",
    "plt.title('Player Mentions in Tweets from Various NBA Media Outlets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.159356Z",
     "start_time": "2021-06-07T21:55:15.143900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATL</th>\n",
       "      <th>BOS</th>\n",
       "      <th>BKN</th>\n",
       "      <th>CHA</th>\n",
       "      <th>CHI</th>\n",
       "      <th>CLE</th>\n",
       "      <th>DAL</th>\n",
       "      <th>DEN</th>\n",
       "      <th>DET</th>\n",
       "      <th>GSW</th>\n",
       "      <th>...</th>\n",
       "      <th>OKC</th>\n",
       "      <th>ORL</th>\n",
       "      <th>PHI</th>\n",
       "      <th>PHX</th>\n",
       "      <th>POR</th>\n",
       "      <th>SAC</th>\n",
       "      <th>SAT</th>\n",
       "      <th>TOR</th>\n",
       "      <th>UTAH</th>\n",
       "      <th>WAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ATL  BOS  BKN  CHA  CHI  CLE  DAL  DEN  DET  GSW  ...  OKC  ORL  PHI  PHX  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "3    0    1    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0  ...    0    0    0    0   \n",
       "\n",
       "   POR  SAC  SAT  TOR  UTAH  WAS  \n",
       "0    0    0    0    0     0    0  \n",
       "1    0    0    0    0     0    0  \n",
       "2    0    0    0    0     0    0  \n",
       "3    0    0    0    0     0    0  \n",
       "4    0    0    0    0     1    0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_team_mentions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.170252Z",
     "start_time": "2021-06-07T21:55:15.161778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ATL     1106\n",
       "BOS     2787\n",
       "BKN     1402\n",
       "CHA      712\n",
       "CHI     1421\n",
       "CLE     3871\n",
       "DAL     1117\n",
       "DEN      915\n",
       "DET      745\n",
       "GSW     5922\n",
       "HOU     2471\n",
       "IND      920\n",
       "LAC     1786\n",
       "LAL     3954\n",
       "MEM      804\n",
       "MIA     1677\n",
       "MIL     1441\n",
       "MIN     1222\n",
       "NOP     1200\n",
       "NYK     2186\n",
       "OKC     1990\n",
       "ORL     1172\n",
       "PHI     2313\n",
       "PHX     1043\n",
       "POR     1128\n",
       "SAC     1075\n",
       "SAT     1678\n",
       "TOR     2237\n",
       "UTAH    1023\n",
       "WAS     1296\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_team_mentions_df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.179760Z",
     "start_time": "2021-06-07T21:55:15.172538Z"
    }
   },
   "outputs": [],
   "source": [
    "#create list of sorted team names based on tweets\n",
    "twitter_team_names = list(tweets_team_mentions_df.sum().sort_values(ascending=False).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.631790Z",
     "start_time": "2021-06-07T21:55:15.186311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Most Mentioned Teams in Tweets from Various NBA Media Outlets')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=twitter_team_names, \n",
    "            y=tweets_team_mentions_df.sum().sort_values(ascending=False).values, \n",
    "           palette=team_colors)\n",
    "plt.xlabel('Team')\n",
    "plt.ylabel('Mention Count')\n",
    "plt.title('Most Mentioned Teams in Tweets from Various NBA Media Outlets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The team mentions are nearly identical to that of the ESPN headlines. Teams in big markets and with high winning percentages dominate the tweets from NBA media."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.646690Z",
     "start_time": "2021-06-07T21:55:15.635445Z"
    }
   },
   "outputs": [],
   "source": [
    "#nltk.download('vader_lexicon')\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.655588Z",
     "start_time": "2021-06-07T21:55:15.648640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Rockets look for repeat against Mavericks'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline_df.loc[3456, 'headlines']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.680587Z",
     "start_time": "2021-06-07T21:55:15.657724Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#NLTK sentiment\n",
    "sia_ = SentimentIntensityAnalyzer()\n",
    "test_string_headline = headline_df.loc[3456, 'headlines']\n",
    "sia_.polarity_scores(test_string_headline)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.735871Z",
     "start_time": "2021-06-07T21:55:15.684654Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.0, subjectivity=0.0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TextBlob Sentiment\n",
