CoronaTweetAnalysisAndSummarization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Sentiment Analysis on Corona-Tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imporing basic libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "#To display plots inline; within the notebook window\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Data/2020-03-28 Coronavirus Tweets.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['status_id', 'user_id', 'created_at', 'screen_name', 'text', 'source',\n",
       "       'reply_to_status_id', 'reply_to_user_id', 'reply_to_screen_name',\n",
       "       'is_quote', 'is_retweet', 'favourites_count', 'retweet_count',\n",
       "       'country_code', 'place_full_name', 'place_type', 'followers_count',\n",
       "       'friends_count', 'account_lang', 'account_created_at', 'verified',\n",
       "       'lang'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Countries with most tweets\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# count the occurrence of each class \n",
    "data = dataset[\"country_code\"].value_counts() \n",
    "# get x and y data \n",
    "points = data.index[:20]\n",
    "frequency = data.values[:20]\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "plt.title(\"Countries with most tweets\")\n",
    "plt.xlabel(\"Number of Tweets\")\n",
    "plt.ylabel(\"Country\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tweets from verfied/ normal account\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# count the occurrence of each class \n",
    "data = dataset[\"verified\"].value_counts() \n",
    "# get x and y data \n",
    "points = data.index\n",
    "frequency = data.values\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "ax.set_yticks([0,1,2])\n",
    "ax.set_yticklabels([\"Normal\",\"Verified\"])\n",
    "plt.xlabel(\"Number of Tweets\")\n",
    "plt.ylabel(\"Type of Account\")\n",
    "plt.title(\"Tweets from verfied/ normal account\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tweets existing/ new account\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# count the occurrence of each class \n",
    "temp_data = dataset.copy()\n",
    "temp_data[\"account_created_at\"] = [i[:4] for i in temp_data[\"account_created_at\"]]\n",
    "data = temp_data[\"account_created_at\"].value_counts() \n",
    "# get x and y data \n",
    "points = data.index\n",
    "frequency = data.values\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "ax.set_yticklabels(data.index)\n",
    "plt.xlabel(\"Number of Tweets\")\n",
    "plt.ylabel(\"Languages\")\n",
    "plt.title(\"Tweets existing/ new account\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yAknSNGlzSvm1Sa5h8DjHJwDHATuBj8y3s8kLJEnTpJURbpLTgecCp1bV9iSXMsgS9N2q2tlGHZIkjbO2Rrirgf/bBNunAKe0VK4kSROhrYD7SWBlkq3AbzGYVpYkSY1Uzc0f370NGzbUzMzMcjdDkqRFSbK5qjbszb4++EKSpA4YcCVJ6oDJCyRJ6oAjXEmSOtDmox2/2FZZkiRNmtYCblU9o62yJEmaNG2OcO9vXk9PcmmSi5LckORDSdJWPZIkjaNRXcP9QeB1wPHAMcBpc3dIsjHJTJKZndu3jagZkiT1w6gC7lVVdWtVPQxsAdbN3cHkBZKkaTKqgPvg0PJOevL1I0mSlotfC5IkqQMGXEmSOtDaVG9VHdS8XgpcOrT+nLbqkCRpXPXi2uoJR61mxsc3SpImmFPKkiR1oBcj3D0lL5jLZAaSpHHjCFeSpA60+WjHtyQ5t63yJEmaJI5wJUnqwJICbpI3JrkxyaeBJzfrjk3yySSbk3w+yVNaaakkSWNsn2+aSnIy8BIGiQpWAl8CNgObgFdX1VeT/DDwB8BzWmirJEljayl3Kf8I8NGq2g6Q5GJgf+AZwIeHMvKtmu/gJBuBjQArDlm7hGZIktR/S/1aUM15vx9wT1WdtMcDqzYxGA2z6sjj5pYjSdJEWco13M8BL05yQJKDgZ8CtgM3JflZgAw8rYV2SpI01vY54FbVl4ALGeS7/Qjw+WbTmcAvJrkGuA746aU2UpKkcbekKeWqehvwtnk2PX8p5UqSNGl68WhHkxdIkiadD76QJKkDBlxJkjrQiynlxWYLmmXWIEnSuGgzecG6JNfOs/6tSZ7bVj2SJI2jkY9wq+rNo65DkqS+a/sa7ookf5jkuiSXNA/F+NMkZ7RcjyRJY6XtgHsc8J6qeipwD/AzLZcvSdJYajvg3lRVW5rlzcC6hXZMsjHJTJKZndu3tdwMSZL6pe2A++DQ8k52c424qjZV1Yaq2rDiwNUtN0OSpH7xe7iSJHXAgCtJUgda+1pQVd0MrB96/462ypYkadz14klTJi+QJE06p5QlSeqAAVeSpA70Ykp5X5MXzDKJgSSp70Y6wk3yuiQHjrIOSZLGwainlF8HGHAlSVOvlSnlJOuATwJXAj8I/DPwOeBxwGeT3FVVz26jLkmSxlGbI9wnA5uq6kTgXuCRwG3Asw22kqRp12bAvaWqLm+W/wJ45u52NnmBJGmatBlwaw/vd91o8gJJ0hRpM+A+McmpzfJLgS8A9wEHt1iHJEljqc2Aez1wVpKtwGHAe4FNwCeSfLbFeiRJGjttPvji4ap69Zx172p+JEmaar140pTJCyRJk66VgDs3NZ8kSdqVyQskSepAL6aUl5q8YCEmNZAk9YUjXEmSOtBqwE2yJsnZbZYpSdIkaHuEuwb4NwE3yYqW65Ekaay0fQ33fODYJFuAHcD9wO3AScDxLdclSdLYaDvgngesr6qTkpwO/F3z/qa5OybZCGwEWHHI2pabIUlSv4z6pqmr5gu2YPICSdJ0GXXAfWDE5UuSNBbaDrhmB5IkaR6tXsOtqruTXJ7kWuA7wB1tli9J0rhq/UlTVfWytsuUJGnc9eLRjmYLkiRNOh/tKElSB3oxwh1V8oJZJjGQJC03R7iSJHVgJAE3yReb13VJvIlKkjT1RhJwq+oZzeI6wIArSZp6oxrh3t8sng/8SJItSV4/irokSRoHo75p6jzg3Kr6yRHXI0lSry3bTVNJNiaZSTKzc/u25WqGJEmdWLaAa7YgSdI0GXXANZmBJEmMPuBuBR5Kco03TUmSptlIbpqqqoOa1x3Aj42iDkmSxkkvHu1o8gJJ0qTz0Y6SJHWgFyPcUScvaJvJECRJi+UIV5KkDrQecJO8Nsn1ST7UdtmSJI2rUUwpnw28oKpuml2RZGVVPTSCuiRJGgutBtwk7wOOAS5O8kTgQgYZg+7CrEGSpCnWasCtqlcneT7wbOAc4KeAZ1bVd9qsR5KkcTPqm6YuXijYmrxAkjRNRh1wH1hog8kLJEnTxK8FSZLUAQOuJEkdaP1rQVW1rll8S9tlS5I0rnrxaEeTF0iSJp1TypIkdcCAK0lSB3oxpTxu2YKGmTlIkrQ3Rj7CTXJpkg2jrkeSpD5zSlmSpA7scUo5yTrg41W1vnl/LnAQcDpwJYPnJq8BfrGqPp/kAOBPgOOB64EDRtFwSZLGyVKv4a6sqh9K8kLgN4DnAr8MbK+qE5OcCHxpqY2UJGncLXVK+a+b180M0vABPAv4C4Cq2gpsne9AkxdIkqbJ3gTch+bst//Q8oPN6052HS3Xngo1eYEkaZrsTcC9AzgiyaOTrAJ+cg/7fw44EyDJeuDEpTVRkqTxt8druFW1I8lbGdwgdRNwwx4OeS/wJ0m2AluAq5bcSkmSxtxe3TRVVRcAF+xm+10013CbhPMvaaNxkiRNil48acrkBZKkSeeDLyRJ6oABV5KkDvRiSnmckxdo75jkQdK0c4QrSVIHDLiSJHXAgCtJUgeWHHCTvDzJVUm2JHl/khVJ/jTJtUm+kuT1bTRUkqRxtqSbppL8APDzwGnNE6n+AHgTcNRQOr81Cxy7EdgIsOKQtUtphiRJvbfUEe6PAScDVyfZ0rw/DDgmybuSPB+4d74DTV4gSZomSw24AT5YVSc1P0+uql8BngZcCrwG+KMl1iFJ0thbasD9DHBGkiMAkhyW5Ghgv6r6CPDrwNOXWIckSWNvSddwq+qfkrwJuCTJfsAO4FeBjzbvAd6wxDZKkjT2UrXHXPEjt2HDhpqZmVnuZkiStChJNlfVhr3Z1+/hSpLUAQOuJEkdMHmBJGlqLGciFUe4kiR1wIArSVIHDLiSJHVgSQF3gcQF9yd5W5JrklyR5DFtNVaSpHG1zwF3TuKCk4CdwJnAo4ArquppwOeA/7TA8RuTzCSZ2bl92742Q5KksbCUu5SHExcAHADcCfwr8PFmn83Aj893cFVtAjYBrDryuOV/+oYkSSO0lIA7m7hgl0c3Jjm3vvf4qp1LrEOSpImwlGu4CyUukCRJc+zz6HOBxAWvaa1lkiRNkKVmC7oQuHDO6oOGtl8EXLSUOiRJmgS9uL56wlGrmVnGx21JkjRqPvhCkqQOGHAlSeqAAVeSpA4YcCVJ6oABV5KkDhhwJUnqgAFXkqQOGHAlSeqAAVeSpA4YcCVJ6oABV5KkDhhwJUnqQL6XK34ZG5HcB9y43O3oicOBu5a7ET1gPwzYDwP2w4D9MNCnfji6qtbuzY69yBYE3FhVG5a7EX2QZMa+sB9m2Q8D9sOA/TAwrv3glLIkSR0w4EqS1IG+BNxNy92AHrEvBuyHAfthwH4YsB8GxrIfenHTlCRJk64vI1xJkibasgfcJM9PcmOSryU5b7nb05YkNyf5SpItSWaadYcl+YckX21eD23WJ8kFTR9sTfL0oXLOavb/apKzhtaf3JT/tebYdH+W/1aSDyS5M8m1Q+tGft4L1bFcFuiHtyT5RvOZ2JLkhUPb3tCc041J/t3Q+nl/P5I8KcmVzflemOSRzfpVzfuvNdvXdXPG80vyhCSfTXJ9kuuS/Eqzfqo+E7vph6n6TCTZP8lVSa5p+uE397XtbfVPp6pq2X6AFcDXgWOARwLXAMcvZ5taPLebgcPnrPtd4Lxm+TzgvzfLLwQ+AQQ4BbiyWX8Y8L+b10Ob5UObbVcBpzbHfAJ4wXKfc9OuZwFPB67t8rwXqqNn/fAW4Nx59j2++eyvAp7U/E6s2N3vB/BXwEua5fcBv9wsnw28r1l+CXDhMvfDkcDTm+WDgX9uzneqPhO76Yep+kw0/0YHNcuPAK5s/p0X1fY2+6fT81+ujm9O+lTgU0Pv3wC8YTnb1OK53cy/Dbg3Akc2y0cy+P4xwPuBl87dD3gp8P6h9e9v1h0J3DC0fpf9lvsHWMeugWbk571QHT3rh7cw/x/XXT73wKea3415fz+aP1p3ASub9f9/v9ljm+WVzX5Z7r4YOoePAT8+rZ+Jefphaj8TwIHAl4AfXmzb2+yfLn+We0r5KOCWofe3NusmQQGXJNmcZGOz7jFVdTtA83pEs36hftjd+lvnWd9XXZz3QnX0zTnNVOkHhqY4F9sPjwbuqaqH5qzfpaxm+7Zm/2XXTAf+IINRzdR+Jub0A0zZZyLJiiRbgDuBf2AwIl1s29vsn84sd8Cd77rjpNw2fVpVPR14AfCaJM/azb4L9cNi14+baTvv9wLHAicBtwPvbNa32Q+97KMkBwEfAV5XVffubtd51k3MZ2Kefpi6z0RV7ayqk4DHAz8E/MB8uzWvbfVDL/pguQPurcATht4/HrhtmdrSqqq6rXm9E/gogw/WHUmOBGhe72x2X6gfdrf+8fOs76suznuhOnqjqu5o/tg8DPwhg88ELL4f7gLWJFk5Z/0uZTXbVwPfbv9s9l6SRzAIMh+qqr9uVk/dZ2K+fpjWzwRAVd0DXMrgGu5i295m/3RmuQPu1cBxzd1jj2RwUfziZW7TkiV5VJKDZ5eB5wHXMji32bsrz2JwHYdm/SuaOzRPAbY1U2CfAp6X5NBmqul5DK473A7cl+SU5o7MVwyV1UddnPdCdfTG7B//xosZfCZg0PaXNHdkPgk4jsGNQPP+ftTgItRngTOa4+f26Ww/nAH8Y7P/smj+nf4YuL6qfm9o01R9Jhbqh2n7TCRZm2RNs3wA8Fzgehbf9jb7pzs9uHD+QgZ37H0deONyt6elczqGwd1x1wDXzZ4Xg+sInwG+2rwe1qwP8J6mD74CbBgq65XA15qfXxhav4HBL+fXgXfTg5sgmnb9JYOpsR0M/rf5i12c90J19Kwf/rw5z60M/mAcObT/G5tzupGhO84X+v1oPmNXNf3zYWBVs37/5v3Xmu3HLHM/PJPB1N1WYEvz88Jp+0zsph+m6jMBnAh8uTnfa4E372vb2+qfLn980pQkSR1Y7illSZKmggFXkqQOGHAlSeqAAVeSpA4YcCVJ6oABV5KkDhhwJUnqgAFXkqQO/D/X/XZhSW0MWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tweets-languages\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# count the occurrence of each class \n",
    "data = dataset[\"lang\"].value_counts() \n",
    "# get x and y data \n",
    "points = data.index[:20]\n",
    "frequency = data.values[:20]\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "plt.title(\"Tweets-Languges\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary libraries for language processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Gayatri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Gayatri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\Gayatri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Gayatri\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('vader_lexicon')\n",
    "nltk.download('punkt')\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from wordcloud import WordCloud\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from nltk.tokenize import sent_tokenize\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk.sentiment.util import *\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract necessary columns from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting English Tweets of all countries\n",
    "tweets= dataset[[\"text\",\"country_code\"]][dataset['lang'] == 'en'].reset_index()\n",
    "tweets.drop([\"index\"],axis=1)\n",
    "\n",
    "#maintain a copy of original tweets before processing it\n",
    "tweets_original = tweets.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retaining only alphabets (removing all punctuations and numbers)\n",
    "tweets[\"text\"] = [re.sub('[^a-zA-Z]', ' ',i) for i in tweets[\"text\"]]\n",
    "\n",
    "#Converting into lower case \n",
    "tweets[\"text\"] = [i.lower() for i in tweets[\"text\"]]\n",
    "\n",
    "#Removing Emoticons\n",
    "def deEmojify(inputString):\n",
    "    return inputString.encode('ascii', 'ignore').decode('ascii')\n",
    "tweets[\"text\"]  = [deEmojify(i) for i in tweets[\"text\"] ]\n",
    "\n",
    "\n",
    "#Removing URLs\n",
    "def removeURLs(str):\n",
    "    ans = \"\"\n",
    "    clean_tweet1 = re.match('(.*?)http.*?\\s?(.*?)', str)\n",
    "    clean_tweet2 = re.match('(.*?)https.*?\\s?(.*?)', str)\n",
    "    if clean_tweet1:\n",
    "        ans=ans+clean_tweet1.group(1)\n",
    "        ans=ans+clean_tweet1.group(2)\n",
    "    elif clean_tweet2: \n",
    "        ans=ans+clean_tweet2.group(1)\n",
    "        ans=ans+clean_tweet2.group(2)\n",
    "    else:\n",
    "        ans = str\n",
    "    return ans\n",
    "\n",
    "\n",
    "tweets[\"text\"] = tweets[\"text\"].apply(lambda tweet: removeURLs(tweet))\n",
    "\n",
    "\n",
    "#Removing Stop Words\n",
    "cachedStopWords = set(stopwords.words(\"english\"))\n",
