nlp_features_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating NLP Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import seaborn as sns; sns.set_style('ticks')\n",
    "\n",
    "import nltk\n",
    "from nltk import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.probability import FreqDist\n",
    "from textblob import TextBlob\n",
    "from collections import Counter\n",
    "import re\n",
    "import string\n",
    "import emoji\n",
    "\n",
    "import gzip\n",
    "import pickle\n",
    "\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from gensim import matutils, models\n",
    "import scipy.sparse\n",
    "\n",
    "%load_ext autoreload\n",
    "\n",
    "%autoreload\n",
    "\n",
    "from functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# increase column width to more easily read tweets\n",
    "pd.set_option('max_colwidth', 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading csvs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan = pd.read_csv('Datasets/5k_jan_tweets.csv', index_col=0)\n",
    "feb = pd.read_csv('Datasets/5k_feb_tweets.csv', index_col=0)\n",
    "mar = pd.read_csv('Datasets/5k_mar_tweets.csv', index_col=0)\n",
    "apr = pd.read_csv('Datasets/5k_apr_tweets.csv', index_col=0)\n",
    "may = pd.read_csv('Datasets/5k_may_tweets.csv', index_col=0)\n",
    "months = [jan, feb, mar, apr, may]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(575949, 17)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat(months)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtaining target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate Vader sentiment analyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# create a column with the compound polarity score\n",
    "df['sentiment_score'] = df['tweet'].apply(lambda x: analyzer.polarity_scores(x)['compound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn this into our target classes: negative, positive, and neutral\n",
    "conditions = [\n",
    "    df.sentiment_score > .05,\n",
    "    df.sentiment_score < -.05\n",
    "]\n",
    "\n",
    "choices = [\n",
    "    'positive',\n",
    "    'negative'\n",
    "]\n",
    "\n",
    "df['sentiment'] = np.select(conditions, choices, 'neutral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(575949, 19)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>date</th>\n",
       "      <th>tweet</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>link</th>\n",
       "      <th>retweet</th>\n",
       "      <th>nlikes</th>\n",
       "      <th>nreplies</th>\n",
       "      <th>nretweets</th>\n",
       "      <th>quote_url</th>\n",
       "      <th>retweet_id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>sentiment_score</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.212523e+18</td>\n",
       "      <td>1.212523e+18</td>\n",
       "      <td>1.577923e+12</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>https://mltshp.com/p/1HLSB  \"This is the death mask of Thomas, an unknown peasant that died in a river.\"</td>\n",
       "      <td>[]</td>\n",
       "      <td>8.431951e+17</td>\n",
       "      <td>Best_of_MLTSHP</td>\n",
       "      <td>MLTSHP</td>\n",
       "      <td>https://twitter.com/Best_of_MLTSHP/status/1212523477896294400</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '843195118924546048', 'username': 'Best_of_MLTSHP'}]</td>\n",
       "      <td>-0.8176</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.212518e+18</td>\n",
       "      <td>1.212518e+18</td>\n",
       "      <td>1.577922e+12</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Ready to heard this? Homemade hair treatment and ann cherry dead sea mud mask... Fucking putas swear they are more woman than me.... Bitch shut th...</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.044739e+18</td>\n",
       "      <td>lumora_lu</td>\n",
       "      <td>MiLu</td>\n",
       "      <td>https://twitter.com/lumora_lu/status/1212518043353530368</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '1044738746913034241', 'username': 'lumora_lu'}]</td>\n",
       "      <td>-0.8905</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.212518e+18</td>\n",
       "      <td>1.212518e+18</td>\n",
       "      <td>1.577922e+12</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>We all know what a joke #CCPChina is when it comes to epidemic outbreak,think of how they stayed silent about #SARS.\\nDear world,stay away from #H...</td>\n",
       "      <td>['#ccpchina', '#sars', '#hk', '#hkers']</td>\n",
       "      <td>9.630325e+17</td>\n",
       "      <td>odiecher</td>\n",
       "      <td>odiecher</td>\n",
       "      <td>https://twitter.com/odiecher/status/1212517557690748928</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://twitter.com/WBYeats1865/status/1212430526826479616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '963032516759207936', 'username': 'odiecher'}]</td>\n",