    "TextBlob(test_string_headline).sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.741743Z",
     "start_time": "2021-06-07T21:55:15.737917Z"
    }
   },
   "outputs": [],
   "source": [
    "def calc_nltk_sentiment(string):\n",
    "    \"\"\"Returns compound polarity score using SentimentIntensityAnalyzer from NLTK.\"\"\"\n",
    "    sia = SentimentIntensityAnalyzer()\n",
    "    return sia.polarity_scores(string)['compound']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:55:15.749613Z",
     "start_time": "2021-06-07T21:55:15.744638Z"
    }
   },
   "outputs": [],
   "source": [
    "def calc_textblob_sentiment(string):\n",
    "    \"\"\"Caclulates sentiment polarity using TextBlob library.\"\"\"\n",
    "    return TextBlob(string).sentiment.polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T21:59:50.621216Z",
     "start_time": "2021-06-07T21:55:15.752669Z"
    }
   },
   "outputs": [],
   "source": [
    "headline_nltk_vals = [calc_nltk_sentiment(x) for x in headline_list]\n",
    "headline_blob_vals = [calc_textblob_sentiment(x) for x in headline_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:15.128500Z",
     "start_time": "2021-06-07T21:59:50.623713Z"
    }
   },
   "outputs": [],
   "source": [
    "tweets_nltk_vals = [calc_nltk_sentiment(x) for x in tweet_list]\n",
    "tweets_blob_vals = [calc_textblob_sentiment(x) for x in tweet_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:15.178344Z",
     "start_time": "2021-06-07T22:15:15.130539Z"
    }
   },
   "outputs": [],
   "source": [
    "#create new columns with sentiment values for headlines and tweets\n",
    "headline_df['nltk_sentiment'] = headline_nltk_vals\n",
    "headline_df['textblob_sentiment'] = headline_blob_vals\n",
    "\n",
    "tweet_df['nltk_sentiment'] = tweets_nltk_vals\n",
    "tweet_df['textblob_sentiment'] = tweets_blob_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:15.192092Z",
     "start_time": "2021-06-07T22:15:15.180200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>headlines</th>\n",
       "      <th>posted_time</th>\n",
       "      <th>tokens</th>\n",
       "      <th>labels</th>\n",
       "      <th>four_labels</th>\n",
       "      <th>five_labels</th>\n",
       "      <th>nltk_sentiment</th>\n",
       "      <th>textblob_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wade matches King for 42nd on scoring list in win</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>['wade', 'match', 'king', '42nd', 'scoring', '...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5859</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chick Hearn's widow, Marge Hearn, dies at 98</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['chick', 'hearns', 'widow', 'marge', 'hearn',...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wittman to miss 2 games after brother's death</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['wittman', 'miss', '2', 'game', 'brother', 'd...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.6705</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LeBron atones for wardrobe mismatch vs. Spurs</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['lebron', 'atones', 'wardrobe', 'mismatch', '...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Source: Rockets petition to rescind Howard's t...</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>['source', 'rocket', 'petition', 'rescind', 'h...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           headlines posted_time  \\\n",
       "0  Wade matches King for 42nd on scoring list in win  2016-02-01   \n",
       "1       Chick Hearn's widow, Marge Hearn, dies at 98  2016-01-31   \n",
       "2      Wittman to miss 2 games after brother's death  2016-01-31   \n",
       "3      LeBron atones for wardrobe mismatch vs. Spurs  2016-01-31   \n",
       "4  Source: Rockets petition to rescind Howard's t...  2016-01-31   \n",
       "\n",
       "                                              tokens  labels  four_labels  \\\n",
       "0  ['wade', 'match', 'king', '42nd', 'scoring', '...       0            3   \n",
       "1  ['chick', 'hearns', 'widow', 'marge', 'hearn',...       0            3   \n",
       "2  ['wittman', 'miss', '2', 'game', 'brother', 'd...       0            3   \n",
       "3  ['lebron', 'atones', 'wardrobe', 'mismatch', '...       0            3   \n",
       "4  ['source', 'rocket', 'petition', 'rescind', 'h...       0            3   \n",