    "tweets[\"text\"] = tweets[\"text\"].apply(lambda tweet: ' '.join([word for word in tweet.split() if word not in cachedStopWords]))\n",
    "\n",
    "#Define words that we do not want to Stem or Lemmatize\n",
    "specialWords = [\"coronavirus\", \"covid\",\"quarantine\",\"coronavirusoutbreak\",\"virus\",\"corona\",\"lockdown\"]\n",
    "\n",
    "#Stemming\n",
    "ps = PorterStemmer()\n",
    "def stemWords(word):\n",
    "    if word in specialWords:\n",
    "            return word\n",
    "    else:\n",
    "        return ps.stem(word)\n",
    "        \n",
    "tweets[\"text\"] = tweets[\"text\"].apply(lambda tweet: ' '.join([stemWords(word) for word in tweet.split()]))\n",
    "\n",
    "\n",
    "#Lemmatization: \n",
    "wnl = WordNetLemmatizer()\n",
    "def lemmatizeWords(word):\n",
    "    if word in specialWords:\n",
    "            return word\n",
    "    else:\n",
    "        return wnl.lemmatize(word)\n",
    "tweets[\"text\"] = tweets[\"text\"].apply(lambda tweet: ' '.join([lemmatizeWords(word) for word in tweet.split()]))\n",
    "\n",
    "\n",
    "#Preparing corpus\n",
    "corpus=[]\n",
    "corpus = [word for tweet in tweets[\"text\"] for word in tweet.split()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Frequency Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'amp': 32961, 'case': 28028, 'coronalockdown': 17027, 'coronavirus': 133838, 'coronavirusoutbreak': 23282, 'covid': 228239, 'day': 18014, 'death': 17563, 'get': 19807, 'go': 16414, 'health': 11940, 'help': 18209, 'home': 17605, 'like': 16007, 'lockdown': 13563, 'make': 11773, 'need': 19010, 'new': 18432, 'one': 15859, 'pandem': 17225, 'peopl': 31447, 'plea': 12188, 'stay': 16708, 'take': 11982, 'test': 19287, 'time': 21205, 'trump': 12634, 'virus': 13081, 'work': 13733, 'world': 12965}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Bag of Words\n",
    "from operator import itemgetter  \n",
    "vectorizer = CountVectorizer(max_features = 30)\n",
    "cv = vectorizer.fit_transform(tweets[\"text\"]).toarray()\n",
    "terms = vectorizer.get_feature_names()\n",
    "freqs = cv.sum(axis=0)\n",
    "result = dict(zip(terms, freqs))\n",
    "print(result)\n",
    "features = []\n",
    "vals = []\n",
    "for key, value in sorted(result.items(), key = itemgetter(1), reverse = True):\n",
    "    features.append(key)\n",
    "    vals.append(value)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# get x and y data \n",
    "points = features\n",
    "frequency = vals\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "plt.title(\"Most Tweeted Words\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Term Frequency (TF-IDF)\n",
    "\n",
    "tf=TfidfTransformer(smooth_idf=True,use_idf=True)\n",
    "tf.fit(cv)\n",
    "\n",
    "# print idf values\n",
    "data = { \"Word\" :vectorizer.get_feature_names(), \"idf_weights\":tf.idf_}\n",
    "df_idf = pd.DataFrame(data)\n",
    " \n",
    "# sort ascending\n",
    "df_idf = df_idf.sort_values(by=['idf_weights'])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# get x and y data \n",
    "points = df_idf[\"Word\"]\n",
    "frequency = df_idf[\"idf_weights\"]\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "plt.title(\"IDF weights of Most Tweeted Words -> Least weights = Most Importance\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(\n",
    "    background_color='black',\n",
    "    max_words=50,\n",
    "    max_font_size=40, \n",
    "    scale=5,\n",
    "    random_state=1,\n",
    "    collocations=False,\n",
    "    normalize_plurals=False\n",
    ").generate(' '.join(corpus))\n",
    "  \n",
    "# plot the WordCloud image                        \n",
    "plt.figure(figsize = (8, 8), facecolor=\"None\") \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.title(\"WordCloud of Corona-Tweets\")\n",
    "plt.tight_layout(pad = 0) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2023</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.0516</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compound    neg    neu    pos\n",
       "0    0.0000  0.000  1.000  0.000\n",
       "1    0.2023  0.185  0.543  0.272\n",
       "2    0.0000  0.000  1.000  0.000\n",
       "3   -0.0516  0.173  0.619  0.208\n",
       "4    0.0000  0.000  1.000  0.000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = SentimentIntensityAnalyzer()\n",
    "scores = tweets[\"text\"].apply(lambda tweet: s.polarity_scores(tweet))\n",
    "scores_df = pd.DataFrame(list(scores))\n",
    "scores_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating sentiments for all tweets\n",
    "scores_df['result'] = scores_df['compound'].apply(lambda res: 'neutral' if res == 0 else ('positive' if res > 0 else 'negative'))\n",
    "scores_df['tweet'] = tweets_original[\"text\"]\n",
    "scores_df[\"country_code\"] = tweets_original[\"country_code\"]\n",
    "scores_df= scores_df.sort_values(by=['compound'])\n",
    "\n",
    "\n",
    "#Calculating sentiments wrt top 5 tweeting countries\n",
    "us = scores_df[[\"tweet\",\"result\",\"country_code\"]][scores_df.country_code == 'US']\n",
    "us_positive = us[[\"tweet\",\"result\",\"country_code\"]][us.result == 'positive']\n",
    "score_us_positive = us_positive.shape[0]\n",
    "us_negative = us[[\"tweet\",\"result\",\"country_code\"]][us.result == 'negative']\n",
    "score_us_negative = us_negative.shape[0]\n",
    "us_neutral = us[[\"tweet\",\"result\",\"country_code\"]][us.result == 'neutral']\n",
    "score_us_neutral = us_neutral.shape[0]\n",
    "\n",
    "india = scores_df[[\"tweet\",\"result\",\"country_code\"]][scores_df.country_code == 'IN']\n",
    "india_positive = india[[\"tweet\",\"result\",\"country_code\"]][india.result == 'positive']\n",
    "score_india_positive = india_positive.shape[0]\n",
    "india_negative = india[[\"tweet\",\"result\",\"country_code\"]][india.result == 'negative']\n",
    "score_india_negative = india_negative.shape[0]\n",
    "india_neutral = india[[\"tweet\",\"result\",\"country_code\"]][india.result == 'neutral']\n",
    "score_india_neutral = india_neutral.shape[0]\n",
    "\n",
    "uk = scores_df[[\"tweet\",\"result\",\"country_code\"]][scores_df.country_code == 'GB']\n",
    "uk_positive = uk[[\"tweet\",\"result\",\"country_code\"]][uk.result == 'positive']\n",
    "score_uk_positive = uk_positive.shape[0]\n",
    "uk_negative = uk[[\"tweet\",\"result\",\"country_code\"]][uk.result == 'negative']\n",
    "score_uk_negative = uk_negative.shape[0]\n",
    "uk_neutral = uk[[\"tweet\",\"result\",\"country_code\"]][uk.result == 'neutral']\n",
    "score_uk_neutral = uk_neutral.shape[0]\n",
    "\n",
    "spain = scores_df[[\"tweet\",\"result\",\"country_code\"]][scores_df.country_code == 'ES']\n",
    "spain_positive = spain[[\"tweet\",\"result\",\"country_code\"]][spain.result == 'positive']\n",
    "score_spain_positive = spain_positive.shape[0]\n",
    "spain_negative = spain[[\"tweet\",\"result\",\"country_code\"]][spain.result == 'negative']\n",
    "score_spain_negative = spain_negative.shape[0]\n",
    "spain_neutral = spain[[\"tweet\",\"result\",\"country_code\"]][spain.result == 'neutral']\n",
    "score_spain_neutral = spain_neutral.shape[0]\n",
    "\n",
    "canada = scores_df[[\"tweet\",\"result\",\"country_code\"]][scores_df.country_code == 'CA']\n",
    "canada_positive = canada[[\"tweet\",\"result\",\"country_code\"]][canada.result == 'positive']\n",
    "score_canada_positive = canada_positive.shape[0]\n",
    "canada_negative = canada[[\"tweet\",\"result\",\"country_code\"]][canada.result == 'negative']\n",
    "score_canada_negative = canada_negative.shape[0]\n",
    "canada_neutral = canada[[\"tweet\",\"result\",\"country_code\"]][canada.result == 'neutral']\n",
    "score_canada_neutral = canada_neutral.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis of Tweets around the world"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "data = scores_df[\"result\"].value_counts()\n",
    "# get x and y data \n",
    "points = data.index\n",
    "frequency = data.values\n",
    "#plot graph\n",
    "ax.bar(points,frequency)\n",
    "plt.title(\"Sentiment Analysis of Tweets around the world\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis in top 5 tweeting countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [[score_us_positive,score_india_positive, score_uk_positive, score_spain_positive, score_canada_positive],\n",
    "[score_us_negative,score_india_negative, score_uk_negative, score_spain_negative, score_canada_negative],\n",
    "[score_us_neutral,score_india_neutral, score_uk_neutral, score_spain_neutral, score_canada_neutral]]\n",
    "X = np.arange(5)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.set_xticks([0,1,2,3,4,5])\n",
    "ax.set_xticklabels([\"USA\",\"India\",\"UK\",\"Spain\",\"Canada\"])\n",
    "ax.bar(X + 0.00, data[0], color = 'g', width = 0.25, label=\"positive\")\n",
    "ax.bar(X + 0.25, data[1], color = 'r', width = 0.25, label=\"negative\")\n",
    "ax.bar(X + 0.50, data[2], color = 'b', width = 0.25, label=\"neutral\")\n",
    "plt.title(\"Sentiment Analysis in top 5 tweeting countries\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_word_list=[]\n",
    "neu_word_list=[]\n",
    "neg_word_list=[]\n",
    "pos_word_weight=[]\n",
    "neu_word_weight=[]\n",
    "neg_word_weight=[]\n",
    "\n",
    "def get_word_sentiment(text):\n",
    "    \n",
    "    tokenized_text = nltk.word_tokenize(text)\n",
    "    #print(tokenized_text)    \n",
    "\n",
    "    for word in tokenized_text:\n",
    "            if (s.polarity_scores(word)['compound']) >= 0.6:\n",
    "                pos_word_list.append(word)\n",
    "                pos_word_weight.append(s.polarity_scores(word)['compound'])\n",
    "            elif (s.polarity_scores(word)['compound']) <= -0.6:\n",
    "                neg_word_list.append(word)\n",
    "                neg_word_weight.append(s.polarity_scores(word)['compound'])\n",
    "            else:\n",
    "                neu_word_list.append(word)\n",
    "                neu_word_weight.append(s.polarity_scores(word)['compound'])\n",
    "\n",
    "for tweet in tweets[\"text\"]:\n",
    "    get_word_sentiment(tweet)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Positive Words in Tweets: 16459\n",
      "Total Negative Words in Tweets: 11410\n",
      "Total Neutral Words in Tweets: 4692731\n"
     ]
    }
   ],
   "source": [
    "print('Total Positive Words in Tweets:',len(pos_word_list))\n",