       "      <td>0.6705</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.212516e+18</td>\n",
       "      <td>1.200523e+18</td>\n",
       "      <td>1.577921e+12</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Thought death masks gave you the horn freak</td>\n",
       "      <td>[]</td>\n",
       "      <td>2.819219e+09</td>\n",
       "      <td>Hevysmoker</td>\n",
       "      <td>Ashley Collins</td>\n",
       "      <td>https://twitter.com/Hevysmoker/status/1212515585562726401</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '2819219369', 'username': 'Hevysmoker'}, {'user_id': '1107018506300780544', 'username': 'cutedeadguys3'}]</td>\n",
       "      <td>-0.7783</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.212510e+18</td>\n",
       "      <td>1.212391e+18</td>\n",
       "      <td>1.577920e+12</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Interesting cult of death mask he’s wearing, 🤔\\nelectronically posing as he did as of the CIA while Brennan was Chief. 🧐</td>\n",
       "      <td>[]</td>\n",
       "      <td>2.361956e+08</td>\n",
       "      <td>FaithR8s</td>\n",
       "      <td>❌Patriot Fan🇺🇸🗽☕️🎶❌</td>\n",
       "      <td>https://twitter.com/FaithR8s/status/1212510409858670593</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '236195607', 'username': 'FaithR8s'}, {'user_id': '14745988', 'username': 'ThunderB'}, {'user_id': '14138785', 'username': 'Telegraph...</td>\n",
       "      <td>-0.2960</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  conversation_id    created_at        date  \\\n",
       "0  1.212523e+18     1.212523e+18  1.577923e+12  2020-01-01   \n",
       "1  1.212518e+18     1.212518e+18  1.577922e+12  2020-01-01   \n",
       "2  1.212518e+18     1.212518e+18  1.577922e+12  2020-01-01   \n",
       "3  1.212516e+18     1.200523e+18  1.577921e+12  2020-01-01   \n",
       "4  1.212510e+18     1.212391e+18  1.577920e+12  2020-01-01   \n",
       "\n",
       "                                                                                                                                                   tweet  \\\n",
       "0                                               https://mltshp.com/p/1HLSB  \"This is the death mask of Thomas, an unknown peasant that died in a river.\"   \n",
       "1  Ready to heard this? Homemade hair treatment and ann cherry dead sea mud mask... Fucking putas swear they are more woman than me.... Bitch shut th...   \n",
       "2  We all know what a joke #CCPChina is when it comes to epidemic outbreak,think of how they stayed silent about #SARS.\\nDear world,stay away from #H...   \n",
       "3                                                                                                            Thought death masks gave you the horn freak   \n",
       "4                               Interesting cult of death mask he’s wearing, 🤔\\nelectronically posing as he did as of the CIA while Brennan was Chief. 🧐   \n",
       "\n",
       "                                  hashtags       user_id        username  \\\n",
       "0                                       []  8.431951e+17  Best_of_MLTSHP   \n",
       "1                                       []  1.044739e+18       lumora_lu   \n",
       "2  ['#ccpchina', '#sars', '#hk', '#hkers']  9.630325e+17        odiecher   \n",
       "3                                       []  2.819219e+09      Hevysmoker   \n",
       "4                                       []  2.361956e+08        FaithR8s   \n",
       "\n",
       "                  name  \\\n",
       "0               MLTSHP   \n",
       "1                 MiLu   \n",
       "2             odiecher   \n",
       "3       Ashley Collins   \n",
       "4  ❌Patriot Fan🇺🇸🗽☕️🎶❌   \n",
       "\n",
       "                                                            link  retweet  \\\n",
       "0  https://twitter.com/Best_of_MLTSHP/status/1212523477896294400    False   \n",
       "1       https://twitter.com/lumora_lu/status/1212518043353530368    False   \n",
       "2        https://twitter.com/odiecher/status/1212517557690748928    False   \n",
       "3      https://twitter.com/Hevysmoker/status/1212515585562726401    False   \n",
       "4        https://twitter.com/FaithR8s/status/1212510409858670593    False   \n",
       "\n",
       "   nlikes  nreplies  nretweets  \\\n",
       "0     1.0       0.0        1.0   \n",
       "1     0.0       0.0        0.0   \n",
       "2     1.0       1.0        1.0   \n",
       "3     0.0       0.0        0.0   \n",
       "4     1.0       0.0        1.0   \n",
       "\n",
       "                                                    quote_url  retweet_id  \\\n",