       "\n",
       "   five_labels  nltk_sentiment  textblob_sentiment  \n",
       "0            3          0.5859                 0.8  \n",
       "1            3          0.0000                 0.0  \n",
       "2            3         -0.6705                 0.0  \n",
       "3            4          0.0000                 0.0  \n",
       "4            3          0.0000                 0.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:15.670573Z",
     "start_time": "2021-06-07T22:15:15.194563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'TextBlob Sentiment Polarity Histogram for Headlines')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 8))\n",
    "axs[0].hist(x=headline_df['nltk_sentiment'], color='darkorange');\n",
    "axs[1].hist(x=headline_df['textblob_sentiment'], color='blue');\n",
    "\n",
    "axs[0].set_xlabel('Sentiment Polarity')\n",
    "axs[0].set_title('NLTK Sentiment Polarity Histogram for Headlines')\n",
    "axs[1].set_xlabel('Sentiment Polarity')\n",
    "axs[1].set_title('TextBlob Sentiment Polarity Histogram for Headlines')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sentiment polarity of headlines when using NLTK and TextBlob meets our expectations. The data is centered around 0, which means that ESPN headlines tend to be neutral. The polarity scale for both libraries ranges from -1 to 1 so I will append the the sentiment scores to the dataframes for both players and teams to see if we can identify any specific biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:15.685507Z",
     "start_time": "2021-06-07T22:15:15.672917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tweet</th>\n",
       "      <th>source</th>\n",
       "      <th>tweet_tokens</th>\n",
       "      <th>dual_labels</th>\n",
       "      <th>trip_labels</th>\n",
       "      <th>quad_labels</th>\n",
       "      <th>five_labels</th>\n",
       "      <th>nltk_sentiment</th>\n",
       "      <th>textblob_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-26 12:35:27</td>\n",
       "      <td>Obi Toppin is expected to participate in the N...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['obi', 'toppin', 'expected', 'participate', '...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.3818</td>\n",
       "      <td>-0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-25 23:08:17</td>\n",
       "      <td>MPJ SLAM</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['mpj', 'slam', '']</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.3818</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-25 17:48:50</td>\n",
       "      <td>Timberwolves G Malik Beasley has been suspende...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['timberwolves', 'g', 'malik', 'beasley', 'sus...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.6369</td>\n",
       "      <td>-0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-25 13:40:20</td>\n",
       "      <td>Luka's game-winner against the Celtics vs. Luk...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['lukas', 'gamewinner', 'celtic', 'v', 'lukas'...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-25 13:10:50</td>\n",
       "      <td>It's time to stop underrating the Utah Jazz Fr...</td>\n",
       "      <td>Yahoo</td>\n",
       "      <td>['time', 'stop', 'underrating', 'utah', 'jazz'...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.2960</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date                                              tweet  \\\n",
       "0  2021-02-26 12:35:27  Obi Toppin is expected to participate in the N...   \n",
       "1  2021-02-25 23:08:17                                          MPJ SLAM    \n",
       "2  2021-02-25 17:48:50  Timberwolves G Malik Beasley has been suspende...   \n",
       "3  2021-02-25 13:40:20  Luka's game-winner against the Celtics vs. Luk...   \n",
       "4  2021-02-25 13:10:50  It's time to stop underrating the Utah Jazz Fr...   \n",
       "\n",
       "  source                                       tweet_tokens  dual_labels  \\\n",
       "0  Yahoo  ['obi', 'toppin', 'expected', 'participate', '...            0   \n",
       "1  Yahoo                                ['mpj', 'slam', '']            1   \n",
       "2  Yahoo  ['timberwolves', 'g', 'malik', 'beasley', 'sus...            1   \n",
       "3  Yahoo  ['lukas', 'gamewinner', 'celtic', 'v', 'lukas'...            1   \n",
       "4  Yahoo  ['time', 'stop', 'underrating', 'utah', 'jazz'...            1   \n",
       "\n",