    "print('Total Negative Words in Tweets:',len(neg_word_list))\n",
    "print('Total Neutral Words in Tweets:',len(neu_word_list)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['glee', 'best', 'great', 'glorious', 'brightest', 'superb', 'freedom', 'greatest', 'amor', 'splendor']\n",
      "['scumbag', 'murder', 'shithead', 'douchebag', 'killing', 'hell', 'disaster', 'saddest', 'dx', 'dickhead']\n",
      "['rotoflex', 'unicefphil', 'youareexpectedtolead', 'kgmu', 'theinvisiblemanathom', 'belgray', 'sukkar', 'kelvinnatembea', 'thingsmygrandparentstaughtm', 'improperli']\n"
     ]
    }
   ],
   "source": [
    "pos_word_list = list(set(pos_word_list))\n",
    "neg_word_list = list(set(neg_word_list))\n",
    "neu_word_list = list(set(neu_word_list))\n",
    "\n",
    "result_pos = dict(zip(pos_word_list, pos_word_weight))\n",
    "result_neg = dict(zip(neg_word_list, neg_word_weight))\n",
    "result_neu = dict(zip(neu_word_list, neu_word_weight))\n",
    "\n",
    "features_pos = []\n",
    "vals_pos = []\n",
    "\n",
    "features_neg = []\n",
    "vals_neg = []\n",
    "\n",
    "features_neu = []\n",
    "vals_neu = []\n",
    "\n",
    "for key, value in sorted(result_pos.items(), key = itemgetter(1), reverse = True):\n",
    "    features_pos.append(key)\n",
    "    vals_pos.append(value)\n",
    "for key, value in sorted(result_neg.items(), key = itemgetter(1), reverse = True):\n",
    "    features_neg.append(key)\n",
    "    vals_neg.append(value)\n",
    "for key, value in sorted(result_neu.items(), key = itemgetter(1), reverse = True):\n",
    "    features_neu.append(key)\n",
    "    vals_neu.append(value)\n",
    "\n",
    "top_positive_words = features_pos[:10]\n",
    "top_positive_words_freq = vals_pos[:10]\n",
    "\n",
    "top_negative_words = features_neg[:10]\n",
    "top_negative_words_freq = vals_neg[:10]\n",
    "\n",
    "top_neutral_words = features_neu[:10]\n",
    "top_neutral_words_freq = vals_neu[:10]\n",
    "\n",
    "\n",
    "print(top_positive_words)\n",
    "print(top_negative_words)\n",
    "print(top_neutral_words)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# get x and y data \n",
    "points = top_positive_words\n",
    "frequency = top_positive_words_freq\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "plt.title(\"Most Positive Words vs their Compound Score\")\n",
    "plt.xlabel(\"Compound Score\")\n",
    "plt.ylabel(\"Positive Words\")\n",
    "plt.show()"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# get x and y data \n",
    "points = top_negative_words\n",
    "frequency = top_negative_words_freq\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "plt.title(\"Most Negative Words vs their Compound Score\")\n",
    "plt.xlabel(\"Compound Score\")\n",
    "plt.ylabel(\"Negative Words\")\n",
    "plt.show()"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# get x and y data \n",
    "points = top_neutral_words\n",
    "frequency = top_neutral_words_freq\n",
    "#plot graph\n",
    "ax.barh(points,frequency)\n",
    "plt.title(\"Most Neutral Words vs their Compound Score\")\n",
    "plt.xlabel(\"Compound Score\")\n",
    "plt.ylabel(\"Neutral Words\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: WHO Corona-Tweets Summarization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>User</th>\n",
       "      <th>Date</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Binders</th>\n",
       "      <th>Permalink</th>\n",
       "      <th>Retweet count</th>\n",
       "      <th>Likes count</th>\n",
       "      <th>Tweet value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>World Health Organization (WHO)</td>\n",
       "      <td>7/4/2020 20:54:39</td>\n",
       "      <td>During #COVID19, masks should be used as part ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.twitter.com/user/status/1247628881...</td>\n",
       "      <td>52</td>\n",
       "      <td>113</td>\n",
       "      <td>2684.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>World Health Organization (WHO)</td>\n",
       "      <td>7/4/2020 20:54:29</td>\n",
       "      <td>In the community during #COVID19, we recommend...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.twitter.com/user/status/1247628839...</td>\n",
       "      <td>51</td>\n",
       "      <td>118</td>\n",
       "      <td>2684.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>World Health Organization (WHO)</td>\n",
       "      <td>7/4/2020 20:54:20</td>\n",
       "      <td>In health care facilities during #COVID19, WHO...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.twitter.com/user/status/1247628799...</td>\n",
       "      <td>22</td>\n",
       "      <td>44</td>\n",
       "      <td>2684.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>World Health Organization (WHO)</td>\n",
       "      <td>7/4/2020 20:54:08</td>\n",
       "      <td>We encourage countries that are considering th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.twitter.com/user/status/1247628751...</td>\n",
       "      <td>35</td>\n",
       "      <td>86</td>\n",
       "      <td>2440.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>World Health Organization (WHO)</td>\n",
       "      <td>7/4/2020 20:53:55</td>\n",
       "      <td>If masks are worn, they must be used safely an...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.twitter.com/user/status/1247628697...</td>\n",
       "      <td>93</td>\n",
       "      <td>169</td>\n",
       "      <td>2928.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              User               Date  \\\n",
       "0  World Health Organization (WHO)  7/4/2020 20:54:39   \n",
       "1  World Health Organization (WHO)  7/4/2020 20:54:29   \n",
       "2  World Health Organization (WHO)  7/4/2020 20:54:20   \n",
       "3  World Health Organization (WHO)  7/4/2020 20:54:08   \n",
       "4  World Health Organization (WHO)  7/4/2020 20:53:55   \n",
       "\n",
       "                                               Tweet  Binders  \\\n",
       "0  During #COVID19, masks should be used as part ...      NaN   \n",
       "1  In the community during #COVID19, we recommend...      NaN   \n",
       "2  In health care facilities during #COVID19, WHO...      NaN   \n",
       "3  We encourage countries that are considering th...      NaN   \n",
       "4  If masks are worn, they must be used safely an...      NaN   \n",
       "\n",
       "                                           Permalink  Retweet count  \\\n",
       "0  https://www.twitter.com/user/status/1247628881...             52   \n",
       "1  https://www.twitter.com/user/status/1247628839...             51   \n",
       "2  https://www.twitter.com/user/status/1247628799...             22   \n",
       "3  https://www.twitter.com/user/status/1247628751...             35   \n",
       "4  https://www.twitter.com/user/status/1247628697...             93   \n",
       "\n",
       "   Likes count  Tweet value  \n",
       "0          113      2684.28  \n",
       "1          118      2684.28  \n",
       "2           44      2684.28  \n",
       "3           86      2440.25  \n",
       "4          169      2928.30  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "who_dataset = pd.read_csv('Data/WHO__tweet_data.csv')\n",
    "who_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retaining only alphabetnumerics\n",
    "who_dataset[\"Tweet\"] = [re.sub('[^a-zA-Z0-9]', ' ',i) for i in who_dataset[\"Tweet\"]]\n",
    "\n",
    "#Converting into lower case \n",
    "who_dataset[\"Tweet\"] = [i.lower() for i in who_dataset[\"Tweet\"]]\n",
    "\n",
    "#Removing Emoticons\n",
    "def deEmojify(inputString):\n",
    "    return inputString.encode('ascii', 'ignore').decode('ascii')\n",
    "who_dataset[\"Tweet\"]  = [deEmojify(i) for i in who_dataset[\"Tweet\"]]\n",
    "\n",
    "#Removing URLs\n",
    "def removeURLs(str):\n",
    "    ans = \"\"\n",
    "    clean_tweet1 = re.match('(.*?)http.*?\\s?(.*?)', str)\n",
    "    clean_tweet2 = re.match('(.*?)https.*?\\s?(.*?)', str)\n",
    "    if clean_tweet1:\n",
    "        ans=ans+clean_tweet1.group(1)\n",
    "        ans=ans+clean_tweet1.group(2)\n",
    "    elif clean_tweet2: \n",
    "        ans=ans+clean_tweet2.group(1)\n",
    "        ans=ans+clean_tweet2.group(2)\n",
    "    else:\n",
    "        ans = str\n",
    "    return ans\n",
    "who_dataset[\"Tweet\"] = who_dataset[\"Tweet\"].apply(lambda tweet: removeURLs(tweet))\n",
    "\n",
    "\n",
    "sentences = [i for i in who_dataset[\"Tweet\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the word frequency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a word frequency table from every sentence.\n",
    "\n",
    "def create_frequency_table(text_string) -> dict:\n",
    "    \n",
    "    #Remove all the stop words\n",
    "    stopWords = set(stopwords.words(\"english\"))\n",
    "    #Tokenise the sentence into words\n",
    "    words = word_tokenize(text_string)\n",
    "    #Stem the words to get the root word\n",
    "    ps = PorterStemmer()\n",
    "    \n",
    "    freqTable = dict()\n",
    "    for word in words:\n",
    "        word = ps.stem(word)\n",
    "        if word in stopWords:\n",
    "            continue\n",
    "        if word in freqTable:\n",
    "            freqTable[word] += 1\n",
    "        else:\n",
    "            freqTable[word] = 1\n",
    "\n",
    "    return freqTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Term Frequency Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Score a sentence by its words, adding the frequency of every non-stop word in a sentence.\n",
    "#First 10 chars of each sentence is used as key instead of using whole sentence; to prevent memory overload.\n",
    "\n",
    "def score_sentences(sentences, freqTable) -> dict:\n",
    "    \n",
    "    sentenceValue = dict()\n",
    "\n",
    "    for sentence in sentences:\n",
    "        word_count_in_sentence = (len(word_tokenize(sentence)))\n",
    "        for word in freqTable:\n",
    "            if word in sentence:\n",
    "                if sentence[:10] in sentenceValue:\n",
    "                    sentenceValue[sentence[:10]] += freqTable[word]\n",
    "                else:\n",
    "                    sentenceValue[sentence[:10]] = freqTable[word]\n",
    "\n",
    "        #To prevent large sentences from dominating, we divide the score of a sentences by total number of words in it\n",
