2. Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(213859, 4)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_pickle(\"./mobileCarriers.pkl\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>screen_name</th>\n",
       "      <th>date_time</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>leila_fong</td>\n",
       "      <td>2019-10-23 23:57:59</td>\n",
       "      <td>Illinois, USA</td>\n",
       "      <td>@ATTHelp I’m blocking, but then they change th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SnackEvangelist</td>\n",
       "      <td>2019-10-23 23:57:44</td>\n",
       "      <td></td>\n",
       "      <td>@ATTHelp Just logged into my account and it’s ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DeathboxTy</td>\n",
       "      <td>2019-10-23 23:54:34</td>\n",
       "      <td></td>\n",
       "      <td>@ATTHelp can’t get my Visual Voicemail to work...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLoLovesYou</td>\n",
       "      <td>2019-10-23 23:54:05</td>\n",
       "      <td>San Antonio, TX</td>\n",
       "      <td>@ATTHelp You were charging me $150 for an outd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATTHelp</td>\n",
       "      <td>2019-10-23 23:53:02</td>\n",
       "      <td></td>\n",
       "      <td>@leila_fong Hey, Leila, we're here to help. Cl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       screen_name           date_time         location  \\\n",
       "0       leila_fong 2019-10-23 23:57:59    Illinois, USA   \n",
       "1  SnackEvangelist 2019-10-23 23:57:44                    \n",
       "2       DeathboxTy 2019-10-23 23:54:34                    \n",
       "3      BLoLovesYou 2019-10-23 23:54:05  San Antonio, TX   \n",
       "4          ATTHelp 2019-10-23 23:53:02                    \n",
       "\n",
       "                                                text  \n",
       "0  @ATTHelp I’m blocking, but then they change th...  \n",
       "1  @ATTHelp Just logged into my account and it’s ...  \n",
       "2  @ATTHelp can’t get my Visual Voicemail to work...  \n",
       "3  @ATTHelp You were charging me $150 for an outd...  \n",
       "4  @leila_fong Hey, Leila, we're here to help. Cl...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed clock time:  0.008540990286403233  seconds\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "import time\n",
    "\n",
    "start_time = time.time() \n",
    "\n",
    "tokenizer = TweetTokenizer()\n",
    "tokenized_tweets = [tokenizer.tokenize(t) for t in df.text]\n",
    "stop_words = stopwords.words(\"english\")\n",
    "stop_words.extend([\"&amp;\", \"&gt;\", \"&lt;\"])\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "clean_text = []\n",
    "handles = []\n",
    "hashtags = []\n",
    "carrier = []\n",
    "# tweet_len = []\n",
    "for tweet in tokenized_tweets:\n",
    "    thandles = []\n",
    "    thashtags = []\n",
    "    joined_words = \" \"\n",
    "    words = [w.lower() for w in tweet if len(w)>2 and w not in stop_words]\n",
    "    thandles = [w for w in words if re.search(\"^@\\w+\", w)]\n",
    "    tlinks = [w for w in words if re.search(\"^https:\\w+\", w)]\n",
    "    thashtags = [w for w in words if re.search(\"^#\\w+\", w)]\n",
    "    words = [w for w in words if w.isalpha() and w not in thandles and w not in thashtags and w not in tlinks]\n",
    "    words = [lemmatizer.lemmatize(w) for w in words]\n",
    "    \n",
    "    joined_words = joined_words.join(words)\n",
    "    tlength = len(joined_words)\n",
    "    handles.append(thandles)\n",
    "    hashtags.append(thashtags)\n",
    "    clean_text.append(joined_words)\n",
    "#     tweet_len.append(tlength)\n",
    "    \n",
    "\n",
    "    \n",
    "print('Elapsed clock time: ', (time.time() - start_time)/3600, ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>screen_name</th>\n",
       "      <th>date_time</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>handles</th>\n",
       "      <th>hashtags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>leila_fong</td>\n",
       "      <td>2019-10-23 23:57:59</td>\n",
       "      <td>Illinois, USA</td>\n",
       "      <td>@ATTHelp I’m blocking, but then they change th...</td>\n",
       "      <td>blocking change number one digit switch unknown</td>\n",
       "      <td>[@atthelp]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SnackEvangelist</td>\n",
       "      <td>2019-10-23 23:57:44</td>\n",
       "      <td></td>\n",
       "      <td>@ATTHelp Just logged into my account and it’s ...</td>\n",
       "      <td>just logged account added how get rid</td>\n",
       "      <td>[@atthelp]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DeathboxTy</td>\n",
       "      <td>2019-10-23 23:54:34</td>\n",
       "      <td></td>\n",
       "      <td>@ATTHelp can’t get my Visual Voicemail to work...</td>\n",
       "      <td>get visual voicemail work suggestion tried tip...</td>\n",
       "      <td>[@atthelp]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLoLovesYou</td>\n",
       "      <td>2019-10-23 23:54:05</td>\n",
       "      <td>San Antonio, TX</td>\n",
       "      <td>@ATTHelp You were charging me $150 for an outd...</td>\n",
       "      <td>you charging outdated unlimited plan switched ...</td>\n",
       "      <td>[@atthelp]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATTHelp</td>\n",
       "      <td>2019-10-23 23:53:02</td>\n",
       "      <td></td>\n",
       "      <td>@leila_fong Hey, Leila, we're here to help. Cl...</td>\n",
       "      <td>hey leila help click learn protect unwanted call</td>\n",
       "      <td>[@leila_fong]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       screen_name           date_time         location  \\\n",
       "0       leila_fong 2019-10-23 23:57:59    Illinois, USA   \n",
       "1  SnackEvangelist 2019-10-23 23:57:44                    \n",
       "2       DeathboxTy 2019-10-23 23:54:34                    \n",
       "3      BLoLovesYou 2019-10-23 23:54:05  San Antonio, TX   \n",
       "4          ATTHelp 2019-10-23 23:53:02                    \n",
       "\n",
       "                                                text  \\\n",
       "0  @ATTHelp I’m blocking, but then they change th...   \n",
       "1  @ATTHelp Just logged into my account and it’s ...   \n",
       "2  @ATTHelp can’t get my Visual Voicemail to work...   \n",