       "   trip_labels  quad_labels  five_labels  nltk_sentiment  textblob_sentiment  \n",
       "0            0            3            1         -0.3818              -0.100  \n",
       "1            2            1            2         -0.3818               0.000  \n",
       "2            2            1            2         -0.6369              -0.125  \n",
       "3            2            2            2          0.0000               0.000  \n",
       "4            2            1            2         -0.2960               0.000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:16.153356Z",
     "start_time": "2021-06-07T22:15:15.687792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'TextBlob Sentiment Polarity Histogram for NBA Media Tweets')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 8))\n",
    "axs[0].hist(x=tweet_df['nltk_sentiment'], color='slategray')\n",
    "axs[1].hist(x=tweet_df['textblob_sentiment'], color='peru')\n",
    "\n",
    "axs[0].set_xlabel('Sentiment Polarity')\n",
    "axs[0].set_title('NLTK Sentiment Polarity Histogram for NBA Media Tweets')\n",
    "axs[1].set_xlabel('Sentiment Polarity')\n",
    "axs[1].set_title('TextBlob Sentiment Polarity Histogram for NBA Media Tweets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The polarity score for the scraped tweets is also centered around 0 for each respective library. It's important to note that although we should expect these media sources to be fairly neutral, lots of context is missing by solely analyzing the text of a tweet. I chose to remove all non-ASCII characters and URLs. Non-ASCII characters like emojis wouldn't be interpreted by either of these libraries, but we as humans can quickly determine the sentiment that might be associated with an emoji.  The URLs included in scraped tweet data may have been an image, GIF, video, or another tweet (in the case of quoted tweets). We would have to develop an entirely separate model to try and interpret the data that might be linked to the URL and then translate that data into words that could easily be scored by NLTK or TextBlob. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:16.173694Z",
     "start_time": "2021-06-07T22:15:16.155734Z"
    }
   },
   "outputs": [],
   "source": [
    "tweets_team_mentions_df['source'] = tweet_df['source']\n",
    "tweets_player_mentions_df['source'] = tweet_df['source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:16.259741Z",
     "start_time": "2021-06-07T22:15:16.175649Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATL</th>\n",
       "      <th>BOS</th>\n",
       "      <th>BKN</th>\n",
       "      <th>CHA</th>\n",
       "      <th>CHI</th>\n",
       "      <th>CLE</th>\n",
       "      <th>DAL</th>\n",
       "      <th>DEN</th>\n",
       "      <th>DET</th>\n",
       "      <th>GSW</th>\n",
       "      <th>...</th>\n",
       "      <th>OKC</th>\n",
       "      <th>ORL</th>\n",
       "      <th>PHI</th>\n",
       "      <th>PHX</th>\n",
       "      <th>POR</th>\n",
       "      <th>SAC</th>\n",
       "      <th>SAT</th>\n",
       "      <th>TOR</th>\n",
       "      <th>UTAH</th>\n",
       "      <th>WAS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HoopsHype</th>\n",
       "      <td>247</td>\n",
       "      <td>420</td>\n",
       "      <td>329</td>\n",
       "      <td>133</td>\n",
       "      <td>290</td>\n",
       "      <td>536</td>\n",
       "      <td>239</td>\n",
       "      <td>161</td>\n",
       "      <td>201</td>\n",
       "      <td>715</td>\n",
       "      <td>...</td>\n",
       "      <td>300</td>\n",
       "      <td>329</td>\n",
       "      <td>406</td>\n",
       "      <td>282</td>\n",
       "      <td>219</td>\n",
       "      <td>278</td>\n",
       "      <td>353</td>\n",
       "      <td>332</td>\n",
       "      <td>210</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SB Nation</th>\n",
       "      <td>189</td>\n",
       "      <td>426</td>\n",
       "      <td>137</td>\n",
       "      <td>135</td>\n",
       "      <td>221</td>\n",
       "      <td>638</td>\n",
       "      <td>175</td>\n",
       "      <td>84</td>\n",
       "      <td>101</td>\n",
       "      <td>781</td>\n",
       "      <td>...</td>\n",
       "      <td>361</td>\n",
       "      <td>122</td>\n",
       "      <td>248</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>135</td>\n",
       "      <td>269</td>\n",
       "      <td>297</td>\n",
       "      <td>143</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>403</td>\n",