    "        if word_count_in_sentence > 0:\n",
    "            sentenceValue[sentence[:10]] = sentenceValue[sentence[:10]] // word_count_in_sentence\n",
    "\n",
    "    return sentenceValue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Threshold Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Consider the average score of the sentences as a threshold\n",
    "\n",
    "def find_average_score(sentenceValue) -> int:\n",
    "    sumValues = 0\n",
    "    for entry in sentenceValue:\n",
    "        sumValues += sentenceValue[entry]\n",
    "\n",
    "    # Average value of a sentence from original text\n",
    "    average = int(sumValues / len(sentenceValue))\n",
    "\n",
    "    return average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select a sentence for summarization, only if the sentence score is more than the threshold score\n",
    "\n",
    "def generate_summary(sentences, sentenceValue, threshold):\n",
    "    sentence_count = 0\n",
    "    summary = []\n",
    "\n",
    "    for sentence in sentences:\n",
    "        if sentence[:10] in sentenceValue and sentenceValue[sentence[:10]] > (threshold):\n",
    "            summary.append(sentence)\n",
    "            sentence_count += 1\n",
    "\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Driver Function for WHO corona-tweet summarizarion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1)   more than 3 000 healthworkers have been infected amp many have paid the ultimate price when health workers are exposed or become sick they have to go home amp stay there for at least 2 weeks hospitals scramble to find replacements drtedros thankshealthheroes worldhealthday\n",
      "\n",
      "2)   thankshealthheroes \n",
      "\n",
      "3)   more than 70 countries have joined who s solidarity trial to accelerate the search for an effective treatment and about 20 institutions amp companies are racing to develop a vaccine drtedros covid19 coronavirus\n",
      "\n",
      "4)   we encourage countries that are considering the use of masks for the general population to study their effectiveness so we can all learn most importantly masks should only ever be used as part of a comprehensive package of interventions drtedros covid19 coronavirus\n",
      "\n",
      "5)   in health care facilities who continues to recommend the use of medical masks respirators amp other personal protective equipment for healthworkers drtedros covid19\n",
      "\n",
      "6)   we understand that some countries have recommended or are considering the use of both medical and non medical masks in the general population to prevent the spread of covid19 drtedros\n",
      "\n",
      "7)   many of those healthworkers are now supporting the covid19 response they are tracing contacts finding cases and providing public health information to communities drtedros coronavirus\n",
      "\n",
      "8)   this may be particularly important for refugees internally displaced persons migrants and the homeless drtedros covid19 coronavirus\n",
      "\n",
      "9)   there are 3 main areas for countries to focus on 1 we call on all countries to ensure core public health measures are fully funded including case finding testing contact tracing collecting data and communication and information campaigns drtedros covid19 coronavirus\n",
      "\n",
      "10)   if countries rush to lift restrictions quickly the coronavirus could resurge amp the economic impact could be more severe amp prolonged financing the health response is an essential investment not just in saving lives but in the longer term social amp economic recovery drtedros\n",
      "\n",
      "11)   for some countries debt relief is essential to enable them to take care of their people amp avoid economic collapse this is an area of cooperation between who the imf and the worldbank drtedros covid19 coronavirus\n",
      "\n",
      "12)   in the short term countries can ease the burden on their populations through social welfare programs to ensure people have food amp other life essentials drtedros covid19 coronavirus\n",
      "\n",
      "13)   more than 1 million confirmed cases of covid19 have now been reported to who including more than 50 000 deaths but we know that this is much more than a health crisis we are all aware of the profound social and economic consequences of the pandemic drtedros coronavirus\n",
      "\n",
      "14)   but what doesn t change is who s commitment to protecting the health of all people based on the best science without fear or favour drtedros covid19 coronavirus\n",
      "\n",
      "15)   we recommend handwashing amp physical distancing but we recognize this can be a practical challenge for those who lack access to clean or who live in cramped conditions drtedros covid19 coronavirus\n",
      "\n",
      "16)   we constantly review amp update our guidance as we learn more amp we are working to adapt it for specific contexts drtedros covid19 coronavirus\n",
      "\n",
      "17)   i have called on governments to put in place social welfare measures to ensure vulnerable people have food amp other life essentials during this crisis drtedros covid19 coronavirus\n",
      "\n",
      "18)   many countries are asking people to stay at home and shutting down population movement which can help to limit transmission of the coronavirus but can have unintended consequences for the poorest and most vulnerable people drtedros covid19\n",
      "\n",
      "19)  now available new guidelines to help countries maintain essential health services amp safeguard health care workers during covid19 outbreak \n",
      "\n",
      "20)   countries should work hand in hand with communities to build trust and support resilience and mentalhealth drtedros covid19 coronavirus\n",
      "\n",
      "21)   we understand that many countries are implementing measures that restrict the movement of people in implementing these measures it s vital to respect the dignity and welfare of all people drtedros covid19 coronavirus\n",
      "\n",
      "22)   in addition who is working intensively with several partners to massively increase access to life saving products including diagnostics personal protective equipment medical oxygen ventilators and more drtedros covid19 coronavirus\n",
      "\n",
      "23)   earlier today i spoke to trade ministers from the g20org countries about ways to address the chronic shortage of personal protective equipment and other essential medical supplies drtedros covid19 \n",
      "\n",
      "24)   ensuring free movement of essential health products is vital for saving lives amp curbing the social amp economic impacts of the covid19 pandemic drtedros coronavirus\n",
      "\n",
      "25)   previous outbreaks have demonstrated that when health systems are overwhelmed deaths due to vaccine preventable and treatable conditions increase dramatically drtedros covid19 coronavirus\n",
      "\n",
      "26)   i am very grateful to those working in various national organizations providing critical cybersecurity intelligence to the who cybersecurity team drtedros covid19 coronavirus\n",
      "\n",
      "27)   more than 1 million health workers have been trained through our courses on \n",
      "\n",
      "28)   in the meantime we call on individuals amp countries to refrain from using therapeutics that have not been demonstrated to be effective in the treatment of covid19 drtedros coronavirus\n",
      "\n",
      "29)   a vaccine is still at least 18 months away in the meantime we recognize that there is an urgent need for therapeutics to treat patients amp save lives drtedros covid19 coronavirus\n",
      "\n",
      "30)   when healthworkers are at risk we re all at risk health workers in low amp middle income countries deserve the same protection as those in the wealthiest countries drtedros covid19 coronavirus\n",
      "\n",
      "31)   countries also expressed several common challenges the chronic global shortage of personal protective equipment is now one of the most urgent threats to our collective ability to save lives drtedros covid19 coronavirus\n",
      "\n",
      "32)   earlier today we held a briefing with around 50 ministers of health from around the world at which amp shared their experiences and the lessons they have learned drtedros covid19 coronavirus\n",
      "\n",
      "33)   this is especially important for countries who are not part of the g20org but will be affected by decisions made by g20 countries drtedros covid19 \n",
      "\n",
      "34)   there are now more than 1 2 a million confirmed cases of covid19 amp more than 20 000 deaths these are tragic numbers but let s also remember that around the there are hundreds of thousands of survivors drtedros coronavirus\n",
      "\n",
      "35)  the full address by drtedros at the g20org summit on covid19 \n",
      "\n",
      "36)   drtedros welcomed g20org commitment to do whatever it takes to overcome the covid19 pandemic protect lives amp livelihoods restore confidence amp shore up stability currently threatened in trade amp other sectors take all necessary health measures ensure adequate financing\n",
      "\n",
      "37)   drtedros calls on g20org to fight unite and ignite against covid19 \n",
      "\n",
      "38)   this is especially relevant for many vulnerable countries whose health systems may collapse under the weight of the numbers of patients we ve seen in some countries with community transmission drtedros covid19 coronavirus\n",
      "\n",
      "39)   more than 150 countries amp territories still have fewer than 100 cases by taking the same aggressive actions now these countries have the chance to prevent community transmission amp avoid some of the more severe social amp economic costs seen in other countries drtedros covid19\n",
      "\n",
      "40)   we understand that these countries are now trying to assess when and how they will be able to ease these measures the answer depends on what countries do while these population wide measures are in place drtedros covid19 coronavirus\n",
      "\n",
      "41)   3 prioritize treatment for those at highest risk of severe illness drtedros covid19 coronavirus\n",
      "\n",
      "42)   2 ramp up surveillance and lab testing so that those with the virus can be identified quickly and isolated safely helping to break the chains of transmission drtedros covid19 coronavirus\n",
      "\n",