       "0                                                         NaN         NaN   \n",
       "1                                                         NaN         NaN   \n",
       "2  https://twitter.com/WBYeats1865/status/1212430526826479616         NaN   \n",
       "3                                                         NaN         NaN   \n",
       "4                                                         NaN         NaN   \n",
       "\n",
       "                                                                                                                                                reply_to  \\\n",
       "0                                                                                      [{'user_id': '843195118924546048', 'username': 'Best_of_MLTSHP'}]   \n",
       "1                                                                                          [{'user_id': '1044738746913034241', 'username': 'lumora_lu'}]   \n",
       "2                                                                                            [{'user_id': '963032516759207936', 'username': 'odiecher'}]   \n",
       "3                                 [{'user_id': '2819219369', 'username': 'Hevysmoker'}, {'user_id': '1107018506300780544', 'username': 'cutedeadguys3'}]   \n",
       "4  [{'user_id': '236195607', 'username': 'FaithR8s'}, {'user_id': '14745988', 'username': 'ThunderB'}, {'user_id': '14138785', 'username': 'Telegraph...   \n",
       "\n",
       "   sentiment_score sentiment  \n",
       "0          -0.8176  negative  \n",
       "1          -0.8905  negative  \n",
       "2           0.6705  positive  \n",
       "3          -0.7783  negative  \n",
       "4          -0.2960  negative  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Take a look at our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    239200\n",
       "negative    221363\n",
       "neutral     115386\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sentiment.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    0.415315\n",
       "negative    0.384345\n",
       "neutral     0.200341\n",
       "Name: sentiment, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sentiment.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    575949.000000\n",
       "mean          0.017756\n",
       "std           0.505938\n",
       "min          -0.998400\n",
       "25%          -0.381800\n",
       "50%           0.000000\n",
       "75%           0.440400\n",
       "max           0.999600\n",
       "Name: sentiment_score, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sentiment_score.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to save\n",
    "# df.to_csv('Datasets/5k_jan_thru_may_tweets.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load file with sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (15) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(575949, 19)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uncomment to load\n",
    "# df = pd.read_csv('Datasets/5k_jan_thru_may_tweets.csv', index_col=0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert date column to datetime\n",
    "df['date'] = df.date.astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA/Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **See if number of likes has an effect on sentiment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentiment\n",
       "negative    105452\n",
       "neutral      49210\n",
       "positive    121327\n",
       "Name: tweet, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.nlikes > 0].groupby('sentiment')['tweet'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentiment\n",
       "negative    18052\n",
       "neutral      8223\n",
       "positive    21355\n",
       "Name: tweet, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.nlikes > 10].groupby('sentiment')['tweet'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Doesn't seem to affect much**\n",
    "\n",
    "- **How about month?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'] = df.date.apply(lambda x: x.month)\n",
    "df['month'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month  sentiment\n",
       "1      positive     0.412361\n",
       "       negative     0.409046\n",
       "       neutral      0.178592\n",
       "2      negative     0.421300\n",
       "       positive     0.401849\n",
       "       neutral      0.176851\n",
       "3      positive     0.430622\n",
       "       negative     0.387278\n",
       "       neutral      0.182100\n",
       "4      positive     0.405667\n",
       "       negative     0.346007\n",
       "       neutral      0.248327\n",
       "5      positive     0.417187\n",
       "       negative     0.393458\n",
       "       neutral      0.189355\n",