       "      <td>1397</td>\n",
       "      <td>499</td>\n",
       "      <td>313</td>\n",
       "      <td>658</td>\n",
       "      <td>2158</td>\n",
       "      <td>463</td>\n",
       "      <td>421</td>\n",
       "      <td>272</td>\n",
       "      <td>3554</td>\n",
       "      <td>...</td>\n",
       "      <td>1040</td>\n",
       "      <td>415</td>\n",
       "      <td>1187</td>\n",
       "      <td>404</td>\n",
       "      <td>546</td>\n",
       "      <td>388</td>\n",
       "      <td>812</td>\n",
       "      <td>1151</td>\n",
       "      <td>425</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slam</th>\n",
       "      <td>42</td>\n",
       "      <td>53</td>\n",
       "      <td>80</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>26</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Athletic</th>\n",
       "      <td>80</td>\n",
       "      <td>178</td>\n",
       "      <td>188</td>\n",
       "      <td>39</td>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "      <td>80</td>\n",
       "      <td>129</td>\n",
       "      <td>58</td>\n",
       "      <td>265</td>\n",
       "      <td>...</td>\n",
       "      <td>92</td>\n",
       "      <td>100</td>\n",
       "      <td>162</td>\n",
       "      <td>79</td>\n",
       "      <td>109</td>\n",
       "      <td>115</td>\n",
       "      <td>56</td>\n",
       "      <td>172</td>\n",
       "      <td>104</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yahoo</th>\n",
       "      <td>129</td>\n",
       "      <td>281</td>\n",
       "      <td>157</td>\n",
       "      <td>59</td>\n",
       "      <td>116</td>\n",
       "      <td>390</td>\n",
       "      <td>108</td>\n",
       "      <td>84</td>\n",
       "      <td>74</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>158</td>\n",
       "      <td>243</td>\n",
       "      <td>111</td>\n",
       "      <td>86</td>\n",
       "      <td>116</td>\n",
       "      <td>143</td>\n",
       "      <td>222</td>\n",
       "      <td>98</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ATL   BOS  BKN  CHA  CHI   CLE  DAL  DEN  DET   GSW  ...   OKC  \\\n",
       "source                                                             ...         \n",
       "HoopsHype     247   420  329  133  290   536  239  161  201   715  ...   300   \n",
       "SB Nation     189   426  137  135  221   638  175   84  101   781  ...   361   \n",
       "SI            403  1397  499  313  658  2158  463  421  272  3554  ...  1040   \n",
       "Slam           42    53   80   22   22    28   44   24   34    66  ...    34   \n",
       "The Athletic   80   178  188   39   97    87   80  129   58   265  ...    92   \n",
       "Yahoo         129   281  157   59  116   390  108   84   74   492  ...   150   \n",
       "\n",
       "              ORL   PHI  PHX  POR  SAC  SAT   TOR  UTAH  WAS  \n",
       "source                                                        \n",
       "HoopsHype     329   406  282  219  278  353   332   210  213  \n",
       "SB Nation     122   248  130  131  135  269   297   143  158  \n",
       "SI            415  1187  404  546  388  812  1151   425  598  \n",
       "Slam           30    48   29   29   32   26    45    28   55  \n",
       "The Athletic  100   162   79  109  115   56   172   104  145  \n",
       "Yahoo         158   243  111   86  116  143   222    98  112  \n",
       "\n",
       "[6 rows x 30 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_team_mentions_df.groupby('source').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-07T22:15:16.322110Z",
     "start_time": "2021-06-07T22:15:16.263044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LeBron</th>\n",
       "      <th>Steph</th>\n",
       "      <th>Harden</th>\n",
       "      <th>KD</th>\n",
       "      <th>Kyrie</th>\n",
       "      <th>Dame</th>\n",
       "      <th>Luka</th>\n",
       "      <th>Zion</th>\n",
       "      <th>Ja</th>\n",
       "      <th>Giannis</th>\n",
       "      <th>Jokic</th>\n",
       "      <th>Embiid</th>\n",
       "      <th>CP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HoopsHype</th>\n",
       "      <td>940</td>\n",
       "      <td>537</td>\n",
       "      <td>254</td>\n",
       "      <td>503</td>\n",
       "      <td>219</td>\n",
       "      <td>172</td>\n",
       "      <td>165</td>\n",
       "      <td>79</td>\n",
       "      <td>18</td>\n",
       "      <td>238</td>\n",
       "      <td>191</td>\n",