      "43)   the new global humanitarian response plan builds on that effort and sets a six point action plan for how to prepare and respond to this covid19 emergency drtedros coronavirus\n",
      "\n",
      "44)   we ve worked with business leaders to ensure supply chains are working and bottlenecks are overcome drtedros covid19 coronavirus\n",
      "\n",
      "45)   i implore leaders to stand together and heed this appeal since the covid19 outbreak was identified who and our partners have been ramping up surveillance and lab testing across low and middle income countries drtedros coronavirus\n",
      "\n",
      "46)   people and communities that are already uprooted due to conflict displacement the climate crisis or other disease outbreaks are the ones we must urgently prioritize drtedros covid19 coronavirus\n",
      "\n",
      "47)  live now launch of the covid19 global humanitarian response plan with drtedros coronavirus \n",
      "\n",
      "48)   even if we do everything else right if we don t prioritize protecting healthworkers many people will die because the health worker who could have saved their life is sick drtedros covid19 coronavirus\n",
      "\n",
      "49)   more than 300 000 cases of covid19 have now been reported to who from almost every country in the the pandemic is accelerating it took 67 days from the 1st reported case to reach the first 100k cases 11 days for the second 100k amp just 4 days for the third 100k drtedros \n",
      "\n",
      "50)   i d also like to take this opportunity to thank fifacom for its generous contribution of 10 million u s dollars to the covid19 solidarity response fund drtedros coronavirus \n",
      "\n",
      "51)  watch our goodwill ambassador alissonbecker taking the safehands challenge and promoting the global covid19 response together for a safer healthier world coronavirus \n",
      "\n",
      "52)   4 exercise who recommends 30 minutes of physical activity a day for adults and one hour a day for children drtedros covid19 coronavirus beactive\n",
      "\n",
      "53)   in addition to increasing access to masks gloves gowns and tests we re also increasing access to the evidence based technical guidance countries and health workers need to save lives drtedros covid19 coronavirus\n",
      "\n",
      "54)   we understand the heavy financial toll this covid19 pandemic is taking on businesses and the global economy drtedros coronavirus\n",
      "\n",
      "55)   who is working actively to support all countries and especially those that need our support the most drtedros covid19 coronavirus\n",
      "\n",
      "56)   data from many countries clearly show that people under 50 make up a significant proportion of patients requiring hospitalization drtedros covid19 coronavirus\n",
      "\n",
      "57)   many countries have already confirmed that they will join the solidarity trial argentina bahrain canada france iran norway south africa spain switzerland and thailand and i trust many more will join drtedros covid19 coronavirus\n",
      "\n",
      "58)   the solidarity trial provides simplified procedures to enable even hospitals that have been overloaded to participate drtedros covid19 coronavirus\n",
      "\n",
      "59)   if that s not possible countries can use community facilities to isolate amp care for mild covid19 cases amp refer them for specialized care quickly if needed drtedros coronavirus\n",
      "\n",
      "60)   who is working in solidarity with other countries with covid19 community transmission to apply the lessons learned in and elsewhere and adapt them to the local context drtedros coronavirus\n",
      "\n",
      "61)   at the peak in there were more than 800 covid19 cases and yesterday there were only 90 cases drtedros coronavirus\n",
      "\n",
      "62)   many countries are listening to our call amp finding solutions to increase their ability to implement the full package of measures that have turned the tide in several countries drtedros covid19 coronavirus\n",
      "\n",
      "63)   we understand that different countries and communities are in different situations with different levels of covid19 transmission drtedros coronavirus\n",
      "\n",
      "64)   we re all in this together and we can only succeed together drtedros covid19 coronavirus\n",
      "\n",
      "65)   mentalhealth and psychosocial considerations during covid19 outbreak \n",
      "\n",
      "66)  stay safe and be respectful to each other covid19 coronavirus \n",
      "\n",
      "67)   there are simple effective things we can all do to reduce the risk of infection for ourselves amp those around us clean your hands regularly with an alcohol based rub or soap and water drtedros covid19 coronavirus \n",
      "\n",
      "68)   even if you cannot stop covid19 transmission you can slow it down and save lives drtedros coronavirus\n",
      "\n",
      "69)   who has evidence based guidance that every country can use according to each of the 8 pillars drtedros covid19 coronavirus\n",
      "\n",
      "70)   more than 132 000 cases of covid19 have now been reported to who from 123 countries and territories 5 000 people have lost their lives a tragic milestone drtedros coronavirus\n",
      "\n",
      "71)   all countries must strike a fine balance between protecting health minimizing economic amp social disruption amp respecting human rights drtedros covid19 coronavirus\n",
      "\n",
      "72)   even those countries with community transmission or large clusters can turn the tide on this coronavirus several countries have demonstrated that this virus can be suppressed and controlled drtedros covid19\n",
      "\n",
      "73)   if countries detect test treat isolate trace and mobilize their people in the response those with a handful of covid19 cases can prevent those cases becoming clusters and those clusters becoming community transmission drtedros coronavirus\n",
      "\n",
      "74)   as i said on monday just looking at the number of covid19 cases and the number of countries affected does not tell the full story drtedros coronavirus\n",
      "\n",
      "75)   we have called every day for countries to take urgent and aggressive action we have rung the alarm bell loud and clear drtedros covid19 coronavirus\n",
      "\n",
      "76)   breaking we have therefore made the assessment that covid19 can be characterized as a pandemic drtedros coronavirus \n",
      "\n",
      "77)   the great advantage we have is that the decisions we all make as governments businesses communities families amp individuals can influence the trajectory of this covid19 epidemic drtedros coronavirus \n",
      "\n",
      "78)   there are many vulnerable people in our communities elderly people with underlying conditions people with cancer on chemotherapy amp other precious members of our society it s a duty of us all to do as much as we can to protect those people among us drmikeryan covid19 \n",
      "\n",
      "79)   as long as that s the case those countries have the opportunity to break the chains of covid19 transmission prevent community transmission and reduce the burden on their health systems drtedros coronavirus\n",
      "\n",
      "80)   more funds are being made available for the covid19 response and we re very grateful to all countries amp partners who have contributed just since friday amp have announced contributions drtedros coronavirus\n",
      "\n",
      "81)   nigeria senegal and ethiopia have strengthened covid19 surveillance and diagnostic capacity to find cases quickly drtedros coronavirus \n",
      "\n",
      "82)   is a good example of an all of government approach prime minister leehsienloong s regular videos are helping to explain the coronavirus risks amp reassure people drtedros covid19 \n",
      "\n",
      "83)   there are many examples of countries demonstrating that these measures work amp many others have activated covid19 emergency measures drtedros coronavirus \n",
      "\n",
      "84)   the fundamental elements of the covid19 response are the same for all countries 5 laboratory testing 6 treating patients amp hospital readiness 7 infection prevention amp control 8 all of society amp all of government approach drtedros coronavirus\n",
      "\n",
      "85)   the fundamental elements of the covid19 response are the same for all countries 1 emergency response mechanisms 2 risk comms amp public engagement 3 case finding amp contact tracing 4 public health measures drtedros coronavirus\n",
      "\n",
      "86)   depending on their context countries with covid19 community transmission could consider closing schools cancelling mass gatherings and other measures to reduce exposure drtedros coronavirus\n",
      "\n",
      "87)   for the first three categories countries must focus on finding testing treating and isolating individual covid19 cases and following their contacts drtedros coronavirus\n",
      "\n",
      "88)   countries that continue finding and testing covid19 cases and tracing their contacts not only protect their own people they can also affect what happens in other countries and globally drtedros coronavirus\n",
      "\n",
      "89)   all countries must take a comprehensive blended strategy for controlling their covid19 epidemics amp pushing this coronavirus back drtedros\n",
      "\n",
      "90)   the great advantage we have is that the decisions we all make as governments businesses communities families amp individuals can influence the trajectory of this covid19 epidemic drtedros coronavirus\n",
      "\n",
      "91)  how to manage covid19 risk when organizing meetings amp events \n",
      "\n",
      "92)  q how are covid19 and influenza viruses different who situation report 6 march 2020 \n",
      "\n",
      "93)  q how are covid19 amp influenza viruses similar more who situation report 6 march 2020 \n",
      "\n",
      "94)   who is working with the wef to engage companies around the world and earlier this week i spoke to more than 200 ceos about how they can protect their staff and customers ensure business continuity and contribute to the response drtedros covid19 coronavirus\n",
      "\n",
      "95)   all of this requires the involvement of the private sector to ensure countries can access life saving products drtedros covid19 coronavirus\n",
      "\n",
      "96)   we encourage every country to review who s disease commodity package for covid19 to ensure it has the supplies it needs incl protective equipment amp medical devices drtedros coronavirus\n",
