sentimentanalysisusingtextblob .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "from textblob import TextBlob\n",
    "from wordcloud import WordCloud\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumerKey='iL2KI650FefaLUj8X8yh0xKmo'\n",
    "consumerSecret='bHfmdHDc9wIifVW26Gw9XqAGhpS9XyRAe6HxxWXGzUDtkUf38o'\n",
    "accessToken='1195745298582106112-EZi82yH3RTahSCBMToXBFlERp7Fxqu'\n",
    "accessTokenSecret='Fjk9XgOT3y7nug3Z6VXNBQbXrD10HoXRIR52wVdbiGqR7'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "auth= tweepy.OAuthHandler(consumerKey,consumerSecret)\n",
    "\n",
    "auth.set_access_token(accessToken,accessTokenSecret)\n",
    "\n",
    "api=tweepy.API(auth,wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fetching the tweets\n",
    "#fetching the new tweets\n",
    "posts= tweepy.Cursor(api.search,q='coronavirus',lang=\"en\").items(200)\n",
    "i=1\n",
    "#fetching the old tweets\n",
    "oldposts=tweepy.Cursor(api.search,q='coronavirus',lang=\"en\", since='2020-04-01').items(200)\n",
    "#creating a data frame for old and new tweets\n",
    "for tweet in posts:\n",
    "    df= pd.DataFrame([tweet.text for tweet in posts],columns=['Tweets'])\n",
    "    oldf= pd.DataFrame([tweet.text for tweet in oldposts],columns=['Tweets'])\n"
   ]
  },
  {
   "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>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @TravelineCymru: Unfortunately we will not ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT @SkyNews: Coronavirus: Nottingham students ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jacob Rees-Mogg shames Andy Burnham's 'cheap p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A question of time? Priorities and considerati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RT @Joe_Mayes: The problem of a shortage of cu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets\n",
       "0  RT @TravelineCymru: Unfortunately we will not ...\n",
       "1  RT @SkyNews: Coronavirus: Nottingham students ...\n",
       "2  Jacob Rees-Mogg shames Andy Burnham's 'cheap p...\n",
       "3  A question of time? Priorities and considerati...\n",
       "4  RT @Joe_Mayes: The problem of a shortage of cu..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oldf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @vietthanhsports: Obama delivers blazing cr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT @nycsouthpaw: It was an even more serious f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coronavirus vaccine: When will it be ready and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @funder: Trump should be prosecuted for his...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White House task force document leaks highligh...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets\n",
       "0  RT @vietthanhsports: Obama delivers blazing cr...\n",
       "1  RT @nycsouthpaw: It was an even more serious f...\n",
       "2  Coronavirus vaccine: When will it be ready and...\n",
       "3  RT @funder: Trump should be prosecuted for his...\n",
       "4  White House task force document leaks highligh..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: Obama delivers blazing critique of Trump in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: It was an even more serious failure for you,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coronavirus vaccine: When will it be ready and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>: Trump should be prosecuted for his catastrop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White House task force document leaks highligh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>: Opinion: McConnell helps \"working families\" ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>: Is  getting it yet? \\n\\nCoronavirus live new...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Important contribution from   in   “The implem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>: AstraZeneca trial volunteer dies in Brazil, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Christmas Village will be back to JFK Plaza an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets\n",
       "0    : Obama delivers blazing critique of Trump in ...\n",
       "1    : It was an even more serious failure for you,...\n",
       "2    Coronavirus vaccine: When will it be ready and...\n",
       "3    : Trump should be prosecuted for his catastrop...\n",
       "4    White House task force document leaks highligh...\n",
       "..                                                 ...\n",
       "194  : Opinion: McConnell helps \"working families\" ...\n",
       "195  : Is  getting it yet? \\n\\nCoronavirus live new...\n",
       "196  Important contribution from   in   “The implem...\n",
       "197  : AstraZeneca trial volunteer dies in Brazil, ...\n",
       "198  Christmas Village will be back to JFK Plaza an...\n",
       "\n",
       "[199 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning the data\n",
    "def cleanTxt(text):\n",
    "    text=re.sub(r'@[A-Za-z0-9,.'']+','',text) #remove @mentions\n",
    "    text=re.sub(r'#','',text) #Removing the # symbol\n",
    "    text=re.sub(r'https?:\\/\\/\\S+','',text)#remove hyperlink\n",
    "    text=re.sub(r'RT[\\s]+','',text)#removing RT\n",
    "    \n",
    "    return text\n",
    "#cleaning the new data\n",
    "df['Tweets']=df['Tweets'].apply(cleanTxt)\n",
    "\n",
    "#show the cleaned text\n",
    "df"
   ]
  },
  {
   "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>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: Unfortunately we will not be able to update ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: Coronavirus: Nottingham students fined £40,0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jacob Rees-Mogg shames Andy Burnham's 'cheap p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A question of time? Priorities and considerati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_Mayes: The problem of a shortage of customs a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>: Coronavirus: 400,000 children in England off...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>: BREAK: Sophie Wilmès, until this month the P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>: BA parent company slashes passenger forecast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>: Following a phone call with the Health Secre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>: The news has barely mentioned it, but Big Ph...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets\n",
       "0    : Unfortunately we will not be able to update ...\n",
       "1    : Coronavirus: Nottingham students fined £40,0...\n",
       "2    Jacob Rees-Mogg shames Andy Burnham's 'cheap p...\n",
       "3    A question of time? Priorities and considerati...\n",
       "4    _Mayes: The problem of a shortage of customs a...\n",
       "..                                                 ...\n",
       "195  : Coronavirus: 400,000 children in England off...\n",
       "196  : BREAK: Sophie Wilmès, until this month the P...\n",
       "197  : BA parent company slashes passenger forecast...\n",
       "198  : Following a phone call with the Health Secre...\n",