       "3  @ATTHelp You were charging me $150 for an outd...   \n",
       "4  @leila_fong Hey, Leila, we're here to help. Cl...   \n",
       "\n",
       "                                          clean_text        handles hashtags  \n",
       "0    blocking change number one digit switch unknown     [@atthelp]       []  \n",
       "1              just logged account added how get rid     [@atthelp]       []  \n",
       "2  get visual voicemail work suggestion tried tip...     [@atthelp]       []  \n",
       "3  you charging outdated unlimited plan switched ...     [@atthelp]       []  \n",
       "4   hey leila help click learn protect unwanted call  [@leila_fong]       []  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clean_text\"] = clean_text\n",
    "df[\"handles\"] = handles\n",
    "df[\"hashtags\"] = hashtags\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add sentiment columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "polarity = [0.0]*len(df)\n",
    "subjectivity = [0.5]*len(df)\n",
    "sentiment = [\"\"]*len(df)\n",
    "\n",
    "for i in df.index:\n",
    "    blob = TextBlob(df.clean_text[i])\n",
    "    polarity[i] = blob.polarity\n",
    "    subjectivity[i] = blob.subjectivity\n",
    "    if polarity[i] > 0.0:\n",
    "        sentiment[i] = \"pos\"\n",
    "    elif polarity[i] < 0.0:\n",
    "        sentiment[i] = \"neg\"\n",
    "    else:\n",
    "        sentiment[i] = \"neu\"\n",
    "\n",
    "df[\"sentiment\"]    = sentiment\n",
    "df[\"polarity\"]     = polarity\n",
    "df[\"subjectivity\"] = subjectivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add \"Carrier\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carrier keywords: 'tmobile', 'att', 'verizon'\n",
    "# As long as a keyword appears in the user's tweet, \n",
    "# that tweet is considered to be in the description of that certain carrier.\n",
    "# One tweet can describe more than one carrier.\n",
    "\n",
    "carrier = []\n",
    "\n",
    "for text in df[['screen_name','text']].values:\n",
    "    screen_name = text[0].lower()\n",
    "    twitter_text = text[1].lower()\n",
    "    current_carrier = []\n",
    "    if 'tmobile' in twitter_text or 'tmobile' in screen_name:\n",
    "        current_carrier.append('tmobile')\n",
    "    if 'att' in twitter_text or 'att' in screen_name:\n",
    "        current_carrier.append('att')\n",
    "    if 'verizon' in twitter_text or 'verizon' in screen_name:\n",
    "        current_carrier.append('verizon')\n",
    "#     if len(current_carrier) == 0:\n",
    "#         current_carrier.append('None')\n",
    "    carrier.append(' '.join(current_carrier))\n",
    "\n",
    "df['carrier'] = carrier\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>carrier</th>\n",
       "      <th>location</th>\n",
       "      <th>date_time</th>\n",
       "      <th>clean_text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>att</td>\n",
       "      <td>Illinois, USA</td>\n",
       "      <td>2019-10-23 23:57:59</td>\n",
       "      <td>blocking change number one digit switch unknown</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>att</td>\n",
       "      <td></td>\n",
       "      <td>2019-10-23 23:57:44</td>\n",
       "      <td>just logged account added how get rid</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>att</td>\n",
       "      <td></td>\n",
       "      <td>2019-10-23 23:54:34</td>\n",
       "      <td>get visual voicemail work suggestion tried tip...</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>att</td>\n",
       "      <td>San Antonio, TX</td>\n",
       "      <td>2019-10-23 23:54:05</td>\n",
       "      <td>you charging outdated unlimited plan switched ...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>att</td>\n",
       "      <td></td>\n",
       "      <td>2019-10-23 23:53:02</td>\n",
       "      <td>hey leila help click learn protect unwanted call</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  carrier         location           date_time  \\\n",
       "0     att    Illinois, USA 2019-10-23 23:57:59   \n",
       "1     att                  2019-10-23 23:57:44   \n",
       "2     att                  2019-10-23 23:54:34   \n",
       "3     att  San Antonio, TX 2019-10-23 23:54:05   \n",
       "4     att                  2019-10-23 23:53:02   \n",
       "\n",
       "                                          clean_text sentiment  \n",
       "0    blocking change number one digit switch unknown       neg  \n",
       "1              just logged account added how get rid       neu  \n",
       "2  get visual voicemail work suggestion tried tip...       neu  \n",
       "3  you charging outdated unlimited plan switched ...       pos  \n",
       "4   hey leila help click learn protect unwanted call       neu  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['carrier','location','date_time','clean_text','sentiment']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171423, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop records that has null value in carrier culumn, which means that the tweet does not describe any carrier.\n",
    "\n",
    "df = df[df.carrier != '']\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# get the counts of each carrier\n",
    "\n",
    "c = df.carrier.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a clean text for classifier, clf_text, which remove the links and keep the stop words\n",
    "\n",
    "tokenized_tweets = [tokenizer.tokenize(t) for t in df.text]\n",
    "clf_text = []\n",
    "for tweet in tokenized_tweets:\n",
    "    joined_words = \" \"\n",
    "    words = [w.lower() for w in tweet if w.isalpha() or w[0]=='@']\n",
    "    tlinks = [w for w in words if re.search(\"^https:\\w+\", w)]\n",
    "    words = [w for w in words if  w not in tlinks]\n",
    "    joined_words = joined_words.join(words)\n",
    "    clf_text.append(joined_words)\n",
    "\n",
    "df[\"clf_text\"] = clf_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    @atthelp i m blocking but then they change the...\n",
       "1    @atthelp just logged into my account and it s ...\n",
       "2    @atthelp can t get my visual voicemail to work...\n",
       "3    @atthelp you were charging me for an outdated ...\n",
       "4    @leila_fong hey leila here to help click here ...\n",