       "Name: sentiment, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('month').sentiment.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month  sentiment\n",
       "1      positive     0.412361\n",
       "       negative     0.409046\n",
       "       neutral      0.178592\n",
       "2      negative     0.421300\n",
       "       positive     0.401849\n",
       "       neutral      0.176851\n",
       "3      positive     0.430622\n",
       "       negative     0.387278\n",
       "       neutral      0.182100\n",
       "4      positive     0.405667\n",
       "       negative     0.346007\n",
       "       neutral      0.248327\n",
       "5      positive     0.417187\n",
       "       negative     0.393458\n",
       "       neutral      0.189355\n",
       "Name: sentiment, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('month').sentiment.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Not much effect here either**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning text\n",
    "#### Start with NLTK stopwords and add our own subject-specific ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# start with NLTK stop words\n",
    "stop_words = stopwords.words('english')\n",
    "\n",
    "# add search term words and hashtags to the list\n",
    "stop_words += ['mask', '#mask',\n",
    "               'masks', '#masks',\n",
    "               'face', '#face',\n",
    "               'facemask', '#facemask',\n",
    "               'facemasks', '#facemasks', \n",
    "               'covid', '#covid',\n",
    "               'covid19', '#covid19',\n",
    "               'corona', '#corona',\n",
    "               'coronavirus', '#coronavirus',\n",
    "               'rona', '#rona',\n",
    "               'virus', '#virus',\n",
    "               'viral', '#viral',\n",
    "               'viruses', '#viruses']\n",
    "\n",
    "# very common/conflicting stop words discovered during eda\n",
    "eda_stop_words = ['wear', 'wearing', 'get', 'one', 'people', 'us', 'like', 'n95',\n",
    "                   'go', 'going', 'would', 'surgical', 'via', 'also', 'use', 'even', 'know']\n",
    "\n",
    "stop_words += eda_stop_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create clean tweet column with our clean_text function\n",
    "#### Save as a series, because that'll be helpful later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7min 2s, sys: 2.83 s, total: 7min 5s\n",
      "Wall time: 7min 9s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0                                                                                                                  death thomas unknown peasant died river\n",
       "1                                                    ready heard homemade hair treatment ann cherry dead sea mud fucking putas swear woman bitch shut fuck\n",
       "2    joke #ccpchina come epidemic outbreakthink stayed silent #sars dear worldstay away #hk im sure infected amongst already dear fellow #hkers facewit...\n",
       "3                                                                                                                            thought death gave horn freak\n",
       "4                                                              interesting cult death thinkingface electronically posing cia brennan chief facewithmonocle\n",
       "Name: clean_tweet, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time df['clean_tweet'] = df['tweet'].apply(lambda x: clean_text(x, stop_words))\n",
    "\n",
    "# pull the clean tweet column\n",
    "clean_tweets = df['clean_tweet']\n",
    "clean_tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Save to pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to save\n",
    "# clean_tweets.to_pickle('Datasets/clean_tweets.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to load\n",
    "# clean_tweets = pd.read_pickle('Datasets/clean_tweets.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create sparse vector matrix, our main features, which will also help us with LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# regex pattern to vectorize by\n",
    "pattern = \"(#?[a-zA-Z]+[0-9]*(?:'[a-zx]+)?)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.87 s, sys: 206 ms, total: 8.08 s\n",
      "Wall time: 8.09 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#airlines</th>\n",
       "      <th>#amazon</th>\n",
       "      <th>#america</th>\n",
       "      <th>#americanairlines</th>\n",
       "      <th>#art</th>\n",
       "      <th>#besafe</th>\n",
       "      <th>#breaking</th>\n",
       "      <th>#breakingnews</th>\n",
       "      <th>#business</th>\n",
       "      <th>#california</th>\n",
       "      <th>...</th>\n",
       "      <th>yorkers</th>\n",
       "      <th>young</th>\n",
       "      <th>youre</th>\n",