       "199  : The news has barely mentioned it, but Big Ph...\n",
       "\n",
       "[200 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning the old data\n",
    "oldf['Tweets']=oldf['Tweets'].apply(cleanTxt)\n",
    "#printing\n",
    "oldf"
   ]
  },
  {
   "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>Tweets</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: Obama delivers blazing critique of Trump in ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: It was an even more serious failure for you,...</td>\n",
       "      <td>0.383333</td>\n",
       "      <td>-0.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coronavirus vaccine: When will it be ready and...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>: Trump should be prosecuted for his catastrop...</td>\n",
       "      <td>0.294643</td>\n",
       "      <td>-0.098214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White House task force document leaks highligh...</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>: Opinion: McConnell helps \"working families\" ...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>: Is  getting it yet? \\n\\nCoronavirus live new...</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.128788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Important contribution from   in   “The implem...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>: AstraZeneca trial volunteer dies in Brazil, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Christmas Village will be back to JFK Plaza an...</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets  Subjectivity  Polarity\n",
       "0    : Obama delivers blazing critique of Trump in ...      0.000000  0.000000\n",
       "1    : It was an even more serious failure for you,...      0.383333 -0.037500\n",
       "2    Coronavirus vaccine: When will it be ready and...      0.500000  0.350000\n",
       "3    : Trump should be prosecuted for his catastrop...      0.294643 -0.098214\n",
       "4    White House task force document leaks highligh...      0.300000 -0.200000\n",
       "..                                                 ...           ...       ...\n",
       "194  : Opinion: McConnell helps \"working families\" ...      0.750000  0.375000\n",
       "195  : Is  getting it yet? \\n\\nCoronavirus live new...      0.277778  0.128788\n",
       "196  Important contribution from   in   “The implem...      1.000000  0.400000\n",
       "197  : AstraZeneca trial volunteer dies in Brazil, ...      0.000000  0.000000\n",
       "198  Christmas Village will be back to JFK Plaza an...      0.285714  0.178571\n",
       "\n",
       "[199 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a function to get subjectivity (to tell its a opinion or not)\n",
    "def getSubjectivity(text):\n",
    "    return TextBlob(text).sentiment.subjectivity\n",
    "#create a function for polarity (to tell its +ve or -ve)\n",
    "def getPolarity(text):\n",
    "    return TextBlob(text).sentiment.polarity\n",
    "#create two new columns\n",
    "df['Subjectivity']=df['Tweets'].apply(getSubjectivity)\n",
    "df['Polarity']=df['Tweets'].apply(getPolarity)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Tweets</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: Obama delivers blazing critique of Trump in ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: It was an even more serious failure for you,...</td>\n",
       "      <td>0.383333</td>\n",
       "      <td>-0.037500</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coronavirus vaccine: When will it be ready and...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>: Trump should be prosecuted for his catastrop...</td>\n",
       "      <td>0.294643</td>\n",
       "      <td>-0.098214</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White House task force document leaks highligh...</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>: Opinion: McConnell helps \"working families\" ...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>: Is  getting it yet? \\n\\nCoronavirus live new...</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.128788</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Important contribution from   in   “The implem...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>: AstraZeneca trial volunteer dies in Brazil, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Christmas Village will be back to JFK Plaza an...</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets  Subjectivity  \\\n",
       "0    : Obama delivers blazing critique of Trump in ...      0.000000   \n",
       "1    : It was an even more serious failure for you,...      0.383333   \n",
       "2    Coronavirus vaccine: When will it be ready and...      0.500000   \n",
       "3    : Trump should be prosecuted for his catastrop...      0.294643   \n",
       "4    White House task force document leaks highligh...      0.300000   \n",
       "..                                                 ...           ...   \n",
       "194  : Opinion: McConnell helps \"working families\" ...      0.750000   \n",
       "195  : Is  getting it yet? \\n\\nCoronavirus live new...      0.277778   \n",
       "196  Important contribution from   in   “The implem...      1.000000   \n",
       "197  : AstraZeneca trial volunteer dies in Brazil, ...      0.000000   \n",
       "198  Christmas Village will be back to JFK Plaza an...      0.285714   \n",
       "\n",
       "     Polarity  Analysis  \n",
       "0    0.000000   Neutral  \n",
       "1   -0.037500  Negative  \n",
       "2    0.350000  Positive  \n",
       "3   -0.098214  Negative  \n",
       "4   -0.200000  Negative  \n",
       "..        ...       ...  \n",
       "194  0.375000  Positive  \n",
       "195  0.128788  Positive  \n",
       "196  0.400000  Positive  \n",
       "197  0.000000   Neutral  \n",
       "198  0.178571  Positive  \n",
       "\n",
       "[199 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getAnalysis(score):\n",
    "    if score <0:\n",
    "        return 'Negative'\n",
    "    elif score==0:\n",
    "        return 'Neutral'\n",
    "    else:\n",
    "        return 'Positive'\n",
    "df['Analysis']=df['Polarity'].apply(getAnalysis)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Tweets</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: Unfortunately we will not be able to update ...</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: Coronavirus: Nottingham students fined £40,0...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jacob Rees-Mogg shames Andy Burnham's 'cheap p...</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A question of time? Priorities and considerati...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_Mayes: The problem of a shortage of customs a...</td>\n",
       "      <td>0.088462</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>: Coronavirus: 400,000 children in England off...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>: BREAK: Sophie Wilmès, until this month the P...</td>\n",
       "      <td>0.262500</td>\n",