       "Name: clf_text, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"clf_text\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    @ATTHelp I’m blocking, but then they change th...\n",
       "1    @ATTHelp Just logged into my account and it’s ...\n",
       "2    @ATTHelp can’t get my Visual Voicemail to work...\n",
       "3    @ATTHelp You were charging me $150 for an outd...\n",
       "4    @leila_fong Hey, Leila, we're here to help. Cl...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"text\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build 7 groups based on different Carrier\n",
    "\n",
    "- att_df: tweets that only mentioned ATT\n",
    "- ver_df: tweets that only mentioned Verizon\n",
    "- tmo_df: tweets that only mentioned TMobile\n",
    "- tmo_att_df: tweets that mentioned both TMobile and ATT\n",
    "- att_ver_df: tweets that mentioned both ATT and Verizon\n",
    "- tmo_ver_df: tweets that mentioned both TMobile and Verizon\n",
    "- tmo_att_ver_df: tweets that mentioned all TMobile, ATT and Verizon\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "att_df = df[df['carrier']=='att'][['carrier','sentiment','polarity','text','clf_text']]\n",
    "ver_df = df[df['carrier']=='verizon'][['carrier','sentiment','polarity','text','clf_text']]\n",
    "tmo_df = df[df['carrier']=='tmobile'][['carrier','sentiment','polarity','text','clf_text']]\n",
    "\n",
    "tmo_att_df = df[df['carrier']=='tmobile att'][['carrier','sentiment','polarity','text','clf_text']]\n",
    "att_ver_df = df[df['carrier']=='att verizon'][['carrier','sentiment','polarity','text','clf_text']]\n",
    "tmo_ver_df = df[df['carrier']=='tmobile verizon'][['carrier','sentiment','polarity','text','clf_text']]\n",
    "\n",
    "tmo_att_ver_df = df[df['carrier']=='tmobile att verizon'][['carrier','sentiment','polarity','text','clf_text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly sample 50 records for each group to form a new database\n",
    "# And try to make the ratio of pos to neg is 1\n",
    "\n",
    "only_att_pos = att_df[att_df['sentiment']=='pos'].sample(25)\n",
    "only_att_neg = att_df[att_df['sentiment']=='neg'].sample(25)\n",
    "only_ver_pos = ver_df[ver_df['sentiment']=='pos'].sample(25)\n",
    "only_ver_neg = ver_df[ver_df['sentiment']=='neg'].sample(25)\n",
    "only_tmo_pos = tmo_df[tmo_df['sentiment']=='pos'].sample(25)\n",
    "only_tmo_neg = tmo_df[tmo_df['sentiment']=='neg'].sample(25)\n",
    "\n",
    "tmo_att = tmo_att_df.sample(50)\n",
    "att_ver = att_ver_df.sample(50)\n",
    "tmo_ver = tmo_ver_df.sample(50)\n",
    "\n",
    "tmo_att_ver = tmo_att_ver_df.sample(50)\n",
    "\n",
    "\n",
    "clf_corpus = pd.concat([only_att_pos,only_att_neg,only_ver_pos,only_ver_neg,only_tmo_pos,only_tmo_neg,tmo_att,att_ver,tmo_ver,tmo_att_ver] ,ignore_index=True)\n",
    "clf_corpus.to_csv('clf_corpus.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1. Tweets count for per carrier and per group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "att                    82395\n",
       "verizon                47746\n",
       "tmobile                31842\n",
       "tmobile att             3400\n",
       "att verizon             3164\n",
       "tmobile verizon         2129\n",
       "tmobile att verizon      747\n",
       "Name: carrier, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "att_df = df[df['carrier']=='att'][['date_time','location','clean_text','carrier', 'sentiment', 'polarity', 'subjectivity']]\n",
    "verizon_df = df[df['carrier']=='verizon'][['date_time','location','clean_text','carrier', 'sentiment', 'polarity', 'subjectivity']]\n",
    "tmobile_df = df[df['carrier']=='tmobile'][['date_time','location','clean_text','carrier', 'sentiment', 'polarity', 'subjectivity']]\n",
    "\n",
    "tmobile_att_df = df[df['carrier']=='tmobile att'][['date_time','location','clean_text','carrier', 'sentiment', 'polarity', 'subjectivity']]\n",
    "att_verizon_df = df[df['carrier']=='att verizon'][['date_time','location','clean_text','carrier', 'sentiment', 'polarity', 'subjectivity']]\n",
    "tmobile_verizon_df = df[df['carrier']=='tmobile verizon'][['date_time','location','clean_text','carrier', 'sentiment', 'polarity', 'subjectivity']]\n",
    "\n",
    "tmobile_att_verizon_df = df[df['carrier']=='tmobile att verizon'][['date_time','location','clean_text','carrier', 'sentiment', 'polarity', 'subjectivity']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "att = pd.concat([att_df,tmobile_att_df,att_verizon_df,tmobile_att_verizon_df], sort=False)\n",
    "verizon = pd.concat([verizon_df,att_verizon_df,tmobile_verizon_df,tmobile_att_verizon_df], sort=False)\n",
    "tmobile = pd.concat([tmobile_df,tmobile_att_df,tmobile_verizon_df,tmobile_att_verizon_df], sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "carrier_counts = []\n",
    "carrier = ['ATT','Verizon','Tmobile']\n",
    "for i in (att, verizon, tmobile):\n",
    "    carrier_counts.append(len(i))\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.bar(carrier, carrier_counts, color=['lightskyblue','moccasin','yellowgreen'])\n",
    "plt.xlabel('carrier')\n",
    "plt.ylabel('tweets counts')\n",
    "plt.title('Tweets per carrier', fontsize=15)\n",
    "plt.subplot(122)\n",
    "group = ['ATT','Verizon','Tmobile', 'A&T', 'A&V','T&V','A&T&V']\n",
    "plt.bar(group, c, alpha=0.6)\n",
    "plt.xlabel('group')\n",
    "plt.ylabel('tweets counts')\n",
    "plt.title('Tweets per group', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "   As we can see from the graph, during the period from 2019-10-9 to 2019-10-23, ATT has the most tweets, and the carrier combination groups, tmo_att_df, att_ver_df, tmo_ver_df, tmo_att_ver_df, have much lower counts than single carrier group.\n",
    "   We can initially conclude that customers wants to churn with a clear goal is the minority."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2. Frequent description words for each carrier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import nltk\n",
    "\n",
    "def get5CommonWords(text):\n",
    "    words = nltk.tokenize.word_tokenize(text)\n",
    "    stop_list = ['https','att','tmobile','verizon','the','hey','get']\n",
    "    words = [w for w in words if w.isalpha() and w not in stop_list]\n",
    "    most_occur = Counter(words).most_common(5)\n",
    "    return most_occur\n",
    "    # return ', '.join([word[0] for word in most_occur])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "att_common_words = get5CommonWords(' '.join(att_df['clean_text'].values))\n",