       "      <th>youth</th>\n",
       "      <th>youtube</th>\n",
       "      <th>yr</th>\n",
       "      <th>zanyface</th>\n",
       "      <th>zero</th>\n",
       "      <th>zombie</th>\n",
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       "  </thead>\n",
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       "</table>\n",
       "<p>5 rows × 3712 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   #airlines  #amazon  #america  #americanairlines  #art  #besafe  #breaking  \\\n",
       "0          0        0         0                  0     0        0          0   \n",
       "1          0        0         0                  0     0        0          0   \n",
       "2          0        0         0                  0     0        0          0   \n",
       "3          0        0         0                  0     0        0          0   \n",
       "4          0        0         0                  0     0        0          0   \n",
       "\n",
       "   #breakingnews  #business  #california  ...  yorkers  young  youre  youth  \\\n",
       "0              0          0            0  ...        0      0      0      0   \n",
       "1              0          0            0  ...        0      0      0      0   \n",
       "2              0          0            0  ...        0      0      0      0   \n",
       "3              0          0            0  ...        0      0      0      0   \n",
       "4              0          0            0  ...        0      0      0      0   \n",
       "\n",
       "   youtube  yr  zanyface  zero  zombie  zone  \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       0     0  \n",
       "\n",
       "[5 rows x 3712 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a document-term matrix using CountVectorizer, using custom regex tokenizing pattern\n",
    "\n",
    "cv = CountVectorizer(min_df=250, token_pattern=pattern)\n",
    "%time data_cv = cv.fit_transform(clean_tweets)\n",
    "\n",
    "# save as a DataFrame\n",
    "data_vec = pd.DataFrame(data_cv.toarray(), columns=cv.get_feature_names())\n",
    "data_vec.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(575949, 3712)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_vec.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take a look at our list of words and unique vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'death thomas unknown peasant died river ready heard homemade hair treatment ann cherry dead sea mud '"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a big string\n",
    "\n",
    "big_string = ' '.join(clean_tweets)\n",
    "big_string[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 512 ms, sys: 175 ms, total: 687 ms\n",
      "Wall time: 693 ms\n"
     ]
    }
   ],
   "source": [
    "# split it into words\n",
    "%time words = big_string.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Number of words in our corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7793391"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Total unique vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "243080"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_vocab = set(words)\n",
    "len(total_vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Create a word list with corresponding frequencies in the corpus and look at the 25 most frequent words in our cleaned tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('hand', 40134),\n",
       " ('need', 39383),\n",
       " ('spread', 33418),\n",
       " ('protect', 32088),\n",
       " ('make', 31605),\n",
       " ('help', 30306),\n",
       " ('say', 28477),\n",
       " ('glove', 27649),\n",
       " ('public', 25717),\n",
       " ('hospital', 25525),\n",
       " ('new', 25340),\n",
       " ('time', 25085),\n",
       " ('medical', 24652),\n",
       " ('china', 24641),\n",
       " ('doctor', 24152),\n",
       " ('home', 23986),\n",
       " ('work', 23382),\n",
       " ('day', 22567),\n",
       " ('stop', 21958),\n",
       " ('trump', 21074),\n",
       " ('infected', 21008),\n",
       " ('please', 20635),\n",
       " ('everyone', 20359),\n",
       " ('health', 20312),\n",
       " ('death', 20287)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(words).most_common(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Create a similar object in dictionary format and convert to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'hand': 40134, 'need': 39383, 'spread': 33418, 'protect': 32088, 'make': 31605, 'help': 30306, 'say': 28477, 'glove': 27649, 'public': 25717, 'hospital': 25525, ...})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist = FreqDist(words)\n",
    "fdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hand</th>\n",
       "      <td>40134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>need</th>\n",