       "      <td>-0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>: BA parent company slashes passenger forecast...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>: Following a phone call with the Health Secre...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>: The news has barely mentioned it, but Big Ph...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets  Subjectivity  Polarity\n",
       "0    : Unfortunately we will not be able to update ...      0.812500  0.000000\n",
       "1    : Coronavirus: Nottingham students fined £40,0...      0.000000  0.000000\n",
       "2    Jacob Rees-Mogg shames Andy Burnham's 'cheap p...      0.850000  0.033333\n",
       "3    A question of time? Priorities and considerati...      0.000000  0.000000\n",
       "4    _Mayes: The problem of a shortage of customs a...      0.088462  0.000000\n",
       "..                                                 ...           ...       ...\n",
       "195  : Coronavirus: 400,000 children in England off...      0.200000  0.200000\n",
       "196  : BREAK: Sophie Wilmès, until this month the P...      0.262500 -0.062500\n",
       "197  : BA parent company slashes passenger forecast...      0.000000  0.000000\n",
       "198  : Following a phone call with the Health Secre...      0.500000  0.250000\n",
       "199  : The news has barely mentioned it, but Big Ph...      0.100000  0.025000\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oldf['Subjectivity']=oldf['Tweets'].apply(getSubjectivity)\n",
    "oldf['Polarity']=oldf['Tweets'].apply(getPolarity)\n",
    "\n",
    "oldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Tweets</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: Unfortunately we will not be able to update ...</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: Coronavirus: Nottingham students fined £40,0...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jacob Rees-Mogg shames Andy Burnham's 'cheap p...</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A question of time? Priorities and considerati...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>_Mayes: The problem of a shortage of customs a...</td>\n",
       "      <td>0.088462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>: Coronavirus: 400,000 children in England off...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>: BREAK: Sophie Wilmès, until this month the P...</td>\n",
       "      <td>0.262500</td>\n",
       "      <td>-0.062500</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>: BA parent company slashes passenger forecast...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>: Following a phone call with the Health Secre...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>: The news has barely mentioned it, but Big Ph...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets  Subjectivity  \\\n",
       "0    : Unfortunately we will not be able to update ...      0.812500   \n",
       "1    : Coronavirus: Nottingham students fined £40,0...      0.000000   \n",
       "2    Jacob Rees-Mogg shames Andy Burnham's 'cheap p...      0.850000   \n",
       "3    A question of time? Priorities and considerati...      0.000000   \n",
       "4    _Mayes: The problem of a shortage of customs a...      0.088462   \n",
       "..                                                 ...           ...   \n",
       "195  : Coronavirus: 400,000 children in England off...      0.200000   \n",
       "196  : BREAK: Sophie Wilmès, until this month the P...      0.262500   \n",
       "197  : BA parent company slashes passenger forecast...      0.000000   \n",
       "198  : Following a phone call with the Health Secre...      0.500000   \n",
       "199  : The news has barely mentioned it, but Big Ph...      0.100000   \n",
       "\n",
       "     Polarity  Analysis  \n",
       "0    0.000000   Neutral  \n",
       "1    0.000000   Neutral  \n",
       "2    0.033333  Positive  \n",
       "3    0.000000   Neutral  \n",
       "4    0.000000   Neutral  \n",
       "..        ...       ...  \n",
       "195  0.200000  Positive  \n",
       "196 -0.062500  Negative  \n",
       "197  0.000000   Neutral  \n",
       "198  0.250000  Positive  \n",
       "199  0.025000  Positive  \n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oldf['Analysis']=oldf['Polarity'].apply(getAnalysis)\n",
    "oldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGoCAYAAACkOfQWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde1zUVf4/8NfMIA6gMorKRWUAk9Y0oryUEngrKzXBkMRLu5WSTET92tJkZTPXMbzk7rbuhiZqN60kBTQrKyNFNAnKML+VKDBggAYKiIDIML8/kMvIDDAwM5+Z4fV8PHrseubzmc97jqO8Ped9zhGVl5drQERERGRjxEIHQERERGQKTHKIiIjIJjHJISIiIpvEJIeIiIhsEpMcIiIisklMcoiIiMgmMckh6uFkMhlmzpwpdBikg0qlgkwmg0KhMMvz0tLSIJPJEBcXZ5bnEZkakxwiI2loaMB7772HWbNmwdvbGwMHDsTw4cMxYcIEKBQK7N27V5C4ZDIZ7rzzTkGebU5xcXGQyWTYtWtXt97nrbfegkwmg0wmQ1ZWlpGiIyIh2AkdAJEtaGhowPz583Ho0CH069cPDz/8MDw8PFBRUYG8vDzs378fmZmZCA0NFTrUNjIyMuDg4CB0GBbjvffeg0gkgkajwc6dOzFmzBihQzKbMWPGICMjAy4uLkKHQmQUTHKIjOCTTz7BoUOHMHr0aBw8eBDOzs5ar9fW1uLEiRMCRdc+X19foUOwGMePH8evv/6KuXPnIjMzE/v27cPrr7+Ofv36CR2aWTg6OvL7QDaF01VERnDy5EkAwIIFC9okOAAglUoxZcoUnfcePXoU4eHhGD58OAYNGoTRo0fjpZdewsWLF9tcO3PmTMhkMqhUKuzcuRMTJ06Eq6srRowYgeeffx7l5eXN1zbVVwBAYWFh8xTMrTUeumpyWk/9pKam4pFHHsGQIUMwfPhwPPvss83P+fHHHxEWFga5XI4hQ4YgPDwcKpVK5+esqKjA2rVrMWHCBLi7u2Po0KF4+OGHkZyc3ObaptgVCgVUKhWefvpp+Pj4wNXVFZMmTcJnn33Wpl/Wr18PAIiKitL6rPri0eWdd94BACxatAgLFixAdXU19uzZo/PaXbt2NdevZGdn4/HHH4enpyfc3d3xyCOP4LvvvmtzT3FxMdatW4fp06fD19cXgwYNwp/+9CcsXrwYv/zyS6di/Mtf/gKZTIZjx47pfP3bb7+FTCbD4sWLm9tyc3Px/PPP4+6774abmxvkcjnGjx+PqKgoFBYWNl+nryans/cTWRomOURGMGDAAADA+fPnDbrv3//+N2bPno2TJ0/igQcegEKhwO23347t27djypQp+P3333Xet2rVKqxatQqjR4/GkiVL4O7ujvfeew8LFixovsbT0xOvvPIKAKBfv3545ZVXmv/rbKHx559/jvDwcLi6uuLJJ5+Eu7s7du/ejQULFuDkyZOYMWMGxGIxnnjiCfj5+eGLL77AvHnz0NDQoPU+RUVFmDp1KjZu3AiZTIYnn3wSoaGhUKlUePLJJ/UWuhYWFmLatGkoLCzEvHnzMGfOHPz6669YuHAhjhw50nzdggULEBAQAACYMWOG1mfVlXTqUl5ejv3792Po0KEICgrC/PnzIRaLsXPnznbvO3XqFB566CHU1dXhz3/+Mx566CGcPHkSwcHB+O2337SuPX78ON58803IZDLMnj0bCoUCY8aMQUpKCqZNm4bs7OwO41yyZAkA6I1rx44dAICnnnoKQGNiNXXqVOzevRu+vr545plnMH/+fPj6+uLAgQMdfme7ez+RkDhdRWQEjz76KP79739jx44dqKysxCOPPAJ/f394e3tDJBLpvCc9PR2rV6/GuHHjkJiY2DzqAgAfffQRIiMj8corr+CDDz5oc29WVhZOnDiBIUOGAADq6+vx6KOP4vjx48jMzMTYsWMhl8sRExOD9evXw9nZGTExMQZ/rkOHDuGzzz7DuHHjAAB1dXWYPHkyjh8/jrCwMLz99tt49NFHAQAajQZz587F4cOH8fnnn2slUgqFArm5uUhISMDcuXOb2ysrKzFr1ixs2LABM2fOhJ+fn9bzjx07hpUrV2LZsmXNbWFhYQgNDcXmzZsxadIkAMDChQtRUFCA9PR0zJw5EwsXLjT4s+7evRu1tbXNyc2wYcMQFBSEb7/9trlP9fXR1q1bMW/evOa2nTt34sUXX8TWrVvxz3/+s7k9KCgIZ8+eRd++fbXe49SpU5gxYwZWr17dYYF6YGAgRo4ciQMHDqC0tBQDBw5sfu3ixYv4/PPPcfvtt+P+++8HAKSkpKC8vBxr165FVFSU1ntdv34dN27caPd53b2fSEgcySEyAj8/P7z99tsYPHgwEhMT8fTTT+Oee+6Bl5cX5s2bh+TkZGg0Gq17tmzZAo1Gg3/9619aCQ4AhIeHw8/PD59//jkqKyvbPG/58uXNCQ4A2NnZYdGiRQCAH374wWifKywsrDnBAQB7e3uEhIQAAO66667mBAcARCIRwsLCAACnT59ubj9z5gyOHDmCmTNnaiU4QOMI04oVK6DRaJCYmNjm+Z6envjrX/+q1TZt2jQMGzbMqJ8TAN59912IRCKt0bCmZKm90ZwJEyZoJThA43SXnZ1dmxgHDRrUJsEBAH9/fwQGBuLYsWOdShoWL16Murq6Ngnw+++/jxs3bjSP4gCAWNz417yjo2Ob9+nduzf69OnT7rO6ez+RkDiSQ2Qkc+bMwaxZs5CWloYTJ07gzJkz+O6773Do0CEcOnQI06dPxwcffAB7e3sAjXU8dnZ2OHDgAA4cONDm/erq6qBWq5Gbmwt/f3+t1279NYDmpKd1XU533TqyAgBubm4AoHNZetNrRUVFzW1N9UpXr17VOS1VVlYGADh79myb1+68805IJJI27UOGDEFGRkZnPkKnpKen47fffkNAQAC8vb2b22fNmoV+/fohKSkJr7/+us6pL12/F7169cLgwYN1/l4cOnQIO3bswKlTp1BWVob6+nqt18vKypr7UZ958+Zh9erVeOedd/DCCy9AJBI1b2Hg6OiI8PDw5msfeeQRrFmzBsuWLcPXX3