      "\n",
      "97)   who is working closely with industry associations regulators amp other partners to monitor this risk and so far we have not identified any imminent specific shortages drtedros covid19 coronavirus\n",
      "\n",
      "98)   is a major producer of active pharmaceutical ingredients amp the intermediate products that are used to produce in other countries who has focused on the most essential that are critical for primaryhealthcare amp emergencies drtedros covid19 coronavirus\n",
      "\n",
      "99)   so far who has received applications for review amp approval of 40 diagnostic tests 20 vaccines are in development amp many clinical trials of therapeutics are underway drtedros covid19 coronavirus\n",
      "\n",
      "100)  live daily media briefing on covid19 with drtedros coronavirus \n",
      "\n",
      "101)   more funding is being made available to support countries that need it amp that have plans in place drtedros covid19 coronavirus\n",
      "\n",
      "102)   train your healthworkers to identify covid19 cases provide careful amp compassionate treatment amp protect themselves from infection if countries act aggressively to find isolate treat cases amp trace every contact they can change the trajectory of this epidemic drtedros\n",
      "\n",
      "103)   we see encouraging signs from the number of newly reported covid19 cases appears to be declining amp the cases that are being reported are being identified primarily from known clusters drtedros coronavirus\n",
      "\n",
      "104)   outside 2055 covid19 cases were reported in 33 countries around 80 of those cases continue to come from just three countries drtedros coronavirus\n",
      "\n",
      "105)  who has shipped covid19 laboratory testing equipment to 93 countries who situation report \n",
      "\n",
      "106)  live askwho q amp a on covid19 in the workplace \n",
      "\n",
      "107)  daily media briefing on covid19 with drtedros coronavirus \n",
      "\n",
      "108)  launch of the who electronic essentialmedicines list with drtedros \n",
      "\n",
      "109)  live askwho on covid19 with mvankerkhove coronavirus \n",
      "\n",
      "110)  live from geneva with dr bruce aylward lead of the covid19 international experts mission in china \n",
      "\n",
      "111)  live from geneva with the un secretary general antonioguterres and drtedros on covid19 \n",
      "\n",
      "112)  the full briefing on covid19 by drtedros \n",
      "\n",
      "113)  who covid19 situation report 22 february 2020 \n",
      "\n",
      "114)  live now drtedros briefing africanunion on covid19 \n",
      "\n",
      "115)   pahowho who europe whoemro whoafro whosearo whowpro drtedros seat belts helmets child seats low speed lower alcohol limits for young drivers sidewalks and bike lanes they all reduce the risk of deaths on the road roadsafety \n",
      "\n",
      "116)   pahowho who europe whoemro whoafro whosearo whowpro drtedros more people die every year as a result of road traffic injury than from hiv aids tuberculosis and diarrhoeal diseases but the political commitment amp investments in roadsafety is only a small fraction of that made to combat these diseases \n",
      "\n",
      "117)   pahowho who europe whoemro whoafro whosearo whowpro drtedros dyk with an impact speed of 80 km h the likelihood of death in a crash is 20 times what it would be at an impact speed of 30 km h 1 slow down 2 know the traffic rules 3 always keep to the speed limits roadsafety \n",
      "\n",
      "118)   pahowho who europe whoemro whoafro whosearo whowpro drtedros buckle up wearing a seat belt reduces the risk of death among drivers and front seat occupants by 45 50 and the risk of death and serious injuries among rear seat occupants by 25 roadsafety \n",
      "\n",
      "119)   pahowho who europe whoemro whoafro whosearo whowpro drtedros drinking and driving is one of the main causes of road crashes never drink and drive roadsafety \n",
      "\n",
      "120)   pahowho who europe whoemro whoafro whosearo whowpro drtedros progress on roadsafety can also be made by improving vehicle standards such as those that mandate electronic stability control and advanced braking and enhanced post crash care \n",
      "\n",
      "121)  a future for the world s children report launch with drtedros \n",
      "\n",
      "122)   pahowho who europe whoemro whoafro whosearo whowpro road traffic crashes are not accidents when someone drives and it s not an accident when a driver is distracted by their and it s not an accident when a dies because they re not wearing a helmet it s not an accident drtedros roadsafety \n",
      "\n",
      "123)  q amp a on a future for the world s children with helenclarknz \n",
      "\n",
      "124)   pahowho who europe whoemro whoafro whosearo whowpro progress on roadsafety can be made with better legislation around key risks such as speeding drinking and driving failing to use seat belts motorcycle helmets and child restraints \n",
      "\n",
      "125)   pahowho who europe whoemro whoafro whosearo whowpro lack of pedestrian crossings sidewalks and separate bike lanes as well as excessive speed increase the risk for injuries and deaths it is time to make walking and cycling safe roadsafety \n",
      "\n",
      "126)  live on periscope daily media briefing on covid19 with drtedros \n",
      "\n",
      "127)   pahowho who europe whoemro whoafro whosearo whowpro roadsafety is an urgent health and development matter the number of deaths on the s roads remains unacceptably high with an estimated 1 35 million people killed each year \n",
      "\n",
      "128)   pahowho i love the fact that i m the first person that the person comes in contact with when they come into the emergency department amp the fact that whatever care i provide for the patient will be a lasting one trecia simone stewart \n",
      "\n",
      "129)  live with drtedros on health security from the munsecconf msc2020 \n",
      "\n",
      "130)  daily media briefing on covid19 with drtedros coronavirus \n",
      "\n",
      "131)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos avoid using condoms that are expired see manufacturing or expiry date on the package do not use a latex condom more than 5 years old it s expired and may break during use worldcondomday internationalcondomday unaids \n",
      "\n",
      "132)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos male and female condoms cannot get lodged inside the uterus nor do they have any harmful effects on a foetus if the woman is pregnant the opening to the cervix is far too small to allow a condom to pass through worldcondomday internationalcondomday \n",
      "\n",
      "133)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos sexual stimulation can be enhanced when a man allows their partner to put the condom on him prior to sexual intercourse worldcondomday internationalcondomday \n",
      "\n",
      "134)  daily press conference on covid19 with drmikeryan coronavirus \n",
      "\n",
      "135)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos non lubricated condoms increase risk of hiv stis because they are more likely to tear and they can cause lesions on the vaginal wall worldcondomday internationalcondomday \n",
      "\n",
      "136)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos who recommends that condoms with nonoxynol 9 n 9 spermicidal treatment no longer be promoted due to adverse side effects n 9 does not provide extra protection against hiv sti instead it actually increases the risk of hiv infection when used frequently by high risk women \n",
      "\n",
      "137)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos avoid oil based lubricants such as petroleum jelly hand lotion baby oil or edible oils these weaken latex condoms and make them more prone to breakage therefore making them less effective worldcondomday internationalcondomday epaphotos \n",
      "\n",
      "138)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos condom effectiveness depends on user behaviour in opening the package putting on the condom type of sexual activity lubrication and number of times an individual condom is used condoms are designed for single use only worldcondomday \n",
      "\n",
      "139)   pahowho who europe whoemro whoafro whosearo whowpro unicef epaphotos a very small number of men and women are allergic to latex or to chemicals added to latex these people should not use latex condoms they should be counselled to use synthetic condoms such as polyurethane condoms worldcondomday \n",
      "\n",
      "140)   pahowho who europe whoemro whoafro whosearo whowpro unicef condoms can be made of latex polyurethane synthetic material or animal tissue new male and female condoms are being developed by research teams around the world worldcondomday epaphotos \n",
      "\n",
      "141)   pahowho who europe whoemro whoafro whosearo whowpro unicef the female condom is about the same length as the male condom but is wider for a looser fit allowing freedom in movement that some say provides a more natural feeling lubricant is used on the inside and the outside of female condoms worldcondomday \n",
      "\n",
      "142)   pahowho who europe whoemro whoafro whosearo whowpro dyk condoms are available for men and women male condoms are sheaths or coverings that fit over a man s erect penis female condoms are sheaths or linings that fit loosely inside a woman s vagina made of thin transparent soft plastic film worldcondomday unicef \n",
      "\n",
      "143)  world experts and funders set priorities for covid19 research \n",
      "\n",
      "144)  press conference on ebola in drc amp covid19 with drtedros \n",
      "\n",
      "145)   pahowho who europe whoemro whoafro whosearo whowpro on womeninscience day it s a moment to recall that principles of human rights and social equity require that women play just as significant roles in science and health as men \n",
      "\n",
      "146)   pahowho who europe whoemro whoafro whosearo whowpro on womeninscience day let s remember fran oise barr sinoussi s work on hiv which was fundamental to the identification of the virus as the cause of aids \n",
      "\n",
      "147)   m scribe devisridhar coronavirus disease 2019 covid19\n",
      "\n",
      "148)   pahowho who europe whoemro whoafro whosearo whowpro on womeninscience day let s remember anne szarewski who discovered the cause of cervicalcancer leading to the first ever hpv vaccine \n",