       "      <td>39383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread</th>\n",
       "      <td>33418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protect</th>\n",
       "      <td>32088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <td>31605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         word_count\n",
       "hand          40134\n",
       "need          39383\n",
       "spread        33418\n",
       "protect       32088\n",
       "make          31605"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts = pd.DataFrame(data=fdist.values(), index=fdist.keys(), columns=['word_count'])\n",
    "word_counts = word_counts.sort_values('word_count', ascending=False)\n",
    "word_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Take the top 25 words and create a chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "top25_words = word_counts.iloc[:25,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "plt.title('Top 25 Words', fontsize=30, pad=15)\n",
    "plt.ylabel('Word Count', fontsize=28, labelpad=15)\n",
    "plt.xticks(rotation=45, fontsize=22)\n",
    "plt.yticks(fontsize=22)\n",
    "plt.ylim(bottom=15000, top=41000)\n",
    "\n",
    "# plot bar chart with twitter blue ;)\n",
    "plt.bar(top25_words.index, top25_words.word_count, color='#38A1F3');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA\n",
    "#### Topic modeling!\n",
    "#### PLEASE NOTE: These are not our original topics. Unfortunately we lost those (except for a screenshot, which resides in our Images folder); we used those original topics for our presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one of the required inputs is a term-document matrix\n",
    "tdm = data_vec.transpose()\n",
    "tdm.head()\n",
    "\n",
    "# put the term-document matrix into a new gensim format, from df --> sparse matrix --> gensim corpus\n",
    "# TAKES AWHILE\n",
    "sparse_counts = scipy.sparse.csr_matrix(tdm)\n",
    "corpus = matutils.Sparse2Corpus(sparse_counts)\n",
    "\n",
    "# gensim also requires dictionary of the all terms and their respective location in the term-document matrix\n",
    "id2word = dict((v, k) for k, v in cv.vocabulary_.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Pickle the gensim corpus and vocab locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to save\n",
    "# with gzip.open('Datasets/gensim_corpus.pkl', 'wb') as c:\n",
    "#     pickle.dump(corpus, c, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "# with gzip.open('Datasets/gensim_id2word.pkl', 'wb') as i:\n",
    "#     pickle.dump(id2word, i, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to load\n",
    "# with gzip.open('gensim_corpus.pkl', 'rb') as c:\n",
    "#     corpus = pickle.load(c)\n",
    "    \n",
    "# with gzip.open('gensim_id2word.pkl', 'rb') as i:\n",
    "#     id2word = pickle.load(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Use corpus and dictionary of term locations, and set number of topics and passes to create an LDA model\n",
    "###### NOTE: keeping passes low due to the size of our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.058*\"facewithmedicalmask\" + 0.028*\"week\" + 0.023*\"today\" + 0.020*\"#staysafe\" + 0.019*\"order\" + 0.018*\"#lockdown\" + 0.016*\"employee\" + 0.015*\"crisis\" + 0.013*\"local\" + 0.013*\"box\"'),\n",
       " (1,\n",
       "  '0.064*\"new\" + 0.057*\"case\" + 0.049*\"death\" + 0.038*\"protest\" + 0.020*\"lockdown\" + 0.017*\"number\" + 0.017*\"fear\" + 0.015*\"news\" + 0.015*\"droplet\" + 0.013*\"country\"'),\n",
       " (2,\n",
       "  '0.052*\"home\" + 0.044*\"stay\" + 0.042*\"please\" + 0.040*\"safe\" + 0.040*\"keep\" + 0.039*\"hand\" + 0.023*\"take\" + 0.015*\"wash\" + 0.012*\"#socialdistancing\" + 0.012*\"sanitizer\"'),\n",
       " (3,\n",
       "  '0.045*\"trump\" + 0.014*\"business\" + 0.014*\"police\" + 0.013*\"fauci\" + 0.013*\"life\" + 0.011*\"say\" + 0.011*\"american\" + 0.011*\"rule\" + 0.010*\"positive\" + 0.010*\"second\"'),\n",
       " (4,\n",
       "  '0.052*\"social\" + 0.045*\"distancing\" + 0.034*\"spread\" + 0.030*\"protect\" + 0.021*\"others\" + 0.019*\"stop\" + 0.017*\"infected\" + 0.017*\"distance\" + 0.017*\"prevent\" + 0.016*\"help\"'),\n",
       " (5,\n",
       "  '0.025*\"state\" + 0.018*\"health\" + 0.016*\"worker\" + 0.014*\"patient\" + 0.012*\"medical\" + 0.012*\"care\" + 0.012*\"million\" + 0.012*\"community\" + 0.011*\"hospital\" + 0.011*\"testing\"'),\n",
       " (6,\n",
       "  '0.018*\"make\" + 0.015*\"cloth\" + 0.010*\"filter\" + 0.009*\"take\" + 0.009*\"stop\" + 0.009*\"good\" + 0.009*\"kill\" + 0.008*\"rollingonthefloorlaughing\" + 0.008*\"micron\" + 0.007*\"air\"'),\n",
       " (7,\n",