       "      <td>210</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SB Nation</th>\n",
       "      <td>750</td>\n",
       "      <td>520</td>\n",
       "      <td>220</td>\n",
       "      <td>405</td>\n",
       "      <td>216</td>\n",
       "      <td>99</td>\n",
       "      <td>42</td>\n",
       "      <td>52</td>\n",
       "      <td>21</td>\n",
       "      <td>154</td>\n",
       "      <td>49</td>\n",
       "      <td>120</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>3868</td>\n",
       "      <td>1747</td>\n",
       "      <td>857</td>\n",
       "      <td>1897</td>\n",
       "      <td>1129</td>\n",
       "      <td>366</td>\n",
       "      <td>365</td>\n",
       "      <td>600</td>\n",
       "      <td>132</td>\n",
       "      <td>802</td>\n",
       "      <td>221</td>\n",
       "      <td>572</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slam</th>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Athletic</th>\n",
       "      <td>277</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>160</td>\n",
       "      <td>79</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>34</td>\n",
       "      <td>133</td>\n",
       "      <td>39</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yahoo</th>\n",
       "      <td>728</td>\n",
       "      <td>414</td>\n",
       "      <td>228</td>\n",
       "      <td>397</td>\n",
       "      <td>218</td>\n",
       "      <td>140</td>\n",
       "      <td>113</td>\n",
       "      <td>74</td>\n",
       "      <td>39</td>\n",
       "      <td>179</td>\n",
       "      <td>40</td>\n",
       "      <td>161</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              LeBron  Steph  Harden    KD  Kyrie  Dame  Luka  Zion   Ja  \\\n",
       "source                                                                    \n",
       "HoopsHype        940    537     254   503    219   172   165    79   18   \n",
       "SB Nation        750    520     220   405    216    99    42    52   21   \n",
       "SI              3868   1747     857  1897   1129   366   365   600  132   \n",
       "Slam              26     15      11    55     27     7    14    20    4   \n",
       "The Athletic     277    105     105   160     79    70    45    95   34   \n",
       "Yahoo            728    414     228   397    218   140   113    74   39   \n",
       "\n",
       "              Giannis  Jokic  Embiid   CP  \n",
       "source                                     \n",
       "HoopsHype         238    191     210  184  \n",
       "SB Nation         154     49     120  117  \n",
       "SI                802    221     572  491  \n",
       "Slam               13     12      12   12  \n",
       "The Athletic      133     39      30   48  \n",
       "Yahoo             179     40     161  120  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_player_mentions_df.groupby('source').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-08T23:22:26.944973Z",
     "start_time": "2021-06-08T23:22:26.515024Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Sentiment Distribution - ESPN Headlines - NLTK vs. TextBlob')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(15, 8), sharex=True)\n",
    "sns.boxplot(x=headline_df['nltk_sentiment'], showfliers=False, ax=ax[0])\n",
    "sns.boxplot(x=headline_df['textblob_sentiment'], showfliers=False, ax=ax[1])\n",
    "ax[0].set_xlabel('Sentiment Polarity - NLTK')\n",
    "ax[1].set_xlabel('Sentiment Polarity - TextBlob')\n",
    "plt.suptitle('Sentiment Distribution - ESPN Headlines - NLTK vs. TextBlob', fontsize=14)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-08T23:06:41.765501Z",
     "start_time": "2021-06-08T23:06:41.356401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sentiment Distribution by Media Source - NLTK')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(y=tweet_df['source'], x=tweet_df['nltk_sentiment'], showfliers=False)\n",
    "plt.title('Sentiment Distribution by Media Source - NLTK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-13T16:59:15.085092Z",
     "start_time": "2021-06-13T16:59:14.823029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SI                 51315\n",
       "HoopsHype          37702\n",
       "SB Nation          14506\n",
       "Yahoo              13381\n",
       "The Athletic        6870\n",
       "Slam                2780\n",
       "Basketball News      477\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df['source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-13T17:07:05.209665Z",