      "\n",
      "149)   pahowho who europe whoemro whoafro whosearo whowpro on womeninscience day let s remember elizabeth garrett anderson who taught herself french so that she could obtain a medical degree at the university of sorbonne in paris she became s first female doctor \n",
      "\n",
      "150)   pahowho who europe whoemro whoafro whosearo whowpro on womeninscience day let s remember anandi gopal joshi one of the first indian female doctors she was appointed physician in charge at a hospital in central india before she died of tuberculosis aged just 22 \n",
      "\n",
      "151)   pahowho who europe whoemro whoafro whosearo whowpro on womeninscience day let s remember fe del mundo a paediatrician from the philippines who did pioneering work on infectious diseases including dengue she was the first female student at harvard medical school \n",
      "\n",
      "152)   pahowho who europe whoemro whoafro whosearo whowpro on womeninscience day let s remember florence nightingale a 19th century statistician and founder of modern nursing who understood the benefits of hygiene and sanitation in preventing disease \n",
      "\n",
      "153)   pahowho who europe whoemro whoafro whosearo whowpro three quarters of people with epilepsy living in low income countries do not get the treatment they need epilepsyday \n",
      "\n",
      "154)   pahowho who europe whoemro whoafro whosearo whowpro with only 10 years to achieve globalgoalsun target 3 4 on noncommunicable diseases governments must accelerate their response to the epidemic and consider implementing the most effective and feasible interventions first \n",
      "\n",
      "155)   pahowho who europe whoemro whoafro whosearo whowpro the risk of dying from the four major noncommunicable diseases between the ages of 30 and 70 years declined from 22 in 2000 to 18 in 2016 \n",
      "\n",
      "156)   pahowho who europe whoemro whoafro whosearo whowpro the 10 ncd indicators include setting time bound targets to ncd deaths developing all of government policies to address ncds implementing tobacco demand reduction measures reducing harmful use of alcohol promoting physical activity strengthening health systems \n",
      "\n",
      "157)   pahowho who europe whoemro whoafro whosearo whowpro epilepsy is one of the world s oldest recognized conditions with written records dating back to 4000 bc fear misunderstanding discrimination and social stigma have surrounded epilepsy for centuries \n",
      "\n",
      "158)   pahowho who europe whoemro whoafro whosearo whowpro epilepsy is not contagious \n",
      "\n",
      "159)   pahowho who europe whoemro whoafro whosearo whowpro people with seizures tend to have more physical problems such as fractures and bruising from injuries related to seizures and higher rates of psychological conditions including anxiety and depression \n",
      "\n",
      "160)  q amp a on advice for travellers during 2019 ncov with carmendolea askwho \n",
      "\n",
      "161)   pahowho who europe whoemro whoafro whosearo whowpro 70 of people living with epilepsy could live seizure free if properly diagnosed and treated \n",
      "\n",
      "162)   pahowho who europe whoemro whoafro whosearo whowpro epilepsy is a disease of the brain that affects people of all ages it is characterized by recurrent seizures which are brief episodes of involuntary movement that may involve a part of the body or the entire body \n",
      "\n",
      "163)   yvette80588826 roellchen2011 drtedros \n",
      "\n",
      "164)  closing of the eb146 with drtedros healthforall \n",
      "\n",
      "165)   pahowho who europe whoemro whoafro whosearo whowpro addressing road traffic deaths and injuries reduces human suffering amp economic losses and positively impacts all aspects of society and development including those linked to environment education employment energy poverty human rights amp equality \n",
      "\n",
      "166)   pahowho who europe whoemro whoafro whosearo whowpro improvements have occurred when a number of sectors have been involved including transport health urban planning amp law enforcement success has also depended on strong leadership and political will at the highest level of government amp civil society and the private sector \n",
      "\n",
      "167)   pahowho who europe whoemro whoafro whosearo whowpro these countries also improved infrastructure through measures such as safer sidewalks amp dedicated lanes for cyclists implemented vehicle standards such as those that mandate advanced braking amp electronic stability control enhanced post crash care \n",
      "\n",
      "168)   pahowho who europe whoemro whoafro whosearo whowpro many countries have already made progress through effective roadsafety management and focusing on better legislation and enforcement around key risks such as speeding drinking and driving failing to use seatbelts \n",
      "\n",
      "169)   pahowho who europe whoemro whoafro whosearo whowpro over half of all road traffic deaths occur among pedestrians cyclists motorcyclists roadsafety \n",
      "\n",
      "170)   pahowho who europe whoemro whoafro whosearo whowpro road traffic injuries are the leading cause of death for children and young adults aged 5 to 29 years according to who s most recent global status report on roadsafety \n",
      "\n",
      "171)  watch live drtedros briefing eb146 on 2019ncov \n",
      "\n",
      "172)   pahowho who europe whoemro whoafro whosearo whowpro untreated depression can prevent people from working and participating in family and community life letstalk timetotalk \n",
      "\n",
      "173)  live from geneva with sanianishtar co chair of the beatncds commission \n",
      "\n",
      "174)  live from geneva with drtedros on the commission s final report on how to beatncds \n",
      "\n",
      "175)  watch live technical briefing on 2019ncov with drtedros \n",
      "\n",
      "176)  watch live drtedros speaking on electronic nicotine delivery systems eb146 \n",
      "\n",
      "177)  live from geneva on 2019ncov with scbriand \n",
      "\n",
      "178)   pahowho who europe whowpro whosearo whoemro whoafro drtedros un news centre moetitshidi hans kluge thank you for your commitment to serving humanity thank you for making this organization great drtedros thanked dr zsuzsanna jakab for her service as the regional director who europe who is now who deputy director general eb146 \n",
      "\n",
      "179)   pahowho who europe whowpro whosearo whoemro whoafro drtedros un news centre moetitshidi hans kluge dear hans kluge welcome to your new position in who it is well deserved you can count on my unwavering support to take your vision forward together we will deliver results to the people we serve drtedros eb146 \n",
      "\n",
      "180)   pahowho who europe whowpro whosearo whoemro whoafro drtedros un news centre moetitshidi the who executive board appointed hans kluge as who regional director for who europe eb146 \n",
      "\n",
      "181)   pahowho who europe whowpro whosearo whoemro whoafro drtedros un news centre moetitshidi my dear sister i greatly value your leadership partnership and friendship you have played a key role in who s global policy group as we work together to build the who of the future you have the full support of the whoafro region and you have mine too drtedros eb146 \n",
      "\n",
      "182)   pahowho who europe whowpro whosearo whoemro whoafro drtedros un news centre the who executive board reappointed dr moetitshidi as who regional director for whoafro eb146 \n",
      "\n",
      "183)  live opening of the eb146 with drtedros \n",
      "\n",
      "184)   pahowho who europe whoemro whoafro whowpro whosearo who is proposing a new strategy to eliminate cervicalcancer as a public health problem 1st steps increase hpv vaccination to 90 ensure 70 of are screened twice in their lives around the ages of 35 amp 45 get appropriate treatment to 90 of diagnosed by 2030 \n",
      "\n",
      "185)  live from geneva on the new coronavirus outbreak \n",
      "\n",
      "186)   breaking \n",
      "\n",
      "187)  live q amp a on novel coronavirus 2019 ncov \n",
      "\n",
      "188)   pahowho who europe whoemro whoafro whosearo whowpro stopping discrimination and promoting inclusion must be core components of any leprosy strategy \n",
      "\n",
      "189)   pahowho who europe whoemro whoafro whosearo whowpro untreated leprosy can cause progressive and permanent damage to the skin nerves limbs and eyes \n",
      "\n",
      "190)   pahowho who europe whoemro whoafro whosearo whowpro leprosy is curable with multidrug therapy treatment in the early stages can prevent disability \n",
      "\n",
      "191)  the full statement by drtedros on ihr emergency committee on new coronavirus \n",
      "\n",
      "192)  live from wef20 on climateaction with drmarianeira on behalf of drtedros \n",
      "\n",
      "193)   pahowho who europe whoemro whoafro whowpro whosearo we re still accepting great videos for our healthforall film festival for those who are still finalizing their videos please submit yours before the end of january 2020 \n",
      "\n",
      "194)  live from wef20 with drtedros amp deepikapadukone on mentalhealth letstalk \n",
      "\n",
      "195)  live from wef20 with drtedros on economics of global health healthforall \n",
      "\n",
      "196)   pahowho who europe whoemro whoafro whowpro whosearo 2 0 2 0 is the international year of the nurse and midwife if you re a film maker we want to see your films highlighting the crucial role of nurses amp midwives in saving lives apply now amp take part of the healthforall film festival \n",
      "\n",
      "197)   pahowho who europe whoemro whoafro whowpro whosearo worldwide cervicalcancer is the fourth most frequent cancer in women of the estimated more than 311 000 deaths from cervical cancer every year nearly 90 of these occur in less developed regions \n",
      "\n",
      "198)   pahowho who europe whoemro whosearo whoafro whowpro new treatments alone will not be sufficient to combat the threat of antimicrobialresistance who works with countries and partners to improve infection prevention and control and to foster appropriate use of existing and future antibiotics antibioticresistance \n",
      "\n",