       "  '0.017*\"pandemic\" + 0.013*\"still\" + 0.013*\"think\" + 0.011*\"want\" + 0.010*\"thing\" + 0.010*\"day\" + 0.010*\"see\" + 0.010*\"without\" + 0.010*\"got\" + 0.009*\"store\"'),\n",
       " (8,\n",
       "  '0.044*\"risk\" + 0.034*\"u\" + 0.028*\"study\" + 0.023*\"reduce\" + 0.022*\"say\" + 0.021*\"eye\" + 0.020*\"nose\" + 0.019*\"mouth\" + 0.018*\"expert\" + 0.017*\"spread\"'),\n",
       " (9,\n",
       "  '0.018*\"test\" + 0.018*\"open\" + 0.013*\"riot\" + 0.013*\"public\" + 0.012*\"mandatory\" + 0.011*\"effective\" + 0.010*\"refuse\" + 0.010*\"foot\" + 0.009*\"glove\" + 0.009*\"week\"')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the topics\n",
    "lda = models.LdaModel(corpus=corpus, id2word=id2word, num_topics=10, passes=10)\n",
    "\n",
    "# view the topics\n",
    "lda.print_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Pickle it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('models/lda_model_10_topics_10_passes.pkl', 'wb') as handle:\n",
    "    pickle.dump(lda, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Assign each tweet to the topic to which it most closely fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_transformed = lda[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "575949"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.020016158),\n",
       " (1, 0.1597161),\n",
       " (2, 0.020016158),\n",
       " (3, 0.020018646),\n",
       " (4, 0.2196404),\n",
       " (5, 0.21993728),\n",
       " (6, 0.020016158),\n",
       " (7, 0.2806068),\n",
       " (8, 0.020016158),\n",
       " (9, 0.020016158)]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus_transformed[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [(0, 0.020016162), (1, 0.16171636), (2, 0.020016162), (3, 0.0200178), (4, 0.21964042), (5, 0.21993735), (6, 0.020016162), (7, 0.27860725), (8, 0.0...\n",
       "1                                                                                       [(5, 0.0974396), (6, 0.1636191), (7, 0.54697824), (9, 0.14908998)]\n",
       "2                                                                  [(0, 0.061181605), (1, 0.061553374), (2, 0.28873074), (4, 0.21947171), (7, 0.34124833)]\n",
       "3    [(0, 0.020004174), (1, 0.020013815), (2, 0.020004174), (3, 0.020006256), (4, 0.020004174), (5, 0.020004174), (6, 0.020005008), (7, 0.8199499), (8,...\n",
       "4    [(0, 0.014292977), (1, 0.23047026), (2, 0.014292977), (3, 0.014294527), (4, 0.15703824), (5, 0.014292977), (6, 0.15701887), (7, 0.014293624), (8, ...\n",
       "Name: lda_scores, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turn our LDA corpus into a column\n",
    "df['lda_scores'] = corpus_transformed\n",
    "df['lda_scores'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7\n",
       "1    7\n",
       "2    7\n",
       "3    7\n",
       "4    9\n",
       "Name: lda, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtain the topic that a resembles the tweet most\n",
    "df['lda'] = df['lda_scores'].apply(lambda x: lda_getter(x))\n",
    "df['lda'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's see if there's any discernable relationship between topics and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sort by percentage of trees in good health\n",
    "days = df.groupby('date').lda.value_counts(normalize=True).unstack()\n",
    "\n",
    "# plot a stacked bar graph\n",
    "p1 = days.plot.area(stacked = True, figsize=(10,6),\n",
    "#                    title = 'Tree Health Across Boroughs', \n",
    "                   alpha = .70, fontsize=14)\n",
    "\n",
    "p1.set_title('Topics Across Time', fontsize=20, pad=10)\n",
    "p1.set_xlabel('Date', fontsize=18, labelpad=10)\n",
    "p1.set_ylabel('Percentage of Tweets', fontsize=18, labelpad=10)\n",
    "p1.legend([f'lda_{i}' for i in range(10)], facecolor='white',\n",
    "          framealpha=1, bbox_to_anchor=(1.01, 1.0), loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cool looking graph but not super helpful\n",
    "##### Look at the relationship between sentiment and topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sort by percentage of trees in good health\n",
    "days = df.groupby('lda').sentiment.value_counts(normalize=True).unstack()\n",
    "\n",
    "# plot a stacked bar graph\n",
    "p1 = days.plot(kind='bar', stacked = True, figsize=(10,6),\n",
    "#                    title = 'Tree Health Across Boroughs', \n",
    "                   color = ['red', 'goldenrod', 'green'], alpha = .70, fontsize=14)\n",
    "\n",
    "p1.set_title('Tweet Sentiment by Topic', fontsize=20, pad=10)\n",
    "p1.set_xlabel('LDA Topic', fontsize=18, labelpad=10)\n",
    "p1.set_ylabel('Sentiment', fontsize=18, labelpad=10)\n",