     "start_time": "2021-06-13T17:07:04.990617Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Basketball News</th>\n",
       "      <td>477.0</td>\n",
       "      <td>0.084771</td>\n",
       "      <td>0.303467</td>\n",
       "      <td>-0.8481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.9062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HoopsHype</th>\n",
       "      <td>37702.0</td>\n",
       "      <td>0.120732</td>\n",
       "      <td>0.311955</td>\n",
       "      <td>-0.9245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>0.9911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SB Nation</th>\n",
       "      <td>14506.0</td>\n",
       "      <td>0.096938</td>\n",
       "      <td>0.357899</td>\n",
       "      <td>-0.9723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3818</td>\n",
       "      <td>0.9670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>51315.0</td>\n",
       "      <td>0.092218</td>\n",
       "      <td>0.347695</td>\n",
       "      <td>-0.9531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>0.9705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slam</th>\n",
       "      <td>2780.0</td>\n",
       "      <td>0.120629</td>\n",
       "      <td>0.379119</td>\n",
       "      <td>-0.9360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4019</td>\n",
       "      <td>0.9524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Athletic</th>\n",
       "      <td>6870.0</td>\n",
       "      <td>0.167369</td>\n",
       "      <td>0.387763</td>\n",
       "      <td>-0.9509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4939</td>\n",
       "      <td>0.9739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yahoo</th>\n",
       "      <td>13381.0</td>\n",
       "      <td>0.085438</td>\n",
       "      <td>0.327417</td>\n",
       "      <td>-0.9501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>0.9661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean       std     min  25%  50%     75%     max\n",
       "source                                                                        \n",
       "Basketball News    477.0  0.084771  0.303467 -0.8481  0.0  0.0  0.2500  0.9062\n",
       "HoopsHype        37702.0  0.120732  0.311955 -0.9245  0.0  0.0  0.3612  0.9911\n",
       "SB Nation        14506.0  0.096938  0.357899 -0.9723  0.0  0.0  0.3818  0.9670\n",
       "SI               51315.0  0.092218  0.347695 -0.9531  0.0  0.0  0.3612  0.9705\n",
       "Slam              2780.0  0.120629  0.379119 -0.9360  0.0  0.0  0.4019  0.9524\n",
       "The Athletic      6870.0  0.167369  0.387763 -0.9509  0.0  0.0  0.4939  0.9739\n",
       "Yahoo            13381.0  0.085438  0.327417 -0.9501  0.0  0.0  0.2960  0.9661"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df.groupby('source').describe()['nltk_sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-13T18:15:51.848674Z",
     "start_time": "2021-06-13T18:15:51.766473Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Negative Articles by NLTK: 21467\n",
      "Number of Positive Articles by NLTK: 46265\n",
      "Number of Neutral Articles by NLTK: 59299\n",
      "Percentage of Negative Articles: 16.899024647526982%\n",
      "Percentage of Positive Articles: 36.420243877478725%\n",
      "Percentage of Neutral Articles: 46.68073147499429%\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of Negative Articles by NLTK: {len(tweet_df.loc[tweet_df.nltk_sentiment < 0])}')\n",
    "print(f'Number of Positive Articles by NLTK: {len(tweet_df.loc[tweet_df.nltk_sentiment > 0])}')\n",
    "print(f'Number of Neutral Articles by NLTK: {len(tweet_df.loc[tweet_df.nltk_sentiment == 0])}')\n",
    "print(f'Percentage of Negative Articles: {len(tweet_df.loc[tweet_df.nltk_sentiment < 0])/len(tweet_df)*100}%')\n",
    "print(f'Percentage of Positive Articles: {len(tweet_df.loc[tweet_df.nltk_sentiment > 0])/len(tweet_df)*100}%')\n",
    "print(f'Percentage of Neutral Articles: {len(tweet_df.loc[tweet_df.nltk_sentiment == 0])/len(tweet_df)*100}%')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the 7 NBA media sources, Basketball News has the smallest sentiment polarity spread, while The Athletic has the largest. It's important to note that while Basketball News has the smallest spread, they also have the fewest number of articles in the dataset. For each media source, the first quartile is at 0, and the third quartile is always positive. Even though we don't have the full context, NLTK is classifying a significant majority of the articles as either neutral or positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-08T23:07:32.127876Z",