    "verizon_common_words = get5CommonWords(' '.join(verizon_df['clean_text'].values))\n",
    "tmobile_common_words = get5CommonWords(' '.join(tmobile_df['clean_text'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATT : comment, day, help, still, like\n",
      "Verizon : customer, disney, free, service, year\n",
      "Tmobile : customer, love, see, want, ticket\n"
     ]
    }
   ],
   "source": [
    "def printFrequentWords(carrier, common_words):\n",
    "    word,count = zip(*common_words)\n",
    "    print(carrier, ':',', '.join(word))\n",
    "\n",
    "printFrequentWords('ATT', att_common_words)\n",
    "printFrequentWords('Verizon', verizon_common_words)\n",
    "printFrequentWords('Tmobile', tmobile_common_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def frequentWordsPlot(common_words):\n",
    "    word,count = zip(*common_words)\n",
    "    plt.bar(word,count,alpha=0.7)\n",
    "\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.subplot(131)\n",
    "frequentWordsPlot(att_common_words)\n",
    "plt.xlabel('words')\n",
    "plt.ylabel('counts')\n",
    "plt.title('ATT', fontsize=15)\n",
    "\n",
    "plt.subplot(132)\n",
    "frequentWordsPlot(verizon_common_words)\n",
    "plt.xlabel('words')\n",
    "plt.ylabel('counts')\n",
    "plt.title('Verizon', fontsize=15)\n",
    "\n",
    "plt.subplot(133)\n",
    "frequentWordsPlot(tmobile_common_words)\n",
    "plt.xlabel('words')\n",
    "plt.ylabel('counts')\n",
    "plt.title('Tmobile', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q3. Mention times for each carrier per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get the time series data\n",
    "# att_df['date_time'].dt.year\n",
    "# att_df['date_time'].dt.date\n",
    "# att_df['date_time'].dt.dayofweek.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getDateCounts(df):\n",
    "    date = df['date_time'].dt.date\n",
    "    df['date'] = date\n",
    "    size = df.groupby('date').size()\n",
    "    return size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# att_df.groupby('date').head(1)\n",
    "# att_df.groupby('date').describe()\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "getDateCounts(att).plot(label='ATT')\n",
    "getDateCounts(verizon).plot(label='Verizon')\n",
    "getDateCounts(tmobile).plot(label='Tmobile')\n",
    "plt.xticks(rotation=70, fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.xlabel('')\n",
    "plt.legend(prop={'size':15})\n",
    "plt.title('Tweets mentioned over time', fontsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q4. Sentiment Analysis for each carrier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a. Polarity distribution for 3 carriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(211)\n",
    "data = att_df[(att_df.polarity >= 0.05) | (att_df.polarity <= -0.05)]\n",
    "sns.distplot(data[\"polarity\"], kde=False, hist=True, label=\"ATT\")\n",
    "data = verizon_df[(verizon_df.polarity >= 0.05) | (verizon_df.polarity <= -0.05)]\n",
    "sns.distplot(data[\"polarity\"], kde=False, hist=True, label=\"Verizon\")\n",
    "data = tmobile_df[(tmobile_df.polarity >= 0.05) | (tmobile_df.polarity <= -0.05)]\n",
    "sns.distplot(data[\"polarity\"], kde=False, hist=True, label=\"Tmobile\")\n",
    "plt.legend(prop={\"size\": 12})\n",
    "plt.title(\"Sentiment Polarity of Three Carriers\")\n",
    "plt.ylabel(\"Tweet Count\")\n",
    "\n",
    "plt.subplot(212)\n",
    "data = att_df[(att_df.polarity >= 0.05) | (att_df.polarity <= -0.05)]\n",
    "sns.distplot(data[\"polarity\"], kde=True, hist=False, label=\"ATT\")\n",
    "data = verizon_df[(verizon_df.polarity >= 0.05) | (verizon_df.polarity <= -0.05)]\n",
    "sns.distplot(data[\"polarity\"], kde=True, hist=False, label=\"Verizon\")\n",
    "data = tmobile_df[(tmobile_df.polarity >= 0.05) | (tmobile_df.polarity <= -0.05)]\n",
    "sns.distplot(data[\"polarity\"], kde=True, hist=False, label=\"Tmobile\")\n",
    "plt.legend(prop={\"size\": 12})\n",
    "plt.title(\"Sentiment Polarity of Three Carriers\")\n",
    "plt.ylabel(\"Tweet Count\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b. Overall sentiments polarity for each carrier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>carrier</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATT</td>\n",
       "      <td>0.086422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Verizon</td>\n",
       "      <td>0.152118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tmobile</td>\n",
       "      <td>0.246736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carrier  sentiment\n",
       "0      ATT   0.086422\n",
       "1  Verizon   0.152118\n",
       "2  Tmobile   0.246736"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall Sentiment Analysis using TextBlob\n",
    "\n",
    "carrier = ['ATT', 'Verizon', 'Tmobile']\n",
    "sentiment = []\n",
    "\n",
    "for df in (att_df, verizon_df, tmobile_df):\n",
    "    data = TextBlob(' '.join(df['clean_text'].values)).sentiment.polarity\n",
    "    sentiment.append(data)\n",
    "\n",
    "\n",
    "data = pd.DataFrame({'carrier':carrier, 'sentiment':sentiment})\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JTwCoqje3q3pvAz7YRoX+Ft3/in8I/FFVTVy9+xFwSJJ96drWFcAfAQfQjXS5gq5r458zWvu6a5L30Y1i+Qy60TEXD8z/J+DMJEvprsStomtj9wY+WFXfGWEbg46hG/H5P5IcQXcP/R/QDQD2JroE7+3AMUk+DjwO+Gu6e+hnTFX9IMlHgS+0Y3IuXa+cR9MNHjZK8qs5yASu344A9gPeS9fl7ufAd+hGM7oUunuf0j0n5h+A19H987kK+D7wvrXZWFX9NMnr6b7EXkP3Bb1gRvbkjm38d5Ld6ZKho+nOgv2M7st0+Vqu7gN0CdsSuvuZ3kI3nHLRNW7b0N30fQLd72c6b6A7q/l5ui4Pb6Ub8Wsi9guT7NPW/QW655x9ui03VFVdkOQVdF0bn0d3/8IL6YZhXmsjHKMT6K72HTtC3/wJ3wX+g25U0vl0I1Pe3rDO5GdsYJ1vbzeOv5Yuwb6W7h+P61uVF9N9Pj5O1xXm3+gavUPWZXtriONbSZ5G99n5BF2y/VO6+0G8H0HSuE1ctfnWQK+Vs+naqEur6vZu6K175XPp7iX7e7oE6Jd0Jx7fM1DviCRX0LUjE4+tuZg73zP9Hrok41i6JPCNdO3LJnQn+ebTnfxbymjt6+voep98ge5+sH+qqg8PxHThwP8Gx9CdTJw4EXnpCOu/k9bDYs8W69voriZe1vaLqlqW5JV0j0x4EV1i9XyGd6dfX39B95ieg1ssvwb+i66Nk4bK+vdSk2a3JAvoGog/raoTxhvN+kn3cO0TgIdX1bQJcZLT6R5TMPmZZ5IkrZckj6C7EvisqvqPcccj9YVX4KQ5IMmD6UYnPRI4cZTkTZIkSXc/PkZAmhsW03VD/S1d10pJkiT1kF0oJUmSJKknvAInSZIkST1hAidJkiRJPXG3G8Rk6623rgULFow7DEnSRnDOOedcU1Xzxx1HX9hGStLcsKb28W6XwC1YsIBly5aNOwxJ0kaQ5KfjjqFPbCMlaW5YU/toF0pJkiRJ6gkTOEmSJEnqCRM4SZIkSeoJEzhJkiRJ6omRErgki5JcnGR5kkOHzP/bJD9Mcn6SU5I8ZGDerUm+315LZzJ4SZIkSZpLph2FMsk84P3As4AVwNlJllbVDweqnQcsrKobk7wKeAfwZ23eTVX1+BmOW5IkSZLmnFGuwO0GLK+qS6rqFuA4YL/BClV1WlXd2CbPBLab2TAlSZIkSaMkcNsCVwxMr2hlUzkY+NrA9GZJliU5M8n+6xCjJEmSJInRHuSdIWU1tGLyUmAh8McDxTtU1ZVJHgqcmuSCqvrJpOUWA4sBdthhh5EClyRJkqS5ZpQrcCuA7QemtwOunFwpyTOBfwT2raqbJ8qr6sr28xLgdGCXyctW1dFVtbCqFs6fP3+tdkCSJEmS5opRErizgZ2S7JhkU+AA4E6jSSbZBfgQXfJ29UD5lknu2d5vDewBDA5+IkmSJEka0bRdKKtqdZJDgJOAecCSqrowyRHAsqpaCvwLcB/gc0kALq+qfYFHAh9KchtdsnjkpNErJWmtfPjcXccdgppXPuGccYegdXDkeb8bdwhqDt3lHuMOQVIPjXIPHFV1InDipLI3D7x/5hTLfQd4zPoEKEmSJEnqjPQgb0mSJEnS+JnASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZI0RJK/SXJhkv9K8ukkmyXZMclZSX6c5DNJNm1179mml7f5CwbW88ZWfnGSvQfKF7Wy5UkO3fh7KEnqIxM4SZImSbIt8NfAwqp6NDAPOAA4Cnh3Ve0EXAsc3BY5GLi2qh4GvLvVI8nObblHAYuADySZl2Qe8H5gH2Bn4MBWV5KkNTKBkyRpuE2AzZNsAtwLuAp4OnB8m38ssH97v1+bps1/RpK08uOq6uaquhRYDuzWXsur6pKqugU4rtWVJGmNTOAkSZqkqn4GvBO4nC5xuw44B/hVVa1u1VYA27b32wJXtGVXt/pbDZZPWmaqckmS1sgETpKkSZJsSXdFbEfgwcC96bo7TlYTi0wxb23Lh8WyOMmyJMtWrlw5XeiSpFnOBE6SpLt6JnBpVa2sqt8BXwD+CLh/61IJsB1wZXu/AtgeoM2/H7BqsHzSMlOV30VVHV1VC6tq4fz582di3yRJPWYCJ0nSXV0O7J7kXu1etmcAPwROA17Q6hwEfLm9X9qmafNPrapq5Qe0USp3BHYCvgecDezURrXclG6gk6UbYb8kST23yfRVJEmaW6rqrCTHA+cCq4HzgKOBrwLHJfnnVnZMW+QY4BNJltNdeTugrefCJJ+lS/5WA6+uqlsBkhwCnEQ3wuWSqrpwY+2fJKm/TOAkSRqiqg4DDptUfAndCJKT6/4WeOEU63kb8LYh5ScCJ65/pJKkucQulJIkSZLUEyZwkiRJktQTJnCSJEmS1BMmcJIkSZLUEyZwkiRJktQTJnCSJEmS1BMmcJIkSZLUEyZwkiRJktQTJnCSJEmS1BMmcJIkSZLUEyZwkiRJktQTJnCSJEmS1BMjJXBJFiW5OMnyJIcOmf+3SX6Y5PwkpyR5yMC8g5L8uL0OmsngJUmSJGkumTaBSzIPeD+wD7AzcGCSnSdVOw9YWFWPBY4H3tGWfQBwGPAkYDfgsCRbzlz4kiRJkjR3jHIFbjdgeVVdUlW3AMcB+w1WqKrTqurGNnkmsF17vzdwclWtqqprgZOBRTMTuiRJkiTNLaMkcNsCVwxMr2hlUzkY+No6LitJkiRJmsImI9TJkLIaWjF5KbAQ+OO1WTbJYmAxwA477DBCSJIkSZI094xyBW4FsP3A9HbAlZMrJXkm8I/AvlV189osW1VHV9XCqlo4f/78UWOXJEmSpDlllATubGCnJDsm2RQ4AFg6WCHJLsCH6JK3qwdmnQTslWTLNnjJXq1MkiRJkrSWpu1CWVWrkxxCl3jNA5ZU1YVJjgCWVdVS4F+A+wCfSwJweVXtW1WrkryVLgkEOKKqVm2QPZEkSZKkWW6Ue+CoqhOBEyeVvXng/TPXsOwSYMm6BihJkiRJ6oz0IG9JkiRJ0viZwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSNESS+yc5PsmPklyU5MlJHpDk5CQ/bj+3bHWT5L1Jlic5P8kTBtZzUKv/4yQHDZTvmuSCtsx7k2Qc+ylJ6hcTOEmShnsP8PWqegTwOOAi4FDglKraCTilTQPsA+zUXouBDwIkeQBwGPAkYDfgsImkr9VZPLDcoo2wT5KknjOBkyRpkiRbAE8DjgGoqluq6lfAfsCxrdqxwP7t/X7Ax6tzJnD/JA8C9gZOrqpVVXUtcDKwqM3boqq+W1UFfHxgXZIkTckETpKku3oosBL4aJLzknwkyb2BB1bVVQDt5zat/rbAFQPLr2hlaypfMaRckqQ1MoGTJOmuNgGeAHywqnYBbuCO7pLDDLt/rdah/K4rThYnWZZk2cqVK9cctSRp1jOBkyTprlYAK6rqrDZ9PF1C94vW/ZH28+qB+tsPLL8dcOU05dsNKb+Lqjq6qhZW1cL58+ev105JkvrPBE6SpEmq6ufAFUn+sBU9A/ghsBSYGEnyIODL7f1S4GVtNMrdgetaF8uTgL2SbNkGL9kLOKnNuz7J7m30yZcNrEuSpCltMu4AJEnakNq9azdV1W1JHg48AvhaVf1umkVfA3wyyabAJcAr6E58fjbJwcDlwAtb3ROBZwPLgRtbXapqVZK3Ame3ekdU1ar2/lXAx4DNga+1lyRJa2QCJ0ma7b4JPLVdATsFWAb8GfCSNS1UVd8HFg6Z9YwhdQt49RTrWQIsGVK+DHj0dMFLkjTILpSSpNkuVXUj8HzgfVX1PGDnMcckSdI6MYGTJM12SfJkuituX21l9kCRJPWSCZwkabZ7LfBG4ItVdWGShwKnjTkmSZLWiWcgJUmz3QOrat+Jiaq6JMkZ4wxIkqR15RU4SdJs98YRyyRJutvzCpwkaVZKsg/d0P7bJnnvwKwtgNXjiUqSpPVjAidJmq2upHtkwL7AOQPl1wN/M5aIJElaTyMlcEkWAe8B5gEfqaojJ81/GvCvwGOBA6rq+IF5twIXtMnLB+9DkCRpQ6mqHwA/SPKpER7aLUlSL0ybwCWZB7wfeBawAjg7ydKq+uFAtcuBlwOvH7KKm6rq8TMQqzTcz04cdwSasO2zxx2BNMxuSQ4HHkLX7oXu2dsPHWtUkiStg1GuwO0GLK+qSwCSHAfsB9yewFXVZW3ebRsgRkmS1scxdF0mzwFuHXMskiStl1FGodwWuGJgekUrG9VmSZYlOTPJ/sMqJFnc6ixbuXLlWqxakqRpXVdVX6uqq6vqlxOvcQclSdK6GOUKXIaU1VpsY4equrI9OPXUJBdU1U/utLKqo4GjARYuXLg265YkaTqnJfkX4AvAzROFVXXu+EKSJGndjJLArQC2H5jejm5kr5FU1ZXt5yVJTgd2AX6yxoUkSZo5T2o/Fw6UFfD0McQiSdJ6GSWBOxvYKcmOwM+AA4AXj7LyJFsCN1bVzUm2BvYA3rGuwUqStLaq6k/GHYM0Ng70dffhQF+aIdMmcFW1OskhwEl0jxFYUlUXJjkCWFZVS5M8EfgisCXwp0neUlWPAh4JfKgNbvJ7wJGTRq+UJGmDSvLmYeVVdcTGjkWSpPU10nPgqupE4MRJZW8eeH82XdfKyct9B3jMesYoSdL6uGHg/WbAc4GLxhSLJEnrZaQETpKkvqqq/zs4neSdwNIxhSNJ0noZ5TECkiTNJvcCfIi3JKmXvAInSZrVklzAHY+/mQfMB7z/TZLUSyZwkqTZ7rkD71cDv6iq1eMKRpKk9WEXSknSrFZVPwXuD/wp8Dxg5/FGJEnSujOBkyTNakleC3wS2Ka9PpnkNeONSpKkdWMXSknSbHcw8KSqugEgyVHAd4H3jTUqSZLWgVfgJEmzXYBbB6ZvbWWSJPWOV+AkSbPdR4GzknyxTe8PHDPGeCRJWmcmcJKkWa2q3pXkdOApdFfeXlFV5403KkmS1o0JnCRpVkuyO3BhVZ3bpu+b5ElVddaYQ5Mkaa15D5wkabb7IPCbgekbWpkkSb1jAidJmu1SVTUxUVW3YQ8USVJPmcBJkma7S5L8dZJ7tNdrgUvGHZQkSevCBE6SNNv9FfBHwM+AFcCTgMVjjUiSpHVkFxJJ0qxWVVcDB4w7DkmSZoJX4CRJkiSpJ0zgJEmSJKknTOAkSZIkqSdM4CRJs1qS1ybZIp1jkpybZK9xxyVJ0rowgZMkzXZ/XlW/BvYC5gOvAI4cb0iSJK0bEzhJ0myX9vPZwEer6gcDZZIk9YoJnCRptjsnyTfoEriTktwXuG3MMUmStE58DpwkabY7GHg8cElV3ZhkK7pulJIk9Y5X4CRJs93JVXVuVf0KoKp+Cbx7zDFJkrROvAInSZqVkmwG3AvYOsmW3HHf2xbAg8cWmCRJ68EETpI0W/0l8Dq6ZO3cgfJfA+8fS0SSJK0nEzhJ0qxUVe8B3pPkNVX1vnHHI0nSTPAeOEnSbLckyZuSHA2QZKckzx13UJIkrQsTOEnSbLcEuAX4oza9Avjn8YUjSdK6M4GTJM12f1BV7wB+B1BVN+GDvCVJPWUCJ0ma7W5JsjlQAEn+ALh5vCFJkrRuHMREkjTbHQZ8Hdg+ySeBPYCXjzUiSZLWkVfgJEmzWlWdDO3Nn54AABKOSURBVDyfLmn7NLCwqk4fZdkk85Kcl+SENr1jkrOS/DjJZ5Js2srv2aaXt/kLBtbxxlZ+cZK9B8oXtbLlSQ6dqf2VJM1uJnCSpFktSYB9gF2r6gTgXkl2G3Hx1wIXDUwfBby7qnYCrgUObuUHA9dW1cOAd7d6JNkZOAB4FLAI+EBLCufRPYtuH2Bn4MBWV5KkNTKBkyTNdh8Angwc2KavZ4QHeSfZDngO8JE2HeDpwPGtyrHA/u39fm2aNv8Zrf5+wHFVdXNVXQosB3Zrr+VVdUlV3QIc1+pKkrRGJnCSpNnuSVX1auC3AFV1LbDpCMv9K/AG4LY2vRXwq6pa3aZXANu299sCV7T1rwaua/VvL5+0zFTld5FkcZJlSZatXLlyhLAlSbOZCZwkabb7XeuyODEK5XzuSMqGag/6vrqqzhksHlK1ppm3tuV3Law6uqoWVtXC+fPnryFqSdJc4CiUkqTZ7r3AF4FtkrwNeAHwpmmW2QPYN8mzgc2ALeiuyN0/ySbtKtt2wJWt/gpge2BFkk2A+wGrBsonDC4zVbkkSVPyCpwkaVarqk/SdYV8O3AVsH9VfW6aZd5YVdtV1QK6QUhOraqXAKfRJYAABwFfbu+Xtmna/FOrqlr5AW2Uyh2BnYDvAWcDO7VRLTdt21g6IzssSZrVvAInSZrVkhwBnAF8rKpuWM/V/W/guCT/DJwHHNPKjwE+kWQ53ZW3AwCq6sIknwV+CKwGXl1Vt7a4DgFOAuYBS6rqwvWMTZI0B5jASZJmu8voRqB8b5Lr6ZK5b1bVl9e4VNOeGXd6e38J3QiSk+v8FnjhFMu/DXjbkPITgRNHiUGSpAkmcJKkWa2qlgBLkvw+8CLg9cBi4L5jDUySZtCHz9113CGoeeUTzpm+0noY6R64JIuSXJxkeZJDh8x/WpJzk6xO8oJJ8w5K8uP2OmjyspIkbUhJPpLkO8AH6U5cvgDYcrxRSZK0bqZN4NrQy+8H9gF2Bg5MsvOkapcDLwc+NWnZBwCHAU+i63JyWBIbTUnSxrQV3X1mv6K7P+2agWe5SZLUK6N0odwNWN76/ZPkOGA/uhuyAaiqy9q8yc/V2Rs4uapWtfknA4uAT6935JIkjaCqngeQ5JF07dJpSeZV1XbjjUySpLU3SgK3LXDFwPQKuitqoxi27LYjLitJ0nprD+V+KvA0uq6Tp9INZCJJUu+MksBlSFmNuP6Rlk2ymO6GcnbYYYcRVy1J0kj2Ab4JvKeqfFi2JKnXRhnEZAWw/cD0dsCoDeBIy1bV0VW1sKoWzp8/f8RVS5I0kt9U1WcGk7ckR40zIEmS1tUoCdzZwE5JdkyyKd3DSZeOuP6TgL2SbNkGL9mrlUmStLE8a0jZPhs9CkmSZsC0CVwbqesQusTrIuCzVXVhkiOS7AuQ5IlJVtA9xPRDSS5sy64C3kqXBJ4NHDExoIkkSRtSklcluQB4RJLzB16XAuePOz5JktbFSA/yrqoTgRMnlb154P3ZdN0jhy27BFiyHjFKkrQuPgV8DXg7MPgM0+s9mShJ6quREjhJkvqmqq4DrgMOHHcskiTNlFHugZMkSZIk3Q2YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST8zaB3kfed7vxh2CmkN3uce4Q5AkSZJmBa/ASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkjRJku2TnJbkoiQXJnltK39AkpOT/Lj93LKVJ8l7kyxPcn6SJwys66BW/8dJDhoo3zXJBW2Z9ybJxt9TSVLfmMBJknRXq4G/q6pHArsDr06yM3AocEpV7QSc0qYB9gF2aq/FwAehS/iAw4AnAbsBh00kfa3O4oHlFm2E/ZIk9ZwJnCRJk1TVVVV1bnt/PXARsC2wH3Bsq3YssH97vx/w8eqcCdw/yYOAvYGTq2pVVV0LnAwsavO2qKrvVlUBHx9YlyRJUzKBkyRpDZIsAHYBzgIeWFVXQZfkAdu0atsCVwwstqKVral8xZBySZLWyAROkqQpJLkP8HngdVX16zVVHVJW61A+LIbFSZYlWbZy5crpQpYkzXImcJIkDZHkHnTJ2yer6gut+Bet+yPt59WtfAWw/cDi2wFXTlO+3ZDyu6iqo6tqYVUtnD9//vrtlCSp90ZK4JIsSnJxGynr0CHz75nkM23+Wa27CUkWJLkpyffb699nNnxJkmZeGxHyGOCiqnrXwKylwMRIkgcBXx4of1kbjXJ34LrWxfIkYK8kW7bBS/YCTmrzrk+ye9vWywbWJUnSlDaZrkKSecD7gWfRnTE8O8nSqvrhQLWDgWur6mFJDgCOAv6szftJVT1+huOWJGlD2gP4n8AFSb7fyv4BOBL4bJKDgcuBF7Z5JwLPBpYDNwKvAKiqVUneCpzd6h1RVava+1cBHwM2B77WXpIkrdG0CRzdsMfLq+oSgCTH0Y22NZjA7Qcc3t4fD/ybz7ORJPVVVX2L4fepATxjSP0CXj3FupYAS4aULwMevR5hSpLmoFG6UE41gtbQOlW1GrgO2KrN2zHJeUn+M8lTh23AG7QlSZIkaXqjJHCjjJQ1VZ2rgB2qahfgb4FPJdniLhW9QVuSJEmSpjVKAjfVCFpD6yTZBLgfsKqqbq6qXwJU1TnAT4CHr2/QkiRJkjQXjZLAnQ3slGTHJJsCB9CNtjVocFSuFwCnVlUlmd8GQSHJQ4GdgEtmJnRJkiRJmlumHcSkqlYnOYRuKOR5wJKqujDJEcCyqlpKN9TyJ5IsB1bRJXkATwOOSLIauBX4q4HRtyRJkiRJa2GUUSipqhPphkgeLHvzwPvfcsdQyoN1Pk/3EFRJkiRJ0noa6UHekiRJkqTxM4GTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeMIGTJEmSpJ4wgZMkSZKknjCBkyRJkqSeGCmBS7IoycVJlic5dMj8eyb5TJt/VpIFA/Pe2MovTrL3zIUuSVK/Tde+SpI02bQJXJJ5wPuBfYCdgQOT7Dyp2sHAtVX1MODdwFFt2Z2BA4BHAYuAD7T1SZI0p43YvkqSdCejXIHbDVheVZdU1S3AccB+k+rsBxzb3h8PPCNJWvlxVXVzVV0KLG/rkyRprhulfZUk6U5GSeC2Ba4YmF7RyobWqarVwHXAViMuK0nSXGQbKUlaa5uMUCdDymrEOqMsS5LFwOI2+ZskF48Q11ywNXDNuINYX28cdwD9MSuOt0YyK4714qFf8WvtITOxkp6yjVw/vf87sn0cWe+PtdZK74/3hm4fR0ngVgDbD0xvB1w5RZ0VSTYB7gesGnFZqupo4OgRYplTkiyrqoXjjkMbh8d77vBYq7GNXA/+Hc0dHuu5xeM9vVG6UJ4N7JRkxySb0g1KsnRSnaXAQe39C4BTq6pa+QFtlModgZ2A781M6JIk9doo7askSXcy7RW4qlqd5BDgJGAesKSqLkxyBLCsqpYCxwCfSLKc7srbAW3ZC5N8FvghsBp4dVXduoH2RZKk3piqfR1zWJKku7l0F8p0d5Rkces6oznA4z13eKyl9eff0dzhsZ5bPN7TM4GTJEmSpJ4Y5R44SZIkSdLdgAncmCV5XpJK8ogkj0ny/fZaleTS9v6UKcr/Y9zxz3VJTk+y96Sy1yX5wFqs48Qk95/56DRTkmw18Df48yQ/G5jedD3We3iS1w8pf3CS49v7PZOcsD7xS31k+9h/tpFzg23kxjfKYwS0YR0IfAs4oKoOBx4PkORjwAlVdfxg5anKNTafphu056SBsgOAv59uwSSh68b87A0Um2ZIVf2SO/42Dwd+U1Xv3IDbu5JuRF9pLrN97D/byDnANnLj8wrcGCW5D7AHcDBt5E71zvHAc5PcEyDJAuDBwLeS/H2Ss5Ocn+QtE/OTXNTOPp4LbJ/ksiRbJ/mrgTNWlyY5rS1zYJILkvxXkqMmNpzkN0neluQHSc5M8sCNvO9zXjueP0rykXZ8PpnkmUm+neTHSXZr9R6Q5Evts3BmkscOrOZxSU5t9V85sN7/GrK9eydZ0j5X5yXZbyPtqrRR2T7OGraRc5ht5IZjAjde+wNfr6r/BlYlecK4A9LaaWedvgcsakUHAJ8BnkX33MPd6M5K7Zrkaa3OHwIfr6pdquqnA+v696p6PPBEugf8vivJg4GjgKe39Twxyf5tkXsDZ1bV44BvAq/ccHuqNXgY8B7gscAjgBcDTwFeD/xDq/MW4Lyqemwr+/jA8o8FngM8GXhzO+ZT+Ue652w+EfgT4F+S3HsG90W6u7B9nAVsI4Vt5AZhAjdeBwLHtffHtWn1z0QXEdrPTwN7tdd5dGcRH0HXWAH8tKrOXMP63kP3BfQVuobq9KpaWVWrgU8CE43cLcBEv+9zgAUzsjdaW5dW1QVVdRtwIXBKdcP7XsAdx+QpwCcAqupUYKsk92vzvlxVN1XVNcBpdP/QTGUv4NAk3wdOBzYDdpjh/ZHuDmwfZw/byLnNNnID8B64MUmyFd0Zo0cnKbqHuFaSN5TPduibL9GdCXwCsHlVnZvkJcDbq+pDgxVb95EbplpRkpcDDwEOmShaw3Z/N/BZuRX/nsfl5oH3tw1M38Ydx2TYcaxJPyeXDxPgf1TVxWsbpNQXto+zjm3k3GYbuQF4BW58XkDXReAhVbWgqrYHLqU7C6Eeqarf0J3pWUJ3ZhG6G7b/vN3HQZJtk2yzpvUk2ZWuS8FL25kqgLOAP279/+fRnYX+z5nfC21g3wReAt2IWcA1VfXrNm+/JJu1f1r3BM5ew3pOAl6TJG1du2ywiKXxsX2cRWwjNQLbyLVkAjc+BwJfnFT2ebq+weqfTwOPo3X5qapvAJ8CvpvkArobue87zToOAR4AnNZu0v5IVV0FvJGu28APgHOr6ssbaB+04RwOLExyPnAkcNDAvO8BXwXOBN7aRteayluBewDntxu437phwpXGyvZx9rGN1Jocjm3kWom9ESRJkiSpH7wCJ0mSJEk9YQInSZIkST1hAidJkiRJPWECJ0mSJEk9YQInSZIkST1hAidJkqReSPLgJMePOw5pnHyMgCRJku52kmxSVaunmh5xHfOq6taZj04aHxM4SZIkbVBJXga8HijgfOCzwJuATYFfAi+pql8kORx4MLAAuAb4BvAcYDPg3sCfAydU1aOTzKN78POewD2B91fVh5LsCRwGXAU8vqp23jh7KW0cm4w7AEmSJM1eSR4F/COwR1Vdk+QBdInc7lVVSf4CeAPwd22RXYGnVNVNSV4OPBl4bFWtSrJgYNUHA9dV1ROT3BP4dpJvtHm7AY+uqks39P5JG5sJnCRJkjakpwPHV9U1AC0RewzwmSQPorsKN5hoLa2qmwamT66qVUPWuxfw2CQvaNP3A3YCbgG+Z/Km2cpBTCRJkrQhhe6K26D3Af9WVY8B/pKui+SEGybVnTw9uN7XVNXj22vHqvrGNMtIvWcCJ0mSpA3pFOBFSbYCaF0o7wf8rM0/aB3XexLwqiT3aOt9eJJ7r2+w0t2dXSglSZK0wVTVhUneBvxnkluB84DDgc8l+RlwJrDjOqz6I3SDnZybJMBKYP8ZCVq6G3MUSkmSJEnqCbtQSpIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk+YwEmSJElST5jASZIkSVJPmMBJkiRJUk/8fzB9Hu/nqO0OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.bar(data['carrier'], data['sentiment'],color=['lightskyblue','moccasin','yellowgreen'])\n",
    "plt.title('Sentiments polarity per carrier', fontsize=15)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(carrier, carrier_counts, color=['lightskyblue','moccasin','yellowgreen'])\n",
    "plt.xlabel('carrier')\n",
    "plt.ylabel('tweets counts')\n",
    "plt.title('Tweets per carrier', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "\n",
    "There is a interesting point that ATT has the most tweets, but get the worst polarity from users. And with the carrier tweets increaseing, the sentiment polarity is decreasing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c. Positve VS. Negative Tweets percentage for 3 carriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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>carrier</th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>percentage_negative</th>\n",
       "      <th>percentage_positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATT</td>\n",
       "      <td>13167</td>\n",
       "      <td>24642</td>\n",
       "      <td>0.348250</td>\n",
       "      <td>0.651750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Verizon</td>\n",
       "      <td>6620</td>\n",
       "      <td>19717</td>\n",
       "      <td>0.251357</td>\n",
       "      <td>0.748643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tmobile</td>\n",
       "      <td>3353</td>\n",
       "      <td>14547</td>\n",
       "      <td>0.187318</td>\n",
       "      <td>0.812682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carrier  negative  positive  percentage_negative  percentage_positive\n",
       "0      ATT     13167     24642             0.348250             0.651750\n",
       "1  Verizon      6620     19717             0.251357             0.748643\n",
       "2  Tmobile      3353     14547             0.187318             0.812682"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carrier = ['ATT', 'Verizon', 'Tmobile']\n",
    "negative_counts = []\n",
    "positive_counts = []\n",
    "\n",
    "for df in (att_df, verizon_df, tmobile_df):\n",
    "    counts = len(df[df.polarity < 0])\n",
    "    negative_counts.append(counts)\n",
    "    counts = len(df[df.polarity > 0])\n",
    "    positive_counts.append(counts)\n",
    "\n",
    "\n",
    "data = pd.DataFrame({'carrier':carrier, 'negative':negative_counts, 'positive':positive_counts})\n",
    "data['percentage_negative'] = data['negative']/(data['negative']+data['positive'])\n",