    "p1.legend(['negative', 'neutral', 'positive'], facecolor='white', framealpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Some definite affect on sentiment based on tweet topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.058*\"facewithmedicalmask\" + 0.028*\"week\" + 0.023*\"today\" + 0.020*\"#staysafe\" + 0.019*\"order\" + 0.018*\"#lockdown\" + 0.016*\"employee\" + 0.015*\"crisis\" + 0.013*\"local\" + 0.013*\"box\"'),\n",
       " (1,\n",
       "  '0.064*\"new\" + 0.057*\"case\" + 0.049*\"death\" + 0.038*\"protest\" + 0.020*\"lockdown\" + 0.017*\"number\" + 0.017*\"fear\" + 0.015*\"news\" + 0.015*\"droplet\" + 0.013*\"country\"'),\n",
       " (2,\n",
       "  '0.052*\"home\" + 0.044*\"stay\" + 0.042*\"please\" + 0.040*\"safe\" + 0.040*\"keep\" + 0.039*\"hand\" + 0.023*\"take\" + 0.015*\"wash\" + 0.012*\"#socialdistancing\" + 0.012*\"sanitizer\"'),\n",
       " (3,\n",
       "  '0.045*\"trump\" + 0.014*\"business\" + 0.014*\"police\" + 0.013*\"fauci\" + 0.013*\"life\" + 0.011*\"say\" + 0.011*\"american\" + 0.011*\"rule\" + 0.010*\"positive\" + 0.010*\"second\"'),\n",
       " (4,\n",
       "  '0.052*\"social\" + 0.045*\"distancing\" + 0.034*\"spread\" + 0.030*\"protect\" + 0.021*\"others\" + 0.019*\"stop\" + 0.017*\"infected\" + 0.017*\"distance\" + 0.017*\"prevent\" + 0.016*\"help\"'),\n",
       " (5,\n",
       "  '0.025*\"state\" + 0.018*\"health\" + 0.016*\"worker\" + 0.014*\"patient\" + 0.012*\"medical\" + 0.012*\"care\" + 0.012*\"million\" + 0.012*\"community\" + 0.011*\"hospital\" + 0.011*\"testing\"'),\n",
       " (6,\n",
       "  '0.018*\"make\" + 0.015*\"cloth\" + 0.010*\"filter\" + 0.009*\"take\" + 0.009*\"stop\" + 0.009*\"good\" + 0.009*\"kill\" + 0.008*\"rollingonthefloorlaughing\" + 0.008*\"micron\" + 0.007*\"air\"'),\n",
       " (7,\n",
       "  '0.017*\"pandemic\" + 0.013*\"still\" + 0.013*\"think\" + 0.011*\"want\" + 0.010*\"thing\" + 0.010*\"day\" + 0.010*\"see\" + 0.010*\"without\" + 0.010*\"got\" + 0.009*\"store\"'),\n",
       " (8,\n",
       "  '0.044*\"risk\" + 0.034*\"u\" + 0.028*\"study\" + 0.023*\"reduce\" + 0.022*\"say\" + 0.021*\"eye\" + 0.020*\"nose\" + 0.019*\"mouth\" + 0.018*\"expert\" + 0.017*\"spread\"'),\n",
       " (9,\n",
       "  '0.018*\"test\" + 0.018*\"open\" + 0.013*\"riot\" + 0.013*\"public\" + 0.012*\"mandatory\" + 0.011*\"effective\" + 0.010*\"refuse\" + 0.010*\"foot\" + 0.009*\"glove\" + 0.009*\"week\"')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.print_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subjectivity\n",
    "##### Use TextBlob to calculate the subjectivity of tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.600000\n",
       "1    0.560000\n",
       "2    0.462963\n",
       "3    0.000000\n",
       "4    0.500000\n",
       "Name: subjectivity_score, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['subjectivity_score'] = df['tweet'].apply(lambda x: TextBlob(x).sentiment.subjectivity)\n",
    "df.subjectivity_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    575949.000000\n",
       "mean          0.370732\n",
       "std           0.290762\n",
       "min           0.000000\n",
       "25%           0.050000\n",
       "50%           0.397576\n",
       "75%           0.565000\n",
       "max           1.000000\n",
       "Name: subjectivity_score, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.subjectivity_score.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5c0a1cba8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.subjectivity_score.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Look at the relationship between subjectivity and sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x147c452b0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot('subjectivity_score', 'sentiment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### As expected, neutral tweets tend to be more objective, whereas positive and negative tweets are more subjective.\n",
    "\n",
    "###### How about between subjectivity and LDA topics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x147ea5cc0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot('subjectivity_score', 'lda')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Some difference here but nothing too telling, given the generalness of LDA topics.\n",
    "###### Make sure our DataFrame has all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'conversation_id', 'created_at', 'date', 'tweet', 'hashtags',\n",
       "       'user_id', 'username', 'name', 'link', 'retweet', 'nlikes', 'nreplies',\n",
       "       'nretweets', 'quote_url', 'retweet_id', 'reply_to', 'sentiment_score',\n",
       "       'sentiment', 'month', 'clean_tweet', 'lda_scores', 'lda',\n",
       "       'subjectivity_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save for good measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to save\n",
    "# df.to_csv('Datasets/5k_jan_thru_may_tweets_plus_features.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}