      "199)   pahowho who europe whoemro whosearo whoafro whowpro new delhi metallo beta lactamase ndm 1 makes bacteria resistant to a broad range of antibiotics including those from the carbapenem family currently the last line of defence against antibiotic resistant bacterial infections antibioticresistance \n",
      "\n",
      "200)   pahowho who europe whoemro whosearo whoafro whowpro worrying gap only 3 antibiotics in the pipeline against the highly resistant new delhi metallo beta lactamase ndm 1 antibioticresistance \n",
      "\n",
      "201)   pahowho who europe whoemro whosearo whoafro whowpro of the 50 antibiotics in the pipeline 32 target who priority pathogens the families of antibiotic resistant bacteria that pose the greatest threat to human health but most have only limited benefits when compared to existing antibiotics \n",
      "\n",
      "202)   pahowho who europe whoemro whosearo whoafro whowpro research and development for antibiotics is primarily driven by small or medium sized enterprises with large pharmaceutical companies playing a diminishing role who new reports antibioticresistance \n",
      "\n",
      "203)   pahowho who europe whoemro whoafro whowpro whosearo two weeks left to apply for who s first ever healthforall film festival your film can make a difference influence health and catalyze change find out more on how to apply \n",
      "\n",
      "204)   pahowho who europe whoemro whoafro whowpro whosearo don t underestimate what you have even through the lens of your mobile you can create impactful stories and promote constructive dialogue around global health challenges and solutions join who s healthforall film festival \n",
      "\n",
      "205)   pahowho who europe whoemro whoafro whowpro whosearo symptoms of early stage cervicalcancer may include bleeding during sexual intercourse postmenopausal bleeding bleeding between periods foul smelling discharge cervicalhealthmonth \n",
      "\n",
      "206)   pahowho who europe whoemro whoafro whowpro whosearo no woman should die from cervicalcancer we have the technical medical and policy tools and approaches to eliminate it the burden of cervical cancer falls on the women who lack access to health services cervicalhealthmonth \n",
      "\n",
      "207)   pahowho who europe whoemro whoafro whowpro whosearo you can raise awareness improve understanding and encourage action on health engage in who s healthforall film festival and submit your short video \n",
      "\n",
      "208)   pahowho who europe whoemro whoafro whowpro whosearo when diagnosed cervicalcancer is one of the most successfully treatable forms of cancer as long as it is detected early and managed effectively cancers diagnosed in late stages can also be controlled with appropriate treatment and palliative care cervicalhealthmonth \n",
      "\n",
      "209)   pahowho who europe whoemro whoafro whowpro whosearo all adult women should undergo periodic cervicalcancer screening screening aims to detect abnormalities in the cells of the cervix which if left untreated can develop into cervical cancer \n",
      "\n",
      "210)   pahowho who europe whoemro whoafro whowpro whosearo you can film stories about health and well being any way you like including through the lens of your smartphone the healthforall film festival is to share your work submit your work now \n",
      "\n",
      "211)  q amp a on coronaviruses \n",
      "\n",
      "212)   pahowho who europe whoemro whoafro whowpro whosearo palliativecare services are essential to cervicalcancer control \n",
      "\n",
      "213)   pahowho who europe whoemro whoafro whowpro whosearo cervicalcancer control needs good data \n",
      "\n",
      "214)   pahowho who europe whoemro whoafro whowpro whosearo nearly 90 of women who die from cervicalcancer have poor access to prevention screening and treatment we need to take a stand against inequality \n",
      "\n",
      "215)   pahowho who europe whoemro whoafro whowpro whosearo cervicalcancer can be cured using health services to check for cancer is key early diagnosis and prompt treatment has a higher potential for cure \n",
      "\n",
      "216)   pahowho who europe whoemro whoafro whowpro whosearo vaccination of young adolescents against hpv is safe and prevents cervicalcancer human papillomavirus hpv is the most common sexually transmitted infection and causes cervical cancer \n",
      "\n",
      "217)   pahowho whowpro whoafro whosearo who europe whoemro talindab ladygaga drtedros goldenglobes myth people who talk about suicide do not take their own lives fact people who talk about suicide may be reaching out for help or support \n",
      "\n",
      "218)   pahowho whowpro whoafro whosearo who europe whoemro talindab ladygaga drtedros goldenglobes myth someone who is suicidal is determined to die fact on the contrary suicidal people are often ambivalent about living or dying access to emotional support at the right time can prevent suicide \n",
      "\n",
      "219)   pahowho whowpro whoafro whosearo who europe whoemro talindab ladygaga drtedros goldenglobes myth talking about suicide with someone who is suicidal is a bad idea and can be interpreted as encouragement fact talking openly can give a person other options or the time to rethink his her decision thereby preventing suicide \n",
      "\n",
      "220)   pahowho whowpro whoafro whosearo who europe whoemro talindab ladygaga drtedros goldenglobes filmmakers when portraying suicide please provide parental guidance for content aimed at viewers under 18 years of age \n",
      "\n",
      "221)   pahowho who europe whoemro whoafro whowpro whosearo cervicalcancer can be prevented cervicalcancer can be prevented cervicalcancer can be prevented cervicalcancer can be prevented cervicalcancer can be prevented cervicalcancer can be prevented \n",
      "\n",
      "222)   pahowho whowpro whoafro whosearo who europe whoemro talindab ladygaga drtedros goldenglobes filmmakers when portraying suicide please consider including a content advisory message prior to the beginning of cinematic televised streamed or theatrical content the impact of portraying suicide on persons involved in stage amp screen productions \n",
      "\n",
      "223)   pahowho whoafro who europe whoemro whowpro whosearo goldenglobes ladygaga talindab drtedros suicide is a serious global public health problem that occurs throughout the lifespan measures that can be taken to prevent suicide include reducing access to means and responsible reporting of suicide by journalists \n",
      "\n",
      "224)   pahowho who europe whoafro whosearo whoemro whowpro drtedros un un news centre icnurses nursingnow2020 investing in women and the education of girls to enter formal paid work will increase gender equality and women s empowerment as women gain income education and autonomy \n",
      "\n",
      "225)   pahowho who europe whoafro whosearo whoemro whowpro drtedros un un news centre icnurses nursingnow2020 strengthening nursing and midwifery and ensuring that they are enabled to work to their full potential is one of the most important things we can do to achieve healthforall and improve health globally \n",
      "\n",
      "226)   pahowho who europe whoafro whosearo whoemro whowpro drtedros un un news centre icnurses we all should aim to ensure that nurses and midwives operate in an environment where they are safe from harm respected and have access to a functioning health care service \n",
      "\n",
      "227)   pahowho who europe whoafro whosearo whoemro whowpro drtedros un un news centre nurses and midwives are often the first and sometimes the only health professionals that people see that s why the quality of their initial assessment care and treatment is vital \n",
      "\n",
      "228)   pahowho who europe whoafro whosearo whoemro whowpro drtedros un un news centre educating nurses and midwives to international standards makes economic sense it saves resources by reducing the need for costly and unnecessary interventions and increases quality of care and healthforall \n",
      "\n",
      "229)   pahowho who europe whoafro whosearo whoemro whowpro drtedros un un news centre the world is also facing a shortfall of 18 million health workers needed to deliver and sustain healthforall by 2030 over half of that shortfall is nurses and midwives \n",
      "\n",
      "230)   pahowho who europe whoafro whosearo whoemro whowpro drtedros nurses and midwives play a critical role in health promotion disease prevention and delivering primary and community care \n",
      "\n",
      "231)  healthy resolution no 11 support breastfeeding happynewyear2020 \n",
      "\n",
      "232)  healthy resolution no 10 primaryhealthcare happynewyear2020 \n",
      "\n",
      "233)  healthy resolution no 9 knowyourstatus happynewyear2020 \n",
      "\n",
      "234)  healthy resolution no 8 roadsafety happynewyear2020 \n",
      "\n",
      "235)  healthy resolution no 7 handhygiene happynewyear2020 \n",
      "\n",
      "236)  healthy resolution no 6 manage stress mentalhealth happynewyear2020 \n",
      "\n",
      "237)  healthy resolution no 5 limit use of alcohol happynewyear2020 \n",
      "\n",
      "238)  healthy resolution no 4 say notobacco happynewyear2020 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 1 Create the word frequency table\n",
    "freq_table = create_frequency_table('. '.join(sentences))\n",
    "\n",
    "# 2 Important Algorithm: score the sentences\n",
    "sentence_scores = score_sentences(sentences, freq_table)\n",
    "\n",
    "# 3 Find the threshold\n",
    "threshold = find_average_score(sentence_scores)\n",
    "\n",
    "# 4 Important Algorithm: Generate the summary\n",
    "summary = generate_summary(sentences, sentence_scores, 1.5 * threshold)\n",
    "\n",
    "#5 ignore repetitive sentences in summary\n",
    "countSummary = 0\n",
    "summaryText = []\n",
    "for i in summary:\n",
    "    if(summary.count(i)==1):\n",
    "        #Trimming extra white spaces\n",
    "        i = re.sub(' +', ' ', i)\n",
    "        \n",
    "        countSummary+=1\n",
    "        summaryText.append(i)\n",
    "        \n",
    "        print(str(countSummary)+\")  \"+i+\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Tweets by WHO: 2290\n",
      "Number of Sentences in Summary: 238\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Tweets by WHO: \"+str(len(sentences)))\n",
    "print(\"Number of Sentences in Summary: \"+str(countSummary))"
   ]
  }
 ],
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