+NadOmYdy4cRg1alRzAtOe7t5PJCQmOURG1KtXL0ydOhVTp04F0Li0fP/+/YiKisKXX36JHTt2IDIyEgBw+fJl1NfXNxfM6lNVVdWmTddqn6ZkQK1Wd/djNNM16tD0nPZeaz0acfnyZQDAkSNHtOpobnXt2rU2bfpWNUkkkjZ1P93x7rvvAoDWKA4AODg4IDQ0FDt37sSePXsQERFhUIy3/l5s2bIFK1asgEwmw5QpUzBs2DBIpVKIRCIcPHgQP//8M65fv95hvH379kV4eDi2bduGb775BtOmTcPXX3+NgoICLFq0SGtkcNiwYfjmm2+wfv16fPXVVzh48CAAYPDgwXjmmWfw4osv6kwkjXU/kZCY5BCZkFgsRkhICH7++We88cYb+Pbbb5uTnH79+uHGjRs2vzqlKQlQKpV47rnnBI6mrStXriAlJQVA48qsW+tOmuzcuVNnktNZ9fX1iIuLg6urK44cOdJmtOb777836P0WL16Mbdu2YceOHZg2bVrzlNrTTz/d5toRI0YgISEBarUaZ86cwdGjR5GQkAClUomGhgYsX7683Wd1934ioTDJITKDplGP1nU548aNw6FDh3D69GmT7kgsFouNOuphqPHjxwMATpw4YdIkp6sjWbt378b169dx55136px6AoDU1FT83//9H77//nutGiVDlJWVoaKiAkFBQW0SnKqqKvz0008Gvd+f/vQnBAYG4osvvkBmZia+/PJL+Pv745577tF7j0QigZ+fH/z8/PDwww9j7Nix+PTTTzudpHT3fiJz44QqkRF88sknSE1N1ZlMXLx4Ee+99x4ANC9zBtA8YvD//t//07lU3FgbCLq4uKC0tBQ1NTXdfq+u8Pf3R0BAAD777DO8++67bQqwAeDcuXPdHtFq2qX3woULBt3XNFW1fv16bN68Wed/TclZR8vJ2zNo0CA4Ojrixx9/1JqCvHHjBlasWNFcm2SIJUuWQK1WY9GiRVCr1TpHcbKysnTuudTUJpVK231Gd+8nEhJHcoiMIDMzE1u2bIGrqyvuu+8+yOVyAI0HLH755ZeoqanB+PHjtaY7goKCsGbNGqxatQpjxozBgw8+CC8vL9TW1qKwsBDHjx+Hp6en3k3fOmvKlCnYs2cPQkNDMXHiRPTu3RujR4/GI4880q33NURCQgKCg4PxwgsvYOvWrRg3bhz69++PoqIi/Prrr8jOzsYHH3yAYcOGdfkZkyZNglgsxpYtW3DlyhUMHjwYAPDMM8/o3SsnPT0dZ8+eha+vLyZOnKj3vcPDw/Haa68hOTkZcXFxnd57pzWxWIylS5fiX//6FyZOnIgZM2bgxo0bSEtLw5UrVxAYGIi0tDSD3nPmzJnw8PBAUVER+vXrp/PYkMTERCQkJGDChAkYPnw4BgwYgMLCQnz22WcQi8V4/vnn231Gd+8nEhKTHCIjiI6OxogRI5qnNVJTU1FdXY3+/ftj/PjxCAkJwaJFi9CrV6829913333YsmULTpw4gS+++AJ9+vSBu7s7wsLC8Nhjj3U7tnXr1kEsFiM1NRUnT56EWq3G/PnzzZrkuLu7IzU1Fdu2bUNKSgr27t2LGzduYPDgwbjtttuwbt265n1duuq2227D9u3b8eabb+KDDz5oHrl6/PHH9SYlTTsc//nPf273vZs279uzZw8+/vhjPPPMM12KceXKlXBxccH777+Pd955B/369cPkyZMRGxvbpZO/7ezsMG/ePPzrX//CvHnz4OTk1OaauXPn4saNGzh58iRSUlJQXV0NV1dXPPTQQ4iKiurwbK7u3k8kJFF5eXnbsWMiIrIKc+bMQWpqKk6cOIGRI0cKHQ6RRWFNDhGRlTp16hRSU1Obd0EmIm2criIisjJvv/02iouL8dFHH0EkEiE2NlbokIgsEqeriIiszJ133onff/8d3t7eWL58eZtjJYioEZMcIiIiskmsySEiIiKbxCSHiIiIbBKTnC7KyckROgSLwb5owb5owb5owb5owb5owb5oYaq+YJJDRERENolJDhEREdkkJjlERERkk5jkEBERkU1ikkNEREQ2iUkOERER2SQmOURERGSTmOQQERGRTWKSQ0RERDaJSQ4RERHZJEGTnPT0dISHh2PkyJGQyWTYtWtXh/ecOXMGM2bMgJubG0aOHIn169dDozHfQeoqlQgREQ6IjPRFRIQDVCqR2Z5NluvS3nTUetwF33sfQK3HXbi0N13okHq0rL0F+MZDgZ9cQvCNhwJZewuEDoksXMbeIsz1OI1pLucx1+M0MvYWCR0SGYGdkA+/du0a7rjjDsyfPx+RkZEdXl9ZWYk5c+Zg4sSJ+Oabb5CTk4OoqCg4OjoiOjra5PGqVCKEhDghL08CwB5ZWUBmpgTJydcgl5sv0SLLcmlvOrwWB6MX6gEA/aorMGBxMPKRgsGhAQJH1/Nk7S2A5+I5uA3nGxuqgXOLM5CFJIwJ9RQ2OLJIGXuLsGSxAwpwf2NDNXB2cT4SUITxoR7CBkfdIuhIzvTp0/Hqq68iODgYYnHHoSQmJqKmpgbx8fG44447EBwcjBdeeAFvvfWWWUZzlErpzQSnRV6eBEql1OTPJsvVL/rZ5gSnSS/Uo1/0swJF1LNVRMe1JDg33YbzqIiOEygisnQbostQAC+ttgJ4YUN0mTABkdEIOpJjqIyMDEyYMAEODg7NbdOmTcPatWuhUqng5eWl8z5jnW6am+sLwF5He22PP022J39+39pyne0OteU424P7BRDmezGgVvc0w4DaYkG/pz35z8itLK0vrtQ66ml3MHmsltYXQmqvL0aMGNGl97SqJOfSpUvw8NAeOhw0aFDza/qSnK52zq18fKTIytLdbqxnWKOcnJwe/flrpDL0q67Q2d6T+0Wo78U3Ug+gum37Zak7/AX6/ejpf0Zas8S+6C89rfM7019agxEj7jTZcy2xL4Riqr6wutVVIpF2oW/TNNWt7aYQG1sLb2+1Vpu3txqxsbUmfzZZrsrNb+HGLf9euAE7VG5+S6CIejbnzTE4h+FabecwHM6bYwSKiCzd8s0u8ES+Vpsn8rF8s4swAZHRWNVIzuDBg3Hp0iWtttLSUgAtIzqmJJdrkJx8DUqlFLm5tfDxkSI2tpZFxz3c4NAA5CMF/aKfhUNtOWqkMlRufotFxwIZE+qJLCThdHQcXK4Xo6y3O5w3x7DomPQaH+qBBBRhQ/QxXLnuiP69q7F8swuLjm2AVSU548ePx2uvvYba2lpIpY3FvqmpqXB3d4dcLjdLDHK5Btu21XCYkbQMDg0AQn/C2ZvfC5aiC2tMqCcQGi90GGRFxod64BMmNTZH0OmqqqoqZGdnIzs7Gw0NDbhw4QKys7NRWFgIAFi9ejVmz57dfP3cuXPh4OCAZ599Fv/3f/+H/fv349///jeeffZZs0xXERERkfUQNMn58ccfERQUhKCgINTU1CAuLg5BQUF4/fXXAQAlJSXIy8trvt7Z2RlJSUkoLi7GlClTsGzZMkRFReG5554T6iMQERGRhRJ0uiowMBDl5bqX3wJAfHzb4eZRo0bh888/N2VYREREZAOsbnUVERERUWcwySEiIiKbxCSHiIiIbBKTHCIiIrJJTHKIiIjIJjHJISIiMqOidBWy/SKBKX9Ftl8kitJVQodks6xqx2MiIiJrVpSugkPwHATW5zY2VAH5wRkoSkmCR4B5du7vSTiSQ0REZCalijh4NSU4N3nV56JUESdQRLaNSQ4REZGZOJYX626vKDFzJD0DkxwiIiIzqZa56253djNzJD0DkxwiIiIzGRgfg3w7H622fDsfDIyPESgi28Ykh4iIyEw8AuSoSUlCmmc4vu8TiDTPcNSw6NhkuLqKiIjIjDwC5PDI3oKcnBz4jRghdDg2jSM5REREZJOY5BAREZFNYpJDRERENolJDhEREdkkJjlERERkk5jkEBERkU1ikkNEREQ2iUkOERER2SQmOURGkLr2e5zsHwbNuEic7B+G1LXfCx0SERmgKF2FbL9InPMMRrZfJIrSVUKHREbAHY+Juil17ffw37gYXihobNAA+Rt/Qyq2Y8rKccIGR0QdKkpXwSF4DgLrcxsbKoH84AwU8bgFq8eRHKJucnxjQ0uCc5MXCuD4xgaBIiIiQ5Qq4uDVlODc5FWfi1JFnEARkbEwySHqpv6ay3rar5g5EiLqCsfyYt3tFSVmjoSMjUkOUTddEQ3Q097fzJEQUVdUy9x1tzu7mTkSMjYmOUTdVP3ycuTDU6stH56ofnm5QBERkSEGxscg385Hqy3fzgcD42MEioiMhYXHRN00ZeU4pGI7zr6xAf01V3BF1B/VLy9n0TGRlfAIkKMoJQlpijg4VpSg2tkNA+NjWHRsA5jkEBnBlJXjgJWJyMnJwb0jRggdDhEZyCNADo/sLUKHQUbG6SoiIhMRqVRwiIiAb2QkHCIiIFJx7xUhqVQiREQ4YNYsJ0REOEClEgkdEpkYR3KIiExApFLBKSQEkrw82ANAVhYkmZm4lpwMjZzTIOamUokQEuKEvDxJc1tmpgTJydcgl2sEjIxMiSM5REQmIFUqIcnL02qT5OVBqlQKFFHPplRKtRIcAMjLk0CplAoUEZkDkxwiC9I0veE0axanN6ycuFj33iviEu69IoTiYt0/7kpK+GPQlnG6ishCtJ7eaMLpDevV4K5775UGN+69IgR39wad7W5uutvJNjCFJbIQnN6wLbWxsVB7e2u1qb29URsbK1BEPVtsbC28vdVabd7easTG1goUEZkDR3KILASnN2yLRi7HteRkSJVK1ObmQurjg9rYWI7KCUQu1yA5+RqUSilKSsRwc2tAbGwti45tHJMcIgvB6Q3bo5HLUbNtG3JycjCC+ycJTi7XYNu2GqHDIDPidBWRheD0Rs8kTk9HHz8/9PX0RB8/P4jT04UOichmMMkhshBN0xt1YWGoDwxEXVgYi45tnDg9HX2CgyEpKIC4shKSggL0CQ5mokNkJJyuIrIgTdMb1DM4KhQQ1ddrtYnq6+GoUKAqO1ugqIhsB0dyiIgEIiov191eUWHmSIhsE5McIiKBaGQy3e3OzmaOhMg2MckhIhJIdXw8NHbaVQMaOztUx8cLFBFZKu6G3jWsySEiEkhDQACqUlIaa3MqKqBxdkZ1fDwaAgKEDo0sCHdD7zomOUREAmoICGCRMbWrvd3QuVChfZyuIiIismDcDb3rmOQQERFZMO6G3nVMcoiIiCwYd0PvOtbkEBERWbDWh72KS0rQ4ObGw147iUkOEZGJiFQqSJVK+PIUcuom7obeNUxyiIhMoPWyX3sAyMrisl8iM2NNDhGRCbS37JeIzEPwJCchIQF+fn5wdXXFpEmTcPz48XavP3z4MB588EEMHToUPj4+mD9/Ps6dO2emaImIOofLfomEJ2iSs2/fPqxYsQIvvfQSjh49ivHjxyMsLAyFhYU6r8/Pz8eCBQswYcIEHD16FMnJyaitrUVYWJiZIyciah+X/VqXonQVsv0icc4zGNl+kShKt6xjE1QqESIiHDBrlhMiIhygUomEDskqCFqT87///Q8LFizAX/7yFwDAxo0bcfjwYezYsQOrVq1qc/1PP/2EGzduYNWqVZBIJACAF198EbNnz0ZZWRlcXFzMGj8RkT61sbGQZGZqTVlx2a9lKkpXwSF4DgLrcxsbKoH84AwUpSTBI0D4+imVSoSQECfk5Uma2zIzJUhOvga5XCNgZJZPsJGcuro6nDp1ClOnTtVqnzp1Kk6ePKnzHn9/f/Tq1Qvvvfce1Go1rl69ig8//BD33HMPExwisihNy37rwsJQOWYM6sLCWHRsoUoVcfBqSnBu8qrPRakiTqCItCmVUq0EBwDy8iRQKqUCRWQ9BBvJKSsrg1qtxqBBg7TaBw0ahEuXLum8Ry6XIykpCU8++SRefvllNDQ0wM/PD5988km7z8rJyTFa3OZ4X2vEvmjBvmjBvgCwfHnL/6+rA9gnFve9cLj8u8526eXfTR5rZ94/N9cXaFyjd0t7rcX1ZXe091lGjBjRpfcUfAm5SKQ9r6jRaNq0Nbl48SKio6MRHh6O0NBQVFVV4fXXX8eTTz6JAwcOQCzWPTDV1c5pT05Ojkne1xqxL1qwL1qwL1qwL1pYYl9kDxgCVLVtrx0wBH4mjLWzfeHjI0VWlu52S+vLrjLV90Kw6SoXFxdIJJI2ozalpaVtRneabNu2DY6OjvjHP/6Bu+66CwEBAXj77beRnp6ud4qLiIioPQPjY5Bv56PVlm/ng4HxMSZ/tkilgkNEBJxmzYJDRAREqrYFz7GxtfD2Vmu1eXurERtba/L4rJ1gSY69vT38/f2Rmpqq1Z6amop7771X5z01NTXNBcdNmn7d0NBgmkCJiMimeQTIUZOShDTPcGQ5T0aaZzhqzFB0bP/773AKCYF9YiLsjh2DfWIinEJC2iQ6crkGycnXEBZWh8DAeoSF1bHouJMEna6KiorC0qVLMWbMGNx7773YsWMHSkpK8NRTTwEAVq9ejaysLOzfvx8AMH36dLz11ltYt24dwsLCcPXqVaxZswZDhw6Fv7+/kB+FiIismEeAHB7ZW8z6zCFbtujdMPLWIxzkcg22basxZ3g2QdAk57HHHsPly5exceNGXLx4ESNHjsSePXvg6ekJACgpKUFeqy/ApEmTkJCQgDfffBObN2+GVCrF2LFj8cknn8DJyUmoj0FERGSwXn/8obOdG0Yaj+CFx0uWLMGSJUt0vhYfH9+mLTQ0FKGhoaYOi8gqqFQiKJVSFBeL4e7egNjYWg5hE1mJG3rqT7lhpPEInuQQUddwgzAi6/Z7ZCRkv/3GDSNNSPCzq4ioa7hBGJF1qxsypHnDyPrAQG4YaQIcySGyUsXFuv+NUlLCf7sQWQuNXN6myJiMh38bElkpd3fd2ya4uXE7BSIigEkOkdXiBmFERO3jdBWRlWraIEyplKKkRAw3N66uIiJqjUkOkUCK0lUoVcTBsbwY1TJ3DIyPMXiHVWNvECZSqSBVKiEuLsb5vn54VaREUWVfq1ue3vpzNLi7ozY2lsWchP9MTsFXp+S4ir7oi6t40F+F578NFjosMiEmOUQCKEpXwSF4DgLrcxsbKoH84AwUmWEreX1EKhWcQkIgyctDHuSYiZ04j/7Nr1vL8vTWn6OJJDOTq1Z6uP9MTkHCqSAUwKu5TXUqH5icwkTHhrEmh0gApYo4eDUlODd51eeiVBEnUESAVKlsTgz+DiXO4zat161leXrrz9Gkaat86rm+OiXXSnAAoABe+OoUE19bxiSHSACO5cW62yuE285dXNwS0+