    "data['percentage_positive'] = data['positive']/(data['negative']+data['positive'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "plt.figure(figsize=(10,6))\n",
    "p1 = plt.bar(data['carrier'], data['percentage_negative'], color='r',label='negative',alpha=0.5)\n",
    "p2 = plt.bar(data['carrier'], data['percentage_positive'], bottom=data['percentage_negative'], color='g', label='positive',alpha=0.5)\n",
    "plt.legend()\n",
    "plt.xticks(fontsize=15)\n",
    "plt.title('Positve VS. Negative Tweets percentage per carrier', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "\n",
    "From this graph, we can explain the previous experiment that carrier with more tweets get lower polarity scores. That is because ATT has the most negative reviews ratio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q5. Representative vocabulary in both Positve Tweets and Negative Tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method1: Most frequent words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('help', 2918), ('thanks', 2431), ('live', 2301), ('first', 2273), ('love', 2177)]\n",
      "[('free', 4006), ('disney', 3376), ('customer', 3167), ('year', 2799), ('new', 2238)]\n",
      "[('love', 2157), ('thanks', 1306), ('new', 1271), ('want', 961), ('best', 867)]\n"
     ]
    }
   ],
   "source": [
    "# Frequent positive words for each carrier\n",
    "\n",
    "att_positive_common_words = get5CommonWords(' '.join(att_df[att_df['sentiment']=='pos']['clean_text'].values))\n",
    "tmobile_positive_common_words = get5CommonWords(' '.join(tmobile_df[tmobile_df['sentiment']=='pos']['clean_text'].values))\n",
    "verizon_positive_common_words = get5CommonWords(' '.join(verizon_df[verizon_df['sentiment']=='pos']['clean_text'].values))\n",
    "\n",
    "print(att_positive_common_words)\n",
    "print(verizon_positive_common_words)\n",
    "print(tmobile_positive_common_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('customer', 2742), ('like', 2387), ('tweet', 2344), ('sent', 2144), ('email', 2143)]\n",
      "[('service', 941), ('black', 571), ('phone', 559), ('galaxy', 441), ('samsung', 386)]\n",
      "[('customer', 441), ('service', 378), ('ticket', 294), ('even', 277), ('phone', 256)]\n"
     ]
    }
   ],
   "source": [
    "# Frequent negative words for each carrier\n",
    "\n",
    "att_negative_common_words = get5CommonWords(' '.join(att_df[att_df['sentiment']=='neg']['clean_text'].values))\n",
    "tmobile_negative_common_words = get5CommonWords(' '.join(tmobile_df[tmobile_df['sentiment']=='neg']['clean_text'].values))\n",
    "verizon_negative_common_words = get5CommonWords(' '.join(verizon_df[verizon_df['sentiment']=='neg']['clean_text'].values))\n",
    "\n",
    "print(att_negative_common_words)\n",
    "print(verizon_negative_common_words)\n",
    "print(tmobile_negative_common_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATT -- Pos  :  help, thanks, live, first, love\n",
      "ATT -- Neg  :  customer, like, tweet, sent, email\n",
      "----------\n",
      "Verizon -- Pos  :  free, disney, customer, year, new\n",
      "Verizon -- Neg  :  service, black, phone, galaxy, samsung\n",
      "----------\n",
      "Tmobile -- Pos  :  love, thanks, new, want, best\n",
      "Tmobile -- Pos  :  customer, service, ticket, even, phone\n"
     ]
    }
   ],
   "source": [
    "def getfrequentWords(carrier, common_words):\n",
    "    word,count = zip(*common_words)\n",
    "    print(carrier,' : ',', '.join(word))\n",
    "\n",
    "getfrequentWords('ATT -- Pos', att_positive_common_words)\n",
    "getfrequentWords('ATT -- Neg', att_negative_common_words)\n",
    "print('----------')\n",
    "getfrequentWords('Verizon -- Pos', verizon_positive_common_words)\n",
    "getfrequentWords('Verizon -- Neg', verizon_negative_common_words)\n",
    "print('----------')\n",
    "getfrequentWords('Tmobile -- Pos', tmobile_positive_common_words)\n",
    "getfrequentWords('Tmobile -- Pos', tmobile_negative_common_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method2: Get representative words by Topic Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import LdaModel, LdaMulticore\n",
    "from gensim.models import TfidfModel\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to list\n",
    "def topicKeywords(df):\n",
    "    tokens = []\n",
    "    for i, row in df.iterrows():\n",
    "        text = row[\"clean_text\"]\n",
    "        words = text.split()\n",
    "        words = [w for w in words if w.isalpha() and len(w)>2]\n",
    "        # only screen out NOUN words\n",
    "        uni_pos = nltk.pos_tag(words, tagset='universal')\n",
    "        words = [w[0] for w in uni_pos if w[1]=='NOUN']\n",
    "        tokens.append(words)\n",
    "\n",
    "    my_dict = Dictionary(tokens)\n",
    "    my_dict.filter_extremes(no_below=5, no_above=0.85)\n",
    "   \n",
    "    # Count Vectorization\n",
    "    dtm = [my_dict.doc2bow(doc) for doc in tokens]\n",
    "    \n",
    "    # TF-IDF Vectorization\n",
    "    tfidf = TfidfModel(dtm)\n",
    "    tfidf = tfidf[dtm]\n",
    "\n",
    "    lda_model = LdaModel(tfidf, num_topics=5, id2word=my_dict, passes=10)\n",
    "    return lda_model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "att - pos\n",
      "Topic 1 :  day, customer, reply, thing, time, wifi, year, work, att, service\n",
      "Topic 2 :  josh, att, career, total, attempt, thank, part, call, chance, post\n",
      "Topic 3 :  thanks, look, help, bill, phone, service, please, account, internet, people\n",
      "Topic 4 :  love, today, week, ticket, month, right, hand, fee, man, sunday\n",
      "Topic 5 :  episode, mini, series, offseason, court, star, player, lol, interview, http\n",
      "----------\n",
      "att - neg\n",
      "Topic 1 :  cause, tweet, concert, celebrate, tomorro, city, idea, life, towards, att\n",
      "Topic 2 :  game, help, everyone, employee, acc, vote, account, please, get, see\n",
      "Topic 3 :  people, fuck, game, att, trump, mean, look, company, news, night\n",
      "Topic 4 :  service, att, time, day, phone, hate, others, hour, internet, customer\n",
      "Topic 5 :  board, member, stephenson, sent, thousand, samuel, email, customer, ticket, shit\n",
      "----------\n",
      "verizon - pos\n",
      "Topic 1 :  phone, note, service, issue, area, thank, verizon, network, cable, data\n",
      "Topic 2 :  thanks, work, bill, day, reward, space, outage, gray, cut, watch\n",