     "start_time": "2021-06-08T23:07:31.717743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sentiment Distribution by Media Source - TextBlob')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(y=tweet_df['source'], x=tweet_df['textblob_sentiment'], showfliers=False)\n",
    "plt.title('Sentiment Distribution by Media Source - TextBlob')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-13T17:07:24.089321Z",
     "start_time": "2021-06-13T17:07:23.828836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Basketball News</th>\n",
       "      <td>477.0</td>\n",
       "      <td>0.067073</td>\n",
       "      <td>0.274532</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HoopsHype</th>\n",
       "      <td>37702.0</td>\n",
       "      <td>0.137893</td>\n",
       "      <td>0.288078</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SB Nation</th>\n",
       "      <td>14506.0</td>\n",
       "      <td>0.083824</td>\n",
       "      <td>0.301815</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>51315.0</td>\n",
       "      <td>0.102100</td>\n",
       "      <td>0.284646</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slam</th>\n",
       "      <td>2780.0</td>\n",
       "      <td>0.094414</td>\n",
       "      <td>0.211722</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Athletic</th>\n",
       "      <td>6870.0</td>\n",
       "      <td>0.128684</td>\n",
       "      <td>0.253163</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yahoo</th>\n",
       "      <td>13381.0</td>\n",
       "      <td>0.072983</td>\n",
       "      <td>0.267874</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.156566</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean       std  min  25%   50%       75%  max\n",
       "source                                                                     \n",
       "Basketball News    477.0  0.067073  0.274532 -0.8  0.0  0.00  0.142857  1.0\n",
       "HoopsHype        37702.0  0.137893  0.288078 -1.0  0.0  0.00  0.285714  1.0\n",
       "SB Nation        14506.0  0.083824  0.301815 -1.0  0.0  0.00  0.200000  1.0\n",
       "SI               51315.0  0.102100  0.284646 -1.0  0.0  0.00  0.233333  1.0\n",
       "Slam              2780.0  0.094414  0.211722 -0.7  0.0  0.00  0.200000  1.0\n",
       "The Athletic      6870.0  0.128684  0.253163 -1.0  0.0  0.05  0.266667  1.0\n",
       "Yahoo            13381.0  0.072983  0.267874 -1.0  0.0  0.00  0.156566  1.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df.groupby('source').describe()['textblob_sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-13T18:14:25.407345Z",
     "start_time": "2021-06-13T18:14:25.148956Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Negative Articles by TextBlob: 17398\n",
      "Number of Positive Articles by TextBlob: 50146\n",
      "Number of Neutral Articles by TextBlob: 59487\n",
      "Percentage of Negative Articles: 13.695869512166322%\n",
      "Percentage of Positive Articles: 39.475403641630784%\n",
      "Percentage of Neutral Articles: 46.8287268462029%\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of Negative Articles by TextBlob: {len(tweet_df.loc[tweet_df.textblob_sentiment < 0])}')\n",
    "print(f'Number of Positive Articles by TextBlob: {len(tweet_df.loc[tweet_df.textblob_sentiment > 0])}')\n",
    "print(f'Number of Neutral Articles by TextBlob: {len(tweet_df.loc[tweet_df.textblob_sentiment == 0])}')\n",
    "print(f'Percentage of Negative Articles: {len(tweet_df.loc[tweet_df.textblob_sentiment < 0])/len(tweet_df)*100}%')\n",
    "print(f'Percentage of Positive Articles: {len(tweet_df.loc[tweet_df.textblob_sentiment > 0])/len(tweet_df)*100}%')\n",
    "print(f'Percentage of Neutral Articles: {len(tweet_df.loc[tweet_df.textblob_sentiment == 0])/len(tweet_df)*100}%')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distributions when using the TextBlob library are very similar to that of NLTK, however HoopsHype has the widest distribution of sentiment polarity based on the boxplots. TextBlob has also evaluated some article sentiments as completely negative, while the minimum value for NLTK was -0.95. Textblob also scored a smaller percentage of the total articles as negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_sentiment_df = tweets_player_mentions_df.join"
   ]
  }
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