/w0HmNNSxPb/05tNq5VX6PdhV9dbZXoY+ZIyFzsvy/sYhsULXMXXe7s3DbuTe4t8Q0BEU6r7GG5emtP4dWO7fK79H64qrO9j6oMnMkZE5McogEMDA+Bvl2Plpt+XY+GBgfI1BEQG1sLNTe3gCANYjFcJzTet1alqe3/hxNuFU+Peivgifytdo8kY8H/VXCBERmwcJjIgF4BMhRlJKENEUcHCtKUO3s1qXVVcakkctxLTkZUqUSw0pKcLDPv/CqSIniq32tanl6688hLilBg5sbV1dRY3Hx5BR8dUqFKvRBH1RxdVUPwCSHSCAeAXJ4ZG8ROgwtrbeYdwPwNgDgmoARdY0lbZWvUonw9797o6rKyeqW4lui7my98Py3wXheq8XfFCGSBWGSQ0RkIjwp3ri6svWCSiWCUilFcbGYSWYPxJocIiIT4UnxxmXo1gtNSWZioj2OHbNDYqI9QkKcoFKJzBEuWQAmOUREJsKT4o3L0K0XmGQS/6QREZkIT4o3LkO3XmCSSfydJiIyEZ4Ub1yGbr3AJJOY5BARmUjTSfEPP1yGwMB6hIXVsei4GzwC5KhJSUKaZziynCcjzTMcNe0UHTPJJK6uIiIyIblcgzVr8jBixAihQ7EJhmy90JRkKpVSlJSIrWq/JzIOJjlERGSz5HINtm2rEToMEginq4isTFG6Ctl+kTYzqPoAACAASURBVDjnGYxsv0gUpXNbeiIiXTiSQ2RFurIZGhFRT8WRHCIrYuhmaEREPRmTHCIrYuhmaEREPRmTHCIrYuhmaIYSqVRwiIiA06xZcIiIgEjVc+t92BdkKk3fLd/ISH63TIw1OURWZGB8DPKDM7SmrNrbDM0QIpUKTiEhkOTlNbdJMjNxLTkZGnnPqvdhX5CptP5u2QNAVha/WybEkRwiK2LoZmiGkCqVWj/UAUCSlwepUtnt97Y27AsyFX63zIsjOURWxpDN0AwhLtZd7yMu6Xn1PuwLMhV+t8yLIzlEBABocNdd79PgZpx6H2vCviBT4XfLvJjkEBEAoDY2Fmpvb602tbc3amNjBYpIOOwLMhV+t8yLSQ5ZnKy9BfjGQ4GfXELwjYcCWXsLhA7J5ujaNVkjl+NacjLqwsJQHxiIurAwkxVDWvquzebsC+pZWn+3KseM4XfLxFiTQxYla28BPBfPwW0439hQDZxbnIEsJGFMqKewwdmIjnZNrtm2TdDnWwqN3PR9QT1T03crJyeHB7eaGEdyyKJURMe1JDg33YbzqIjmjr7GIvSuyUI/n4h6DiY5ZFFcruteeaCvnQwn9K7JQj+fiHoOJjlkUcp66155oK+dDGfqXZMt/flE1HMwySGL4rw5BucwXKvtHIbDeXP3d/SlRgPjY5Bv56PVZqxdk63h+UTUc7DwmCzKmFBPZCEJp6Pj4HK9GGW93eG8OYZFx0bkESBHUUoS0hRxcKwoQbWzGwbGx5it6Ffo55tTUboKpYo4OFz+HdkDhtjs5xRCU986lhejWubOviWdmOSQxRkT6gmExgsdhk0z1a7J1vJ8XYz9Q7PNKrIqy1xFZo2sZYUeCY/TVUTU4zX/0Cz4CGMqjyCw4CM4BM/p1v49XEVmOuxb6iwmOUTU45nihyZXkZkO+5Y6i0kOEfV4pvihyVVkpsO+pc4yKMn59ddfTRUHEZFgTPFDk6vITId9S51lUJIzYcIETJ48GfHx8bh06ZKpYiIiMitT/ND0CJCjJiUJaZ7h+L5PINI8w1HDwlijaN23Wc6T2bekl0GrqzZs2IDExET87W9/w6uvvorJkycjPDwcM2fOhFQqNVWMREQmZapl7U2ryHJycuDHM4qMyhJX6JHlMSjJiYiIQEREBPLz8/Hhhx/ik08+wZIlS9C3b188+uijmDdvHoKCgkwVKxGRyfCHJpHt6VLhsZeXF2JiYpCVlYUvv/wS8+bNw6FDhxASEoLRo0fjH//4B86ePWvsWImIiIg6rdurq0aPHo3x48dj1KhR0Gg0+OOPP/DWW2/hvvvuw7x583DhwgVjxElERERkkC4lORqNBqmpqYiMjISvry+WLl2Kq1evYsOGDfj1119x9uxZrFu3DpmZmYiMjDR2zEREREQdMqgm5/Tp0/j444+xd+9eXLx4EW5ubli8eDHmz5+P22+/XevaZ555BiKRCLGxsUYNmIiIiKgzDEpygoKC4ODggJkzZ2L+/PmYPHkyxGL9g0G33347xo0b1+0giYiIiAxlUJLz5ptvYs6cOejbt2+nrg8KCuJqKyIiIhKEQTU5xcXFKCws1Pv6L7/8gvXr1xsUQEJCAvz8/ODq6opJkybh+PHj7V6v0Wjw1ltvYdy4cRg8eDBuv/12vPbaawY9k4iIiGyfQUnO+vXrcebMGb2vG5rk7Nu3DytWrMBLL72Eo0ePYvz48QgLC2s3kVq5ciW2b9+O1157DRkZGdizZw8mTpxoyMcgIjKISiVCRIQDZs1yQkSEA1QqkdAhGcXxhVvhIHNBX5kMDjIXHF+4tcN7RCoVHCIi4DRrFhwiIiBSdf2kdiJTM2i6SqPRtPt6VVUVevXq1en3+9///ocFCxbgL3/5CwBg48aNOHz4MHbs2IFVq1a1uT4nJwdvv/020tPT2xQ6ExGZgkolQkiIE/LyJM1tmZkSJCdfg1ze/t+Jluz4wq14+OAraErX7KHGwwdfwRcLgYm7luq8R6RSwSkkBJK8vOY2SWYmriUnQyPnkQpkeTpMcn7++WecPn26+dcnTpxAfX19m+vKy8uxY8cO3HbbbZ16cF1dHU6dOoXo6Git9qlTp+LkyZM67/nss8/g5eWFr7/+Go8//jgaGhoQEBCANWvWYNCgQZ16LhFRk6J0FUoVcXAsL0a1zF3nUQ5KpVQrwQGAvDwJlEoptm2rMWe4RjXt4N9w63iU6GZ7DXQnOVKlUivBAQBJXh6kSiVqtm0zTaBE3dBhkvPpp582T0GJRCLs3LkTO3fu1Hlt3759sa2TX/SysjKo1eo2ycmgQYP0Hv6Zn5+PwsJC7Nu3D2+99RZEIhH+/ve/Izw8HF999ZXelV45OTmdislQpnpfa8S+aMG+aGHJfXE56xJ8o6IRqM5tbKgE8mdn4OT/NmPAmMHN1+Xm+gKwb3N/bm6tQZ/P0vriHqh1tttBrTdW39xcHT0B1ObmWnVfCIl90aK9vhjRxbPfOkxy/vznP+OBBx6ARqPB9OnTsWLFCkybNk3rGpFIBEdHRwwfPhz29rr+COgnEmn/W0Kj0bRpa9LQ0IDr169j69atzSNGW7duxdixY/HDDz9g7NixOu/raue0JycnxyTva43YFy3YFy0svS+yQzfBqynBuclLnYvC19/HiFZnWPn4SJGV1fZ+Hx9ppz+fJfZFPSSw15Ho1EOiN1apjw90dYbUx8eq+0Io7IsWpuqLDpMcDw8PeHh4AAAOHDiA22+/3ShTQy4uLpBIJG1GbUpLS/W+v6urK+zs7LSmxIYPHw47OztcuHBBb5JDRHQrx/Ji3e0VJVq/jo2tRWamRGvKyttbjdjYWpPGZ2qHZ76uVZMDAJqb7fqWctTGxkKSmak1ZaX29kYtN30lC2XQ6qr777/faLUv9vb28Pf3R2pqqlZ7amoq7r33Xp333Hfffaivr0deqz9g+fn5qK+vx7Bhw4wSFxH1DNUyd93tzm5av5bLNUhOvoawsDoEBtYjLKzO6ouOgcbi4i9mrkcdJGgAUAcJvpi5Xm/RMQBo5HJcS05GXVgY6gMDURcWxqJjsmjtjuRERUVBJBLhzTffhEQiQVRUVIdvKBKJ8N///rdTD4+KisLSpUsxZswY3HvvvdixYwdKSkrw1FNPAQBWr16NrKws7N+/HwAwefJk3HXXXYiKikJcXBwAICYmBmPHjsXdd9/dqWcSEQHAwPgY5AdnwKu+Zcoq384HA+Nj2lwrl2usushYn4m7lmoVGXdmMw6NXG70IuOsvQWoiI6Dy/VilPV2h/PmGIwJ9TTqM6hnajfJOXr0KMRiMRoaGiCRSHD06FG99TJNOnq9tcceewyXL1/Gxo0bcfHiRYwcORJ79uyBp2fjl7ukpERr1EYsFuPjjz/GK6+8gpkzZ0IqlWLKlClYu3Ztu8dLEBHdyiNAjqKUJKQp4uBYUYJqZzedq6vItLL2FsBz8RzchvONDdXAucUZyEISEx3qNlF5ebl1j7kKhAVjLdgXLdgXLdgXLdgXLW7ti288FJhT/WGb65Ic52NqUbw5QzM7fi9amKovOPxBRERGVZSuQrZfJM55BiPbLxJF6fp3RXa5rrsAXF+7JbHVnbC7Qpyejj5+fujr6Yk+fn4Qp6cLHRIAA3c8njFjBubOnYvZs2dj4MCBpoqJiIisVFG6Cg7BcxBY32r/oeAMFKUk6ZwKLOvtDlS3fZ+y3roLwy2Fre6E3RXi9HT0CQ6GqGmj4MpK9AkORlVKChoCAoSNzZCLr1y5gpdeegkjR47EY489hl27dqGiosJUsRERkZUpVcRpFXMDgFd9LkoVcTqvd94cg3MYrtV2DsPhvLltAbglaW8n7J7GUaFoSXBuEtXXw1GhECiiFgYlOSdOnMCJEyfwwgsvQKVS4bnnnoOvry8WLFiAvXv3orpaRzpOREQ9Rmf3H2oyJtQTBduTkOQ4H0clk5HkOB8F2y2/6Li4WPePz5KSnlcFIiov191uAYMgBk1XAcCf/vQnxMbGIjY2FqdOncLevXuRnJyMzz//HI6OjnjooYewY8cOU8RKREQWrlrmDlTqaL9l/6HWxoR6AqHWVWTs7t6gs93NTXe7LdPIZEBl2990jbOzANFo61bK6e/vjzVr1uD06dPNe+kkJycbKzYiIrIyA+NjkG/no9Wmb/8haxYbWwtvb+1jMWxhJ+yuqI6Ph8ZOe8xEY2eH6njhE1eDR3JaU6lU2LdvH/bt24czZ85AIpFg6tSpxoqNiIisTE/Zf6hpJ2ylUoqSEjHc3BoQG1vb44qOAaAhIABVKSmNtTkVFdA4O6M6Pl7womOgC0lOcXEx9u3bh6SkJPzwww8AGo9beOONNxAcHAwXFxejB0lERNahcffidXC5XoKC3u5wXhUDjwDLrq/pKlvdCbsrGgICUJWdLXQYbRi8hPzkyZNoaGjA3XffjX/84x947LHHmg/wJCKinou7F5OlMSjJKS8vR0xMDObOnQsvLy8ThURERNaoIjquJcG56Tacx+noOKsrLCbbYFCSc/z4cVPFQUREVs6ady8m29TzFvQTEVkZQ45JEJK+XYotffdisl3tjuT4+flBLBbj+++/R69eveDn59epU8hPnTpl1CCJiHoqQ49JEJLz5hicW5yhNWVlDbsXk+1qN8kJCAiASCSCWCzW+jUREZlHqSKuJcG5yas+F2mKOHhkbxEoKt3GhHoiC0k4HR0Hl+vFKOvtDufNMSw6JsG0m+TE37KRz62/JiIi0zL0mAShWdruxWe2HMfQlQr0U5ejUiLDhbXxGBU5UeiwyEwMqsn58MMPoVLpnwsuKCjAhx9+2O2giIioUbVMdz1Le8ckUKMzW45j7IrZ8FSrIEMFPNUqjF0xG2e2cBFNT2FQkhMVFYWMjAy9r2dmZiIqKqrbQRERUaOeckyCKQxdqYA9tE/Htkc9hq4U/nRsMg+DkhyNpv3tqmtqaiCRSNq9hoiIOs8jQI6alCSkeYYjy3ky0jzDUWOBRcetqVQiREQ4YNYsJ0REOEClEqaWs59a9+nYfdXCn45N5tHhPjmFhYUoKCho/vXZs2eRnp7e5rry8nLs3LkTcrnl/sEjIrJGHgFyiysy1kelEiEkxAl5eS3/4M3MlCA5+ZrZz3WqlMgg05HQXJU4Q/jzsckcOkxydu3ahfXr10MkEkEkEmHTpk3YtGlTm+s0Gg3EYjHefPNNkwRKRESWT6mUaiU4AJCXJ4FSKTX7OU8X1sbDbcVsrSmrOtjhwtp4Jjk9RIdJTnBwMHx9faHRaLBkyRIsWbIEEyZM0LpGJBLB0dERd911F9zcWAxHRNRTFRfrroIoKTH/3rOjIiciE/sxdKUCfdUVuCpx5uqqHqbDJGfkyJEYOXIkAOD69euYOHEiz60iIr2K0lUoVcTB4fLvyB4wBAPjYyy6fsSUDs3ZjlmpK+GPOtTBHp9OWYuHkhYLHZZJubs36GwfLjmPbL/XDPpeZOwtwoboMly57oj+vauxfLMLxoe2fyB00/fPsbwY1TJ3DIyPgXPZTwAA55v/dYZKJYJSKUVxsRju7g2Ija012nTbb2tTMOyNV+CrqcI1UR8Uvrwet68MNsp7kzZReXl5p3/Xrl27hsuXL2PYsGE6Xy8sLISLiwscHR2NFqClysnJwYgRI4QOwyKwL1r09L5o2p3Xq9Xmdfl2PhZfKGsKh+ZsR1jqS2hdcqsBkDhlk0UkOqb6Ia6rJuc+13P4sPQheKk7/73I2FuEJYsdUACv5jZP5CNhe43eRMdY3z9dn8HbW22UuqLf1qbg7o2L0avVFNoN2OHHZdt7dKJjqr87DRo//Nvf/oYFCxbofX3hwoX4+9//3u2giMg6lSritH7AAI2785Yq4gSKSDizUlfi1jVFopvtQmv6IZ6YaI9jx+yQmGiPkBAno6yCkss1SE6+hrCwOgQG1iMsrA5rxX/XSnCAjr8XG6LLtBIcACiAFzZEl+m9x1jfv/bqirpr2BuvaCU4ANAL9Rj2xivdfm9qy6BTyFNTU7Fw4UK9r8+aNQu7d+/udlBEZJ2sbXdeU7JHnd72G2aO5VamLg6WyzVa73PO0/DvxZXrumcE9LUDxvv+mbKuyElTpbfdvGvPegaDfscuXrzYbmGxq6srSkp63l9mRNSIu/O2qIO9Qe3mZO7i4K58L/r3rjaovavP0UVfXZGbm+52Q1wT9TGonbrHoG/0wIED8csvv+h9/ZdffoGzMxfmEfVU3J23xadT1rb5l7nmZrvQTPlDXJeufC+Wb3aBJ/K12jyRj+WbXYz6HF1iY2vh7a3WavP2ViM2ttag99Gl8OX1uHHLJMoN2KHw5fXdfm9qy6Ak58EHH8S7776LkydPtnnt+++/x7vvvosHH3zQaMERkXVpvTvv930CrWJ3XlN5KGkxEqdsQjWkqIcY1ZBaTNGxvh/iQdWf4i7ZZchlDbhLdhlfLt1vlOd15XsxPtQDCdtr8IDjMYyR/IAHHI+1W3R863O6szu0rroiY21mePvKYPy4bDuKRW6oRB8Ui9x6fNGxKRm0uurixYuYOnUqSkpK8MADD+COO+6ASCTCmTNn8PXXX8PV1RWHDx+Gu7vuIUNb0tNX0bTGvmjBvmjBvmhhiX3RtLqqpEQMN7cGBFV/ihcPzkJ9q+k0O9Rh97yPMX3rbKM91xL7Qijsixam6guDCo9dXV2RmpqKVatW4eDBg/jyyy8BAH379sW8efOwatUquLq6Gj1IIiIyrluLg++S3a2V4ABAPeyx7OP7MX2r4e9vyn1miDrLoCQHAAYPHoz4+HhoNBqUlpZCo9Fg0KBBEImEOYCNiIi6rxwyne0VXTgAob3zq4jMqcul9A0NDZBIJBgwYAATHCIiKyeD7hO7nWH4id2m3GeGyBAGJzk//PADQkJC4OHhgdtuu635RPKysjI8/vjjOHLkiNGDJCIi09o47xjsbtnbxw512DjvmMHvZUnnV1HPZtA3LiMjAzNmzEBeXh7Cw8Oh0bTMr7q4uKCqqgrvv/++0YMkIiLTmr51NnbP+xhy5EGGy5Ajr8tFx+Zeok6kj0FJzpo1azB8+HCcPHkSr776apvXAwMDkZmZabTgiIjIfKZvnY2fygcgv1yCn8oHdHlVlSn3mSEyhEFJzg8//IBFixZBKpXqrMMZMmQILl68aLTgiIjI+phynxkiQxi0ukosFkMs1p8XXbx4EQ4ODt0OioiIjO/7ZUm4b9tz6I1aXIcU30X8F0NC7kGpIg6O5cWolrnDbdUTkH/xDsTFxWhwd0dtbCw0csM3c7x1iTqREAxKcvz9/fHFF19g6dKlbV6rq6tDYmIixo8fb7TgiIjIOL5floRp255qPhm9F65h2rancGnbIIzEH42NlcCNxZ9onZItyczEteTkLiU6rZ3ZchxDVyowXF2OCokMF9bGY1TkxHbvKUpXaSVgA+NjeuTu2dR1Bk1X/fWvf8XRo0fx3HPP4fTp0wCAkpISfP3115g9ezby8vLw0ksvmSRQIiLquvu2PYdbiwxEAFybEpybWic4ACDJy4NUqdT5nkXpKmT7ReKcZzCy/SJRlK7Sed2ZLccxdsVseKpV6I8KeKpVGLtiNs5sOa433qJ0FRyC5yCw4COMqTyCwIKP4BA8R+8ziHQxKMmZMmUKtm7dioMHD2LOnDkAAIVCgbCwMPz6669ISEjAuHHjTBIoERF1XW90vehXXFLSps2QJGToSgXsb0me7FGPoSsVep9ZqoiDV32uVptXfS5KFXFd/BTUExm84/HcuXMxY8YMpKam4vz582hoaIC3tzemTZuGPn14VDwRkSW6Dil6oWs7Dje4ubVpK1XEIVBHEpKmiINH9hat9n5q3RsN9lXr32jQsbxYd3tF24SLSB+DkxwAcHR0xMyZM40dCxERmch3Ef/VqskBAA2ASxikNWV1A3ZaU1Zqb2/Uxsa2eT9DkpBKiQwyHQnNVYmz3kMjqmXuQKWOdue2CReRPtx+koioBxi3cQ4OR+xEFZxwAxJUwQmHI3bi8sGvkeYZjiznyUjzDEf+9hTUhYWhPjAQdWFheouOq2XuOp+jKwm5sDYedbf8m7oOdriwNl5vvAPjY5Bv56PVlm/ng4HxMZ35uEQAOhjJ6d+/P8RiMYqLi2Fvb4/+/ft36pwqiUQCFxcXTJo0CWvXrsXAgQONFjAREXXNuI1zoN44B9VNv775v7dOL9WEBnT4XgPjY5AfnKFVN6MvCRkVORGZ2I+hKxXoq67AVYlzh6urPALkKEpJQpoiDo4VJah2duPqKjJYu0nO8uXLIRKJYGdnp/XrjqjVapSUlCAlJQVXr17F7t27jRMtEZGVaL38udJpIIoSXrWpH9CGJiGjIicCkT8hJycHI0aM6NTZ5h4B8jYJGJEh2k1yYmJi2v11R3x9fbFx40bDoyIismLNK4+aRjkqgfzgH1GUkmRziQ6TELJkXSo87qxHH30ULi4upnwEEZHFMWTlERGZjsFJTnl5Of773//iyy+/RGFhIQBg2LBhmD59OqKiotC/f//ma728vODl5WW0YImIzC1jbxE2RJfhynVH9O9djeWbXTA+1KPde7j8mcgyGJTknDt3DrNnz0ZxcTFGjhyJ+++/HxqNBrm5udi0aRN27dqF/fv3Y8SIEaaKl4jIbDL2FmHJYgcU4P7Ghmrg7OJ8JKCo3URH3/Lnc453Yn2EA4qLxXB3b0BsbC0PrSQyIYOSnGXLlqGqqgopKSkICgrSeu3IkSN44okn8Morr2Dfvn1GDZKISAgbostaEpybCuCFDdHH8Ek7SY6ulUdp4kAs02xEYaJ9c1tmpqRbp3OrVCIolVImTUR6GLRPzsmTJxEZGdkmwQGASZMmYenSpfjuu++MFhwRkZCuXHc0qL2JR4AcNSlJzfvPfOMeivVBKSgs6a11XV6eBEqltEuxqVQihIQ4ITHRHseO2SEx0R4hIU5QqTpeAdtTdfasLbIdBo3kODs7QyaT6X1dJpO1+zoRkTXp37sazZvK3NregdYrj3JyclDxYl+d15WUdG1PVqVSirw8iVZbU9K0bVtNl97Tlule8ZZhcyveSJtBf7qeeOIJfPDBB7h69Wqb1yoqKvDBBx/giSeeMCiAhIQE+Pn5wdXVFZMmTcLx4/pPpW3t/PnzGDp0KIYMGWLQ84iIOmv5Zhd4Il+rzRP5WL7Z8FWj7u4NOtvd3HS3d6S4WPdf311NmmwdD/zsmdodyUlKStL6ta+vL0QiEcaOHYv58+fDx6dxy+3z58/jo48+wqBBgwwqOt63bx9WrFiBTZs24b777kNCQgLCwsLw3XffYdiwYXrvq6urw9NPP42JEyciPT29088jIsvU1dqS9HQxFApHlJeLIJNpEB9fjYCAriUNuowP9UACirAh+phBq6uaiFQqSJVK+ObmYs3ge5E59J/Iu9AyZeXtrUZsbNdOB9eXNP36qxgREQ42UZ/T1H/i4mI0uLujNjZW5xETndHRirczW45j6EoF+qnLUSmRdbgjM1kHUXl5ud4/BU3HOGg0jZe0/v9631AkwuXLlzv18GnTpmHUqFH4z3/+09x2zz33IDg4GKtWrdJ7X0xMDCoqKhAQEIDly5fj999/79TzjKlp105iX7TGvmjR2b5oqi1pPfXi7a3usCA3PV2M4OA+qK9vqUGxs9MgJaXKqIlOV4lUKjiFhECSl9fcdm7o/Vjptx/FV/vCza17hcK6+q21zvShEDr7vdDVf2pvb71naXUk2y8SgQUftWlP8wyH5Nk/Y+yK2bBvdTBpHeyQuW6/SRMd/n3RwlR90e5IzoEDB4z+wCZ1dXU4deoUoqOjtdqnTp2KkydP6r3v0KFDOHToEI4cOYL9+/ebLD4iMo+u1pYoFI5aCQ4A1NeLoFA4Iju7yiSxGkKqVGr9gAaA2y4cw/sTlqJm97Zuv79crkFy8jUolVJ8+60d/vhDe5rK2utzdPWfJC8PUqUSNdsM77/2ztpymq2d4ACAPeoxdKUCiPypax+ALEK7Sc7999/f3svdUlZWBrVajUGDBmm1Dxo0CJcuXdJ5T0lJCV544QW8//776NtXdxGfLjk5Od2K1dzva43YFy3YFy060xe5ub4A7HW017Z7/+XL/nraNRbxe+Cbm6vjUwG1ublGjW/58sY+/OOPfm1e66gPhdKZmIzef4OBwv9uRu7qHeh79RKu9h2M+lVPY8DgOripy3Xe0lddYfL+s8TfH6G01xddHeUx6bEOnXHrgZ8ajUbvIaDPPPMMnn76aYwbN07n6/qYYgiMw4wt2Bct2BctOtsXPj5SZGXpbm/v/gEDRKjSMWAzYIBIsN+D1odyZtf3hxweGIYirWukPj5Gj6+rfSiEzn4vpD4+0PWhutV/I0YA4W1PWK+QyNBfXdGm/arE2aT9x78vWggyXXWrRx99tMNrRCJRp6aRXFxcIJFI2ozalJaWthndaXL06FGkp6dj/fr1ABoTooaGBri4uGDTpk148sknO/4QRGRRYmNrkZkpaVOT01FBbnx8tc6anPj4jpd3d1frZKZa5o6B8Y2HF2stUQaQD08Utkp01N7eqI2NNXo8Xe1DS1YbGwtJZmabmhxT9N+FtfFw01GTc2FtfKdOSyfLZVCS09DQ0GaURa1Wo7CwEL///jt8fHzg7u7eqfeyt7eHv78/UlNTERIS0tyempqK2bNn67zn1uXln332GTZt2oTDhw/Dw6Nzqx2IyLK0ri0pKRF3uiA3IKABKSlVUCgcUVEhgrOz8VdX6aJvv5XiAXdg5K1LlFGAY44PwHmkO6Q+Pt1aHdServahJdPI5biWnNy4uqqkBA1ubibrv1GRE5GJ/Ri6UoG+6gpclThzdZWNMCjJOXjwYLuvvfjii9i5c2en3y8qKgpLly7FmDFjcO+992LHjh0oKSnBU089BQBYvXo1srKymkeG7rjjDq37f/zxR4jF4jbtRGRdbOegjwAAIABJREFU5HJNlwpkAwIazF5krO+Eccc/dMfh0KseZ7dsMfm0RFf70JJp5PIuFRl3xajIic1Fxs43/yPrZ7SanJkzZyI9PR1/+9vf8Omnn3bqnsceewyXL1/Gxo0bcfHiRYwcORJ79uyBp6cngMZC47xbquuJiISkb78VfWMm1c5ucDBdOETUDqNujenr64sffvjBoHuWLFmC06dP49KlSzhy5AgCAlqKwuLj43H69Gm99y5cuFCQPXKIqOeqlumekj8/cDzy7Xy02pqWKBORMIya5Hz11Vfo16/tMkYiIlsxMD5GZzLT/521WodypnmGo4bnIhEJyqDpqqZVTbeqqKjAsWPHcPr0abz88stGCYyIyBJ5BMhRlJKENEUcHCtKUO3shoHxMc3JTNOhnEQkPIOSnHXr1ulsl8lk8PHxwX/+8x8sWrTIKIEREVmq1ieMk/HoWprPkTDqDoOSnCtXrpgqDiIi6sH0Lc0v4pQfdUO3anLS0tIQHR2NsLAwrFy5EoWFhcaKi4iIjEikUsEhIgJOs2bBISICIpVK6JC0lCritM6VAhqX5pcq4gSKiGxBhyM569atw6ZNm/Dzzz/D1dW1uX3Xrl2Ijo5uPpX866+/xp49e3D48OHmJeBERCQ8XSd6SzIzu3yitynoW5rvWFFi5kjIlnQ4kpOWloapU6dqJTjXr19HTEwM+vXrh5SUFFy4cAE7duxAVVUV/vnPf5o0YCIiMkx7J3pbCn1L86ud3cwcCdmSDpOc3NxcjB07VqvtyJEjuHr1Kp577jkEBQXByckJc+bMweOPP45vv/3WVLESEVEXiIt1j5KIS0w/SnLrNJm9nr3N9C3N5z5D1B0dTldduXIFbm7amXRaWhpEIhEeeughrXZ/f398/PHHxo2QiIi6pUHPmYINbqYdJdE1TeZ74gTqDh5sM03W0dJ8oq7oMMkZPHgwioqKtNpOnDiBPn36YPTo0VrtYrEY9vb2xo2QiIi6xZwnerema5pMeuECxEqlzjOpuDSfjK3D6ap77rkHu3fvRnl5OQDg559/xo8//oigoKA2J5L/9ttvGDJkiGkiJSKiLmk60bsuLAz1gYGoCwszS9GxkNNkREAnkpxly5ahpKQE99xzD2bMmIEZM2ZAJBLhhRde0LpOo9Hg008/xb333muyYImo5ypKVyHbLxLnPIOR7ReJonTLWgKty5ktx1HhcheGj3sAFS534cyW44LF0nSi97UDB1CzbZtZVlW1nibbh5nwRD5kuAyftA/w6dozJn8+UYdJzqhRo5CSkoKxY8eitLQU48ePx759+zBu3Dit69LS0tCnTx/Mnj3bZMESUc/UvFFcwUcYU3kEgQUfwSF4jkUnOme2HMfYFbPhqVahPyrgqVZh7IrZgiY65lYbGwu1tzf2YSbmYR8KIUcF+qMQnnhy41gmOmRyovLyco3QQVijnJwcjBgxQugwLAL7ogX7ooUx+yLbLxKBBR+1aU/zDIefhdZwVLjcBU912ySsQCKHc9lPAkQkDJFKhdF3yVCItvunDRMV4PQVZwGisgz8+6KFqfrCqKeQExGZgjVuFNdPXa6zva+6wsyRGFfG3iLM9TiNaS7nMdfjNDL2FrV7vUYuRyX66XytUtPXFCESNTPo7CoiIiFUy9yBSh3tFrxRXKVEBpmOhOaqxBnWOnaRsbcISxY7oAD3NzZUA2cX5yMBRRgf6qH3vn6iSlRoZDrarwJW2xtkDTiSQ0QWzxo3iruwNh51t/w7sg52uLA23mjPaCps1sjkZils3hBdhgJ4abUVwAsbosvavS/u5QuwQ51Wmx3qEPfyBWOHSKSFSQ4RWTyPADlqUpKQ5hmOLOfJSPMMR42Fn049KnIiMtftR4FEjiuQoUAiR+a6/RgVOdEo79+6sFlmpsLmK9cdDWpvMmvlKLyzLBPDRAVwxhUMExXgnWWZmLVylCnCJGrG6SoisgrWuFHcqMiJQORPzUWVxpyYGbpSAXvUa7XZox5DVyqASNMUNvfvXQ1U62nvwJ2LRuO+fClyc2vh4+OGOxfJAHDdC5kWR3KIiEzMKSsLffz80NfTE338/CBOT+/2ewpR2Lx8sws8ka/V5ol8LN/s0u59KpUIISFOSEy0R1ZWPyQm2iMkxAkqlajd+4i6i0kOEZEJidPTcXtUFCQFBRBXVkJSUIA+wcHdTnQqJW0LeYHGwmZTGR/qgYTtNXjA8RjGSH7AA47HkLC9pt2iYwBQKqXIy5NoteXlSaBUSk0WKxHA6SoiIpNyVCggVqu12kT19XBUKFCVnd3l972wNh5uK2ZrTVk1FTabcr3S+FAPfNJBUnOr4mLd/54uKeG/s8m0+A0jIgDmX6nTmjUe2dAZx+f8G9cLLqEeYlT///buPC7Kav8D+GccRCCVSZBNZUBFRQsXFEpcUq9tLojecfndm4nKK3EpyyW4cVOLQsPKREUviplJIaWi5S27xXXBBbVcbqahyLggIBCg4ojA/P5AhgYGmIFn9s/79fKPOXPmec5z5gBfzwo7HEU/1XuikpYNK9Wd2Hy9lSfS2o9Dm/dXmVwdurtXaUx3c9OcTiQUBjlEZJSVOjXM8cgGbRwNWYPn05bDAQrYoAoOUOBpnFEFOkrHlve39JkzGI6FZ5H77UFUtrLBc6W7TbIOo6IU8PZW783y9q5EVJTCSCUia8Egh4gaX6mjZwXhMfCqyFJL86rIQkF4jF7uJ2SPlUguh31YGB4bOxb2YWEQyWuDiuFpK1F3Wq0IQD9chNLGBmXxwu2XY+g61JVUqsSePfcgk5XD378UMlk59uy5B6mUq6tIvzgnh4iMegRBc45sSE9vhfBwBxQXiyCRKBEfX4agoKaHPmp6rGoCOkllCdwixuMUdN+/RiSX47EJEyC+elWVJj51Cvf27IFSKoVtnc3vatiiHHdT96EqKEin+zXGHI69kEqVSEi4z/OayKDYk0NERlmpU6NM4q45vYEjG9LTWyE4uC2uXROjtLQVrl0TIzi4LdLTm/51JmSPlV10tFqAAwDiq1dhFx0NACiHrcbPlcNW0AAH0L0OiawFgxwiMsgRBA3R9ciG8HAHVFSoDwRVVIgQHt74rruAsD1WrW5p7j1plVvde3JwRES9re6Uj9KFpqkO74raYkD7y/WG0XQhl4sQFmaPsWMfQ1iYfYv2tamZXI4Rb5jcxGiyXByuIqLqlTrYi85vhaNdZQnuiB1x4714wY4gaIxHkBQ5qbtxODwGDiW5KHN0g3N8ZINHNhQXa/5DW1LS9B9gIQ/NrHLX3HtS5VbdezJ490J8F1I9N8cW5SiHLQ6OiMDg3Qs1fk4kl8MuOhqtbt1Clbs7FFFRUEq1O7biz3UoKbqKbvd/RdvKu8D/TgH/O6U2jKatmg38/ry/zalT4mbNpVFNLq+ZN3QXyA7OQI6JH81B5o9BDhEBqD2CAKg+F9qQZ0PrcmSDRKJEqYYTyR0dm/7DK+TeMoqoKIhPnVIbsqr09oYiKkr1evDuhXiIhbjwaB5KQyFjU/N7tFFTh/ZhYbBNOaH23rWrVfjHuBLc8HwM7u5ViIpSNBmoNLaBX0LCfa3KVKMgPKY2wHnEqyILh8NjzO6oDjIvHK4iIrMSH18GGxv1P9A2NtWTj5si5KGZSqkU9/bsQblMhoqhQ1Euk+ncW1Kjqfk9uqg7jHYVUozGf/DltaE4csRG6yMVhNzAzxwmRpNlYk8OEZmVoKAqpKbeRXi4A0pKRHB01H51FSBsj5VSKsX9hIQWXKFaU/N7dFF3GO2fiMYVdFdL06ZHRsgN/Mok7oCG3jdOjCZ9Y08OEZmdoKAqnDt3F3L5HZw7d1frAMdUNTW/RxcPZsyA0qb2/683ofkIhqZ6ZITcwE/XyeVEQmGQQ0RkZIqoKFR6e6ul1Z3fo602n34KUUXtnKNOyNGYr6kemT9v4Dd0aEWLNvDzCJLifupuHPacipNth+Kw51Tc56RjMgAOVxERaZCTLkdBeAwcim+hTOLe6IqvlqqZ32MXHY1WubmocnPTaXXVn9Ud+noXUTiOp9SGrLTtkanZwE8INROjMzMz4cfNAMlAGOQQEdVRb8lzqf6XPAs1v6fu0Jc35PgBf8E/PLfjpvRpuLlpt7qKyBIwyCEiqsOclzxrWtru6d0Km/Y4Qim9Z8SSERkegxwiojrMecmzkENfROaOQQ4RUR1CLXle3uVzbLwTinL0gy3KMafdViy//neBStkwoYa+mkvT7s0HPi/E56uLUah0gpPoJ/x9sQSj3xpgtDKSdWCQQ0RUh3N8JLKDM+D1pyErXZc8L+/yOdbcmQugetM9BWyqX3fZYJBAx1g07d585burePPOF8jCiOoEJXA+9jKAnxnokF5xCTkRUR1/XvJ82vGZZi153ngnFDUBTi3Ro3TLpWn35vfuvIasOhsSZqE7Pl+t+cBUIqGwJ4eISANdztPSpBy2jaQ/bPZ1TZ2m3Zsb2pCwUOmk7+KQlWNPDhGRHtiiXKd0S6Fp9+aGNiR0EhXquzhk5RjkEBHpwZx2WwHU3YtG+Sjdcmnavfmtdp+gKy6rpXXFZfx9scSQRSMrxOEqIiI9WH7970CXDY9WV9kadHWVMWlawt4pKgqrPr+Oz1eff7S6qpCrq8ggGOQQEenJ8ut/x3I8RGbmBfj4+ABoXoCjaUm2Ke97o2kJ++i3pBj9FpCZmQkfn75GKhlZGwY5REQmTNOSbPGpU7i3Z49JBzpEpoBzcoiITJimJdniq1dhFx1tpBIRmQ8GOUREJkzTkmwAaJWr3RETOelynPObg8uewTjnNwc56XIhi0dk0jhcRURkwjQtyQaAKrf6R0zkpMtREB4Dh+JbKJO4Qzx3Ojyi5hv0NHUiU8Igh4jIhGk6VbzS2xuKqCi1fDnpctgHh6gFNHcivkE73FXLZy6nqRMJgUEOEZEJ0/ZU8YLwmNoA55G6AU4NczhNnUgIDHKIiEycNqeKOxRrnrujia6nqROZK6NPPN68eTP8/Pzg6uqK4cOH4+jRow3mPXz4MKZNm4aePXvC3d0dgwcPxvbt2w1YWiIi01Qm0Tx35w7aqr3W9TR1InNm1CBn165diIiIwKJFi3Do0CEEBARAJpPh+vXrGvNnZGSgT58+2LZtG44dO4ZZs2Zh4cKFSElJMXDJiYj0o7mroZzjI5Ft01UtLdumK86v3Nmi09TrSlnyM36QzMQZyTj8IJmJlCU/N/taTRHJ5bAPC8NjY8fCPiwMIjlXhpFuRMXFxXUPVzGYUaNGoU+fPli7dq0qbcCAAQgODsayZcu0usaMGTNQWVlp8B6d6l07fQx6T1PFuqjFuqjFuqilbV3UTB72+tPcmmybrloHJqrVVSW5KHN0g3N8pKCrqFKW/IxnEmajO2rLdxld8d+wzZDFandEg7Z1oWkTxEpvb4vaBJE/I7X0VRdG68kpLy/HmTNnMHLkSLX0kSNH4sSJE1pf586dO5BIeMgbEZm/gvAYtQAHqF4NVRAeo9XnPYKk8Du3Ed3le+B3bqPgy8QlCevUAhwA6I4sSBLWCXofgJsgkjCMNvG4sLAQlZWV6Nixo1p6x44dkZ+fr9U1vvvuOxw8eBDff/99o/kyMzObXU5jXNccsS5qsS5qsS5qaVMX9kU3NabbFd00ibrsiNsNputSPm3y9sjKgq2GdEVWlknUhVAs6VlaqrG6aG4vj9FXV4lEIrXXSqWyXpomx48fR1hYGFatWgV/f/9G8+qjC4zdjLVYF7VYF7VYF7W0rYtzHTpB06pvRYdO8DOBuvwBHTWm30ZH9NOyfNrWhV3XrsDp0xrTLaVd8WeklsUNVzk5OUEsFtfrtSkoKKjXu1PXsWPHIJPJEBkZiVmzZumzmEREBtPQ5GFTWQ1VHDYfl6FevsvoiuKw+YLfSxEVhUpvb7U0TZsgEjXGaEGOra0t+vXrh7S0NLX0tLQ0BAYGNvi59PR0yGQyLF26FHPnztV3MYmIDMYjSIr7qbsFXQ0lJFnsAPw3bDO+wkQcxFB8hYk6TTrWRc0miOUyGSqGDkW5TGZRk47JMIw6XDVv3jy88sor8Pf3R2BgIBITE5Gbm4vQ0FAAwIoVK3D69Gns3bsXQPU+OVOmTMGsWbMwefJk5OXlAQDEYjGcnZ2N9hxEZHnqngMl9EqlhngESU36yAVZ7AAgNtEg99JmE0Sixhg1yJk4cSKKiooQGxuLvLw8+Pr6YufOnfD09AQA5Obm4uqfZtcnJSWhrKwMcXFxiIuLU6V36dIF58+fN3j5icgyaToHigdbEpkfo088nj17NmbPnq3xvfj4+Hqv66YREQlN0zlQPNiSyPwY/VgHIiJtNHcn4OZo6BwoHmxJZF6M3pNDRNQUQw8flUncgVIN6TzYksissCeHiExeS3cC1pWpL+UmIu2wJ4eITJ6hh488gqTISd2Nw3o8B4qI9I9BDhGZPGMMH5n6Um4iahqHq4jI5HH4iIiagz05RGTyOHxERM3BIIeIzAKHj4hIVxyuIiIiIovEIIeIiIgsEoMcIiIiskgMcoiIiMgiMcghIiIii8Qgh4iIiCwSgxwisjiJ792Gz+MP0FnSCj6PP0Die7eNUo6Mr3PwV4/zmBPYCn/1OI+Mr3OMUg4ia8V9cojIoiS+dxtLY6WoQGsAwF1lOyyNfRyAHDPf6miwcmR8nYPZs+xxDUOqE8qA32dlYzNyEDDJw2DlILJm7MkhIosSs7q9KsCpUYHWiFnd3qDl+GBBIa7BSy3tGrzwwYJCg5aDyJoxyCEii3Jfaa9Tur788cBBp3QiEh6DHCKyKPai+zql68vjbcp0Sici4THIISKLErm4FDZ4qJZmg4eIXFxq0HIsjXOCJ7LV0jyRjaVxTgYtB5E148RjIrIo1ZOL5YhZ3R73lfawF91H5OJSg046BoCASR7YjBx8sOAI/lDY43G7+1ga58RJx0QGxCCHiCzOzLc6YuZbAFAFoA0AwwY4NQImeeCrSR7IzMyEj8+TRikDkTXjcBURWZWcdDnO+c3BZc9gnPObg5x0ud7udSAkAWck49BqUBjOSMbhQEgCAGDvkuMokvRFlUSKIklf7F1yXG9lILJm7MkhIquRky6HfXAIhlZkVSeUAtnBGchJ3Q2PIKmg9zoQkoBBaXHwwjVVWnaaHF/2uo3Q3I9giwoAwOMoQaeEsUjGNxgf+5SgZSCyduzJISKrURAeA6+aAOcRr4osFITHCH4vl7S9agEOAHjhGv6WG6cKcGrYogJDEl4RvAxE1o5BDhFZDYfiW5rTS3IFv1c73NGY3gblGtMdUSJ4GYisHYMcIrIaZRJ3zemOboLf6w7aaUx/AFuN6SVwFLwMRNaOQQ4RWQ3n+Ehk23RVS8u26Qrn+EjB71Xk1BXZ8FS/FzyRJP4byutMhyyHDY6EbRK8DETWjhOPichqeARJkZO6G4fDY+BQkosyRzc4x0cKPukYAMb5XsE3R3pBDina4i7uoi3+gD1mDr6EL3p9gyEJr8ARJSiBI46EbeKkYyI9YJBDRFbFI0gKj3Mb9X6fKnd3TEBKvfRyN1l1QBN7FgDQAcB4vZeGyDpxuIqISA8UUVGo9PZWS6v09oYiKspIJSKyPgxyiIj0QCmV4t6ePSiXyVDq749ymQz39uyBUir80BgRacbhKiIyCznpchSEx8Ch+BbKJO56m0sjpOwbrRFzYgFuFtmj0+37iLzRGnVjHHN8LiJzwSCHiEyeIXcqFoo8PQchwfbIqhhanXAXyAjOxu7UHEiDqg/pNMfnIjInHK4iIpNnyJ2KhRITXoCsCi+1tKwKL8SEF6hem+NzEZkTBjlEZPIMuVOxUG4VO2hMzy2pTTfH5yIyJwxyiMjkGXKnYqG4S8o0prs51qab43MRmRMGOURk8gy5U7FQIuOd0dUmWy2tq002IuOdVa/N8bmIzAknHhORyTPkTsVCkQZ5YHdqDmLCD+NmkR06dVAgMt5ZNekYMM/nIjInDHKIyCwYaqdiIUmDPLDxnAcyMzPh4+OnMY85PheRueBwFREREVkkBjlERERkkRjkEBERkUVikENEViX/63QoPPpC5CSFwqMv8r9O19u9RHI57MPC0GPOHNiHhUEkl+vtXkRUHyceE5HVyP86HV6zgtEaFQCA9mUl6DArGNlIhcukIEHvJZLL8diECRBfvQpbADh9GuJTp3hIJ5EBsSeHiKxG+wVzVQFOjdaoQPsFcwW/l110NMRXr6qlia9ehV10tOD3IiLNGOQQkdWwf1DcQHqJ4PdqdUvzkQ2tcnlkA5GhMMghIqtxv42kgXRHwe9V5a75yIYqNx7ZQGQoDHKIyGqUxm3AwzpTER/CBqVxGwS/lyIqCpXe3mppld7eUERFCX4vItKMQQ4RWQ2XSUHI3pKKPAcpSsUS5DlIkb1F+EnHAKCUSnFvzx6Uy2Qo9fdHuUzGScdEBsbVVURkVVwmBQGTzkIJwO7RP31RSqW4n5Dw6FgHHz3eiYg0YU8OERERWSQGOURERGSRjB7kbN68GX5+fnB1dcXw4cNx9OjRRvP/+uuvePHFF+Hm5gZfX1+sWrUKSqXSQKUlIn2o2Rn4sbFjzWZn4Jx0Oc75zcFlz2Cc85uDnPT6ZZ4u2YtvJa/gzqDF+FbyCqZL9hqhpETWy6hzcnbt2oWIiAh8+OGHeOqpp7B582bIZDIcP34cXbp0qZe/tLQUISEhGDx4MH766SdkZmZi3rx5cHBwwIIFC4zwBETUUn/eGbiGqe8MnJMuh31wCIZWZFUnlALZwRnISd0Nj6DqMk+X7MWHWIHuuKL6XABOYboE+Kx4vDGKTWR1jNqTs379evzf//0fXn75ZfTs2ROxsbFwdXVFYmKixvwpKSm4f/8+4uPj0bt3bwQHB+O1117Dhg0b2JtDZKbMcWfggvAYeNUEOI94VWShIDxG9XoKvlULcACgO65gCr41SBmJyIg9OeXl5Thz5ky9HpiRI0fixIkTGj+TkZGBp59+Gvb29qq0UaNG4b333oNcLoeXl5fGz2VmZgpWbkNc1xyxLmqxLmppUxc9srKqz3aqQ5GVZbJ1aV90U2O6XdFNVZndoXlnYzfkmexzGYq1P/+fsS5qNVYXzV2daLQgp7CwEJWVlejYsaNaeseOHZGfn6/xM/n5+fDw8KiXv+a9hoIcfSzd5JLQWqyLWqyLWtrWhV3XrsDp0xrTTbUuz3XoBNytn67o0Al+j8r8LTTvbJwLV/ib6HMZAn9GarEuaumrLow+8VgkEqm9ViqV9dKayq8pnYjMgznuDOwcH4lsm65qadk2XeEcH6l6nYwxuIxuankuoxuSMcYgZSQiI/bkODk5QSwW1+u1KSgoqNe7U8PFxUVjfgANfoaITFvNzsB20dFolZuLKjc3KKKiTHbSMQB4BEmRk7obh8Nj4FCSizJHNzjHR6omHQPVk4unS6rn5rghD7lwRTLGcNIxkQEZLcixtbVFv379kJaWhgkTJqjS09LSMH685l8CAQEBWL58ORQKBezs7FT53d3dITXhX4hE1LianYHNiUeQFB7nNjaapzqgGY/MzEz4+/iwD4fIwIw6XDVv3jwkJSXhs88+w6VLl/Dmm28iNzcXoaGhAIAVK1aoBTx//etfYW9vj7lz5+LChQvYu3cv1qxZg7lz53K4ioiIiNQYdZ+ciRMnoqioCLGxscjLy4Ovry927twJT09PAEBubi6u/mlpqaOjI3bv3o3FixdjxIgRkEgkmDdvHubPn2+sRyAiIiITZfQDOmfPno3Zs2drfC8+Pr5eWp8+ffDvf/9b38UiIiIiM2f01VVERERE+sAgh4iIiCwSgxwiIiKySAxyiIiIyCIxyCEiIiKLxCCHiIiILBKDHCIiIrJIouLiYqWxC0FEREQkNPbkEBERkUVikENEREQWiUEOERERWSQGOURERGSRGOQQERGRRWKQQ0RERBaJQY4Gn376KcaOHQtPT09IJBLI5XKtPpeamorAwEC4uLggMDAQ+/btq5dn8+bN8PPzg6urK4YPH46jR48KXXxBPXjwAEuWLEHXrl3h4eGBqVOn4ubNm41+ZsyYMZBIJPX+PfXUU6o8MTEx9d7v0aOHvh+nRZpTFzt27NBYFwqFQi2fNbSLbdu24YUXXoCXlxc8PT0xduxYHDt2TC2PObQLXb+rX3/9FS+++CLc3Nzg6+uLVatWQalU37njyJEjGD58OFxdXdG3b18kJibq8xEEo0tdHD58GNOmTUPPnj3h7u6OwYMHY/v27fXyaPp5+f333/X9KC2mS13I5XKNz/mf//xHLZ81tAtNP/M1/27fvg2