      "Topic 3 :  sale, family, pixel, help, team, consultant, click, thing, please, verizon\n",
      "Topic 4 :  customer, year, month, disney, verizon, offer, wireless, plan, learn, code\n",
      "Topic 5 :  verizon, iphone, giveaway, copy, resicenters, anb, lol, smartphone, apple, chance\n",
      "----------\n",
      "verizon - neg\n",
      "Topic 1 :  iphone, end, sprint, date, fios, note, verizon, company, anyone, aura\n",
      "Topic 2 :  service, customer, year, cell, verizon, phone, center, experience, sorry, internet\n",
      "Topic 3 :  verizon, galaxy, samsung, shit, smartphone, midnight, block, auction, video, onyx\n",
      "Topic 4 :  hate, fuck, get, phone, today, verizon, yeah, bill, word, service\n",
      "Topic 5 :  call, issue, time, hour, outage, phone, day, data, service, month\n",
      "----------\n",
      "tmobile - pos\n",
      "Topic 1 :  love, lol, data, win, iphone, speed, tmobile, message, guy, sure\n",
      "Topic 2 :  hey, look, service, team, please, experience, area, help, thanks, issue\n",
      "Topic 3 :  see, chance, need, account, hello, carrier, service, friend, ticket, call\n",
      "Topic 4 :  thanks, thank, question, connection, phone, detail, family, today, part, bill\n",
      "Topic 5 :  customer, send, help, get, offer, deal, way, pixel, make, google\n",
      "----------\n",
      "tmobile - neg\n",
      "Topic 1 :  shit, sprint, area, coverage, look, hour, thing, data, breakdown, merger\n",
      "Topic 2 :  fuck, game, day, something, bill, tmobile, team, phone, aka, stunt\n",
      "Topic 3 :  customer, ticket, sucker, get, problem, sorry, issue, way, hey, rep\n",
      "Topic 4 :  mean, phone, plan, business, company, year, detail, blog, help, entrepreneur\n",
      "Topic 5 :  service, tmobile, hate, customer, experience, wtf, time, month, phone, help\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "carrier_df = (att_df, verizon_df, tmobile_df)\n",
    "sentiment = ['pos', 'neg']\n",
    "\n",
    "for d in carrier_df:\n",
    "    for s in sentiment:\n",
    "        model = topicKeywords(d[d['sentiment']==s])\n",
    "        print(d.carrier.values[0], '-', s)\n",
    "        for i in range(5):\n",
    "            wp = model.show_topic(i)\n",
    "            topic_keywords = \", \".join([word for word, prop in wp])\n",
    "            print('Topic',i+1, ': ', topic_keywords)\n",
    "        print('----------')\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "\n",
    "The topic modeling method should be better, because the term frequency of BOW model only considers the frequently appearing words in one total domain. However, the most common words are not necessarily representative. Topic modeling is good for dividing the text set into several topics, and these topics can represent several major themes that users love and complain about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                  75811\n",
       "Bellevue, WA                       4599\n",
       "United States                      4390\n",
       "New York, NY                       1856\n",
       "Dallas, TX                         1669\n",
       "USA                                1640\n",
       "Los Angeles, CA                    1527\n",
       "California, USA                    1440\n",
       "Texas, USA                         1084\n",
       "Florida, USA                       1078\n",
       "Chicago, IL                        1014\n",
       "Houston, TX                         985\n",
       "Atlanta, GA                         943\n",
       "Washington, DC                      908\n",
       "New York, USA                       773\n",
       "Knoxville, TN                       719\n",
       "New Jersey, USA                     660\n",
       "New York                            648\n",
       "Brooklyn, NY                        579\n",
       "Seattle, WA                         564\n",
       "San Francisco, CA                   525\n",
       "Miami, FL                           506\n",
       "Rapid City, SD--Black Hills         498\n",
       "Pennsylvania, USA                   498\n",
       "Las Vegas, NV                       485\n",
       "Boston, MA                          483\n",
       "Philadelphia, PA                    476\n",
       "North Carolina, USA                 464\n",
       "Michigan, USA                       443\n",
       "Nashville, TN                       442\n",
       "                                  ...  \n",
       "478...                                1\n",
       "740 N. Cluff Avenue, Lodi, CA         1\n",
       "วังทองหลาง, กรุงเทพมหานคร             1\n",
       "Boston Ma.                            1\n",
       "luv US, *805*,  AZ, FL                1\n",
       "Guadalajara, Jalisco Mexico           1\n",
       "𝐓𝐡𝐞 𝐖𝐨𝐫𝐥𝐝                             1\n",
       "abraham’s bosom                       1\n",
       "DeFectuoso                            1\n",
       "império jauregui 🏛️                   1\n",
       "between two Great Lakes               1\n",
       "Fresyes, CA                           1\n",
       "Probably somewhere drowning           1\n",
       "TN 🇺🇸🇺🇸🇺🇸🇺🇸🇺🇸🦅🦅🦅                      1\n",
       "Doney Park, AZ                        1\n",
       "beyond the wall                       1\n",
       "BK/BX                                 1\n",
       "Haunted Hills                         1\n",
       "Ms, Tx, Fl                            1\n",
       "Pearl Of The Orient                   1\n",
       "Unfortunately NY                      1\n",
       "Arlington, TN                         1\n",
       "Blue mom and ally stuck in UT         1\n",
       "Madisonville, KY                      1\n",
       "detailsbaejin heart                   1\n",
       "Asia/Europe                           1\n",
       "Wish A Nigga Woods                    1\n",
       "Soufeast, DC                          1\n",
       "Armada, Macomb, MI.                   1\n",
       "Santa Ana , CA                        1\n",
       "Name: location, Length: 33327, dtype: int64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "\n",
    "According to the location data, we can notice it is very messy. Some refer to the country, some refer to the state, and some refer to the city. Therefore, we think it is not worth to explore in depth."
   ]
  }
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