gZe2CQY4GZWVlGDlyJCIiIrT+TEZGBmbOnAmZTIbDhw9DJpNhxowZOHXqlCrPrl27EBERgUWLFuHQoUMICAiATCbD9evX9fEYgoiMjMS+ffuwZcsW7N+/H3fu3MGUKVNQWVnZ4Gc+//xzXLp0SfXv3LlzaNeuHSZMmKCWz8fHRy2fqf9hb05dAICDg4Pac166dAl2dnaq962lXRw5cgQhISFITU3Fjz/+CB8fH0yaNAlXrlxRy2fK7ULX76q0tBQhISFwcXHBTz/9hJUrVyIuLg7r1q1T5cnOzsbkyZMREBCAQ4cO4Y033sDSpUuRmppqqMdqFl3rIiMjA3369MG2bdtw7NgxzJo1CwsXLkRKSkq9vMePH1drA926ddP347RIc3+Gv/76a7XnHDZsmOo9a2kXCxYsqPf7MSgoCEOGDEHHjh3V8janXXAzwEb88ssvGDFiBM6ePQupVNpo3tDQUPzxxx/Ys2ePKi04OBjOzs7YsmULAGDUqFHo06cP1q5dq8ozYMAABAcHY9myZfp5iBYoKSlB9+7dsX79ekyePBkAcOPGDTz55JP46quvMGrUKK2us3PnTsyZMwfnzp1D586dAVRH73v37q33P3lT1dy62LFjB5YuXdpoL4e1tgulUonwtYDKAAAOF0lEQVSePXti0aJFeOWVVwCYfrvQ9bvasmULli9fjt9//x329vYAgNjYWCQmJuLChQsQiURYtmwZ9u3bh59//ln1uQULFuDixYv44Ycf9P9QzSREu50xYwYqKytVPTqHDx/GuHHjcOXKFTg5Oeml3Pqga13I5XL07dsXaWlp6N+/v8ZrWmu7uHHjBvz8/LBp0ybIZDIALWsX7MkRyMmTJzFy5Ei1tFGjRuHEiRMAgPLycpw5c6ZenpEjR6rymJozZ87g4cOHamXu3LkzevbsqVOZt23bhtGjR6sCnBrZ2dnw9fWFn58fZs6ciezsbKGKLriW1MX9+/fxxBNPoHfv3pgyZQrOnj2res+a20V5eTkUCgUkEolauqm2i+Z8VxkZGXj66adVAQ5Q/Xvh1q1bqmHwjIwMjb87fvnlFzx8+FDgpxCGUO32zp079b5/AHjmmWfQs2dPjB8/HocOHWpxefWpJXXx0ksvoXv37njuuefq9dBYa7vYvn07HB0dMX78+HrvNaddMMgRSF5eXr2utY4dOyI/Px8AUFhYiMrKykbzmJr8/HyIxeJ6kbMuZb58+TLS09Mxffp0tfSBAwdiw4YNSElJwdq1a5GXl4dnn30WRUVFgpVfSM2tCx8fH6xbtw5JSUnYvHkz2rRpg+eff141RGOt7QIAoqOj0bZtW7zwwguqNFNuF835rvLz8zXmr3mvsTwVFRUoLCwUqviCEqLdfvfddzh48CBmzJihSnNzc8NHH32E7du3Y/v27fDx8UFwcDDS09OFLL6gmlMXbdu2xbvvvoutW7ciJSUFw4YNQ2hoKJKTk1V5rLFdVFVVYceOHZg6dSratGmjSm9Ju7DR/THMU3R0NFavXt1onn379mHo0KHNvodIJFJ7rVQq66Vpk0fftK2LhuhS5m3btsHNzQ3PPfecWvro0aPVXg8cOBD9+vVDUlIS5s+fr9W1haDvuggICEBAQIDqdWBgIIYOHYpNmzbhgw8+UKVbW7uIj4/Hp59+ij179qB9+/aqdFNpF43R9bvSlL9uujZ5TFFz2+3x48cRFhaGVatWwd/fX5Xu4+MDHx8f1euAgABcu3YNcXFxCAoKEq7geqBLXTg5OWHBggWq1/3790dRURE++eQTTJkypdFrako3Nc1tFwcOHMCNGzfq/ae4Je3CaoKc8PBw1fyBhtQdTtGFq6trvUi1oKBAFdE6OTlBLBY3msdQtK2LkydPorKyEoWFhXB2dla9V1BQgMGDBzd5n/LycnzxxRd4+eWXYWPTeFNr27YtevXqhaysLO0eQiCGqosaYrEY/fr1Uz2nNbaL+Ph4vPfee0hJSVH7A6eJsdqFJs35rlxcXDTmB2p7dBrKY2Njgw4dOghVfEG1pN0eO3YMkydPRmRkJGbNmtXkvfz9/bFr164WlVefhPoZ9vf3x44dO1Svra1dANX/KQ4MDISvr2+TebVtF1YzXOXk5IQePXo0+s/BwaHZ1x80aBDS0tLU0tLS0hAYGAgAsLW1Rb9+/RrNYyja1kW/fv3QunVrtTLfvHkTly5d0qrM33zzDQoLC/HSSy81mVehUCAzMxOurq4tejZdGaouaiiVSvz666+q57S2drFu3TpER0cjOTkZTz/9dJNlMla70KQ531VAQACOHTumtmVAWloa3N3dVYsZAgIC8N///rfeNfv374/WrVsL+xACaW67TU9Ph0wmw9KlSzF37lyt7nX+/HmT+P4bItTPcN3ntKZ2AQC3bt3CgQMH6vXiNETbdiGOiIhYrtUVrUheXh6ysrKQmZmJffv2YeTIkbh37x5sbW1VEwjHjx+P33//Hc888wwAwN3dHe+//z5at24NJycnbNu2DTt27MAnn3wCDw8PAEC7du0QExMDNzc32NnZITY2FkePHsW6devg6OhorMdtkJ2dHXJzc5GQkIAnnngCJSUleP3119G+fXusWLECrVpVx8h166JGREQEvLy8MGfOnHrXjoqKgq2tLaqqqnD58mUsWbIEWVlZ+Pjjjy2qLlauXIkHDx6gVatWuHbtGt59912kpaXho48+srp2sXbtWrzzzjuIj49H3759ce/ePdy7dw+VlZWqJfWm3i6a+q5WrFiBjz76CNOmTQMAdOvWDVu3bsX58+fh4+ODY8eO4e2338bChQtVv/S9vb2xZs0a3L59G126dMH+/fvx4YcfIjo6Gr169TLm4zZK17o4fPgwJk+ejJkzZyI0NFT1/SsUCtV/MDds2IC8vDyIxWLk5eVhw4YN+OyzzxAdHY2ePXsa83EbpWtdJCUl4eLFi7CxscEff/yBpKQkfPzxx1i0aJFqeNta2kWNjRs34pdffsH69evrBXEtaRdWM1yli8TERKxatUr1uqYLf/369fjb3/4GALh69So6deqkyhMYGIjExERER0cjJiYG3t7eSExMxMCBA1V5Jk6ciKKiIsTGxiIvLw++vr7YuXMnPD09DfRkunv//fchFosRGhoKhUKBYcOGYePGjRCLxao8desCqF4hc+jQoQY3r8rJycHs2bNVQx4DBw7EDz/8YHF1UVJSgtdeew35+flo3749/Pz8sH//frVhGmtpFwkJCXj48CFCQ0PVrjVt2jTEx8cDMP120dR3lZubi6tXr6ryOzo6Yvfu3Vi8eDFGjBgBiUSCefPmqc0v8vLyws6dO/GPf/wDiYmJcHNzw6pVqxAcHGzw59OFrnWRlJSEsrIyxMXFIS4uTpXepUsXnD9/HgDw8OFD/POf/8StW7dgZ2enuuazzz5r2IfTka51AQCrV6/G9evXIRaL0a1bN6xbt05tPo61tAuguod7+/btkMlkGkdUWtIuuE8OERERWSSrmZNDRERE1oVBDhEREVkkBjlERERkkRjkEBERkUVikENEREQWiUEOERERWSQGOURkksLDw/Hkk0+azXWJyPQwyCEiwezYsQMSiUT1z8nJCb1798b8+fORm5tr7OI1KDY2Ft98842xi0FEAuOOx0QkuIiICHh7e+PBgwc4fvw4kpKSkJ6ejqNHj6qORjGWtWvXoqqqSi1t9erVmDhxIsaOHWukUhGRPjDIISLBjRo1CoMGDQIATJ8+HY8//jjWr1+P/fv3Y9KkSUYpU1lZGRwcHEz2cEMiEh6Hq4hI74YNGwag+kyzqqoqrFmzBv7+/nBxcYGvry+WLFmCkpKSJq+zY8cOBAcHo0ePHnBxcYG/vz/WrFlTr2dmzJgxGDRoEP73v/9h3Lhx8PDwwKJFiwDUn5MjkUjw4MEDfPHFF6phtjFjxuDKlSuQSCRYv359vXJcvHgREokE//rXv1pSLUSkZ+zJISK9qzmQr0OHDli0aBG2bt2KF154AXPmzMFvv/2GLVu24PTp0/j+++8b7WlJSEiAj48P/vKXv8De3h5paWlYvnw5SktL8fbbb6vlLSkpwcSJEzFu3DhMmjSpwVPMN23ahPnz52PgwIGYMWMGAMDFxQXdunVDYGAgkpOTMW/ePLXPJCcno3Xr1kbrlSIi7TDIISLBlZaWorCwEAqFAidOnMAHH3wAe3t7+Pj44PXXX8fkyZPVekF8fHwQGRmJL774AtOnT2/wuvv371c7pXj27NlYsGABNm3ahDfffBNt2rRRvZefn4+VK1dizpw5jZZ1ypQpePXVV+Hl5aV2CjRQfUL6woULcfHiRfTq1QtA9YnJKSkpGDVqFJycnHSqFyIyLA5XEZHgJk2ahG7duqFPnz6YOXMmXF1dkZycjJMnTwIAXn31VbX8M2fORPv27fH99983et2aAKeyshLFxcUoLCzEkCFDcO/ePWRmZqrltbGxUfXMNFdISAjs7OyQnJysSjty5Ahu3LiBqVOntujaRKR/7MkhIsGtWrUKPXv2RJs2bdC5c2d07twZIpEIu3fvhkgkgo+Pj1r+Nm3aQCqV4tq1a41e99ixY3jnnXdw+vRplJeXq71Xd06Pm5sb7OzsWvQcjo6OePHFF5GSkoK3334bIpEIO3fuRPv27fH888+36NpEpH8McohIcAMGDFCtrtKWUqmESCRq8P3s7GyEhISga9euiImJQefOndGmTRucPXsWy5Ytqzf5WKil6tOmTcOuXbuQnp6OQYMGITU1FRMmTGhxAEVE+scgh4gMxtPTE0qlEpmZmXjiiSdU6eXl5bh27RqGDh3a4Gf3798PhUKBL7/8Ep6enqp0uVze4nI1FlyNHDkSbm5uSE5ORkFBAUpLS+vN3SEi08Q5OURkMM8++ywA1FuWvXXrVpSWluK5555r8LNisRhAdY9PjQcPHgiyjNvBwQHFxcUN3lcmkyE1NRXbt2+Hp6cnBg8e3OJ7EpH+sSeHiAymT58+CA0NVQU1I0aMwG+//YatW7diwIABmDZtWoOfHTVqFGxtbTF16lTMmDED5eXl+PLLL9GqVcv/r9a/f38cPHgQcXFx8PDwgLOzM4YPH656f9q0aYiLi8OPP/6IxYsXN9rzQ0Smg0EOERnUhx9+CKlUis8++wwHDhyAk5MTZs2ahaioqEb3yOnevTt27NiBd955B8uWLYOTkxOmTp2KIUOGICQkpEVlWrlyJd544w2sXLkS9+7dQ1BQkFqQ07t3b/Tt2xdnz57lUBWRGREVFxcrm85GRGTdRo8ejaqqKvz444/GLgoRaYlzcoiImnDhwgWcPHmy0eE0IjI97MkhImrAhQsXcObMGWzcuBE3b97E2bNn0bZtW2MXi4i0xJ4cIqIGpKamYt68eSgrK8OWLVsY4BCZGfbkEBERkUViTw4RERFZJAY5REREZJEY5BAREZFFYpBDREREFolBDhEREVmk/wdiBoTZ6dd9CgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting the polarity and subjectivity\n",
    "plt.figure(figsize=(8,6))\n",
    "for i in range(0,df.shape[0]):\n",
    "    plt.scatter(df['Polarity'][i], df['Subjectivity'][i],color='Blue')\n",
    "    plt.scatter(oldf['Polarity'][i],oldf['Subjectivity'][i],color='Red')\n",
    "plt.title('Sentiment Analysis')\n",
    "plt.xlabel('Polarity')\n",
    "plt.ylabel('Subjectivity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptweets=df[df.Analysis=='Positive']\n",
    "ptweets=ptweets['Tweets']\n",
    "\n",
    "round( (ptweets.shape[0]/df.shape[0])*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptweets=df[df.Analysis=='Negative']\n",
    "ptweets=ptweets['Tweets']\n",
    "\n",
    "round( (ptweets.shape[0]/df.shape[0])*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptweets=df[df.Analysis=='Neutral']\n",
    "ptweets=ptweets['Tweets']\n",
    "\n",
    "round( (ptweets.shape[0]/df.shape[0])*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Analysis'].value_counts()\n",
    "\n",
    "plt.title('SentimentAnalysis')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Counts')\n",
    "df['Analysis'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptweets=oldf[oldf.Analysis=='Negative']\n",
    "ptweets=ptweets['Tweets']\n",
    "\n",
    "round( (ptweets.shape[0]/oldf.shape[0])*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptweets=oldf[oldf.Analysis=='Neutral']\n",
    "ptweets=ptweets['Tweets']\n",
    "\n",
    "round( (ptweets.shape[0]/oldf.shape[0])*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oldf['Analysis'].value_counts()\n",
    "\n",
    "plt.title('SentimentAnalysis')\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Counts')\n",
    "oldf['Analysis'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}