analytics-Version 4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from newsapi import NewsApiClient\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "import re\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "import string\n",
    "from collections import Counter\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import tweepy\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "newsapi = NewsApiClient(api_key = '127ac9edf9da4a56bbce2c6ff98978c4')\n",
    "pd.options.display.max_colwidth = 8000\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "consumerKey = \"jfAlL4ZYSvAcLZjIghNjkPVef\"\n",
    "consumerSecret = \"E2Wd7Fc9kQhMC4x9bTbyo8IzyDOjH7gkgXc5ZzHKBv1na0JdCB\"\n",
    "accessToken = \"1168827517387468800-sqEsr4vVHF4ZQ0BnfyFCqTaK4H17gr\"\n",
    "accessTokenSecret = \"FTFWWelxrVTBlwfWsHSIMe4jKI5ikzkhdZ5QcsKuCNxUS\"\n",
    "\n",
    "authenticate = tweepy.OAuthHandler(consumerKey, consumerSecret)\n",
    "authenticate.set_access_token(accessToken, accessTokenSecret)\n",
    "\n",
    "#calling api\n",
    "api = tweepy.API(authenticate, wait_on_rate_limit = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# All the functions for NewsAPI \n",
    "\n",
    "def get_sources():\n",
    "    sources = {'source':  ['associated-press', 'australian-financial-review', 'bbc-news' , 'bloomberg', 'breitbart-news', 'business-insider',\n",
    "                       'cnn', 'cbc-news', 'financial-post', 'fortune', 'google-news-uk', 'reuters', 'the-huffington-post', 'the-wall-street-journal', 'the-washington-post', \n",
    "                        'time', 'usa-today' ] , 'source name':  ['Associated Press', 'Australian Financial Review', 'BBC News' , 'Bloomberg', 'Breitbart News', 'Business Insider', \n",
    "                       'CNN', 'CBC News ', 'Financial Post', 'Fortune', 'Google News UK', 'Reuters', 'The Huffington Post', 'The Wall Street Journal', 'The Washington Post', \n",
    "                        'Time', 'USA Today' ]}\n",
    "    df = pd.DataFrame(sources, columns = ['source', 'source name' ])\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "def preprocessing (text):\n",
    "    stopwords = nltk.corpus.stopwords.words('english')\n",
    "    another = \"idk,amp,&amp,aboard,about,above,accordance,back,according,across,after,against,ahead,along,alongside,also,although,amid,amidst,among,amongst,and,anent,anti,apart,around,as,aside,astride,at,athwart,atop,barring,because,before,behalf,behind,behither,below,beneath,beside,besides,between,betwixt,beyond,both,but,by,cum,despite,down,due,during,either,ere,even,except,far,following,for,fornenst,fornent,from,front,given,if,in,including,inside,instead,into,lest,lieu,like,means,near,neither,next,nor,notwithstanding,of,off,on,once,only,onto,or,out,outside,outwith,over,owing,per,point,prior,pro,qua,re,regard,regarding,regardless,regards,respect,sans,save,since,so,soon,subsequent,than,thanks,that,though,through,throughout,till,times,to,top,toward,towards,under,underneath,unless,unlike,until,unto,up,upon,versus,via,vice,vis-à-vis,well,when,whenever,where,wherever,whether,while,with,within,without,worth,yet,a,able,all,almost,am,an,any,are,aren't,arent,be,been,can,cannot,could,dear,did,didn't,didnt,do,don't,dont,does,doesn't,doesnt,else,ever,every,get,got,had,has,have,he,her,hers,him,his,how,however,i,is,it,its,just,least,let,likely,may,me,might,most,must,my,no,not,often,other,our,own,rather,said,say,says,she,should,shouldn't,shouldnt,some,the,their,them,then,there,these,they,this,tis,too,twas,us ,was,wasn't,wasnt,we,were,weren't,werent,what,which,who,whom,why,will,would,you,your,a's,accordingly,actually,afterwards,again,ain't,allow,allows,alone,already,always,another,anybody,anyhow,anyone,anything,anyway,anyways,anywhere,away,beforehand,being,c'mon,c's,came,can't,cant,co,com,come,comes,concerning,consequently,corresponding,couldn't,currently,definitely,described,doing,done,downwards,each,edu,eg,eight,elsewhere,enough,entirely,especially,et,etc,everybody,everyone,everything,everywhere,ex,exactly,example,few,fifth,first,five,followed,follows,former,formerly,forth,four,further,furthermore,gets,getting,gives,go,goes,going,gone,gotten,hadn't,happens,hardly,hasn't,haven't,having,he's,hello,help,hence,here,here's,hereafter,hereby,herein,hereupon,herself,hi,himself,hither,hopefully,howbeit,i'd,i'll,i'm,i've,ie,ignored,immediate,inasmuch,inc,indeed,insofar,inward,isn't,it'd,it'll,it's,itself,last,lately,later,latter,latterly,less,let's,liked,ltd,mainly,many,maybe,mean,meanwhile,merely,more,moreover,mostly,much,myself,name,namely,nd,nearly,need,needs,never,nevertheless,new,nine,nobody,non,none,noone,normally,nothing,now,nowhere,obviously,oh,ok,okay,old,one,ones,others,otherwise,ought,ours,ourselves,overall,perhaps,placed,please,possible,presumably,probably,provides,que,quite,qv,rd,really,reasonably,relatively,respectively,right,secondly,see,seeing,seem,seemed,seeming,seems,seen,self,selves,seriously,several,shall,somebody,somehow,someone,something,sometime,sometimes,somewhat,somewhere,sorry,still,sub,such,sup,t's,tends,th,thank,thanx,that's,thats,theirs,themselves,thence,there's,thereafter,thereby,therefore,therein,theres,thereupon,they'd,they'll,they're,they've,think,third,thorough,thoroughly,those,three,thru,thus,together,took,tried,tries,truly,try,trying,twice,two,un,unlikely,usually,very,viz,vs,want,way,we'd,we'll,we're,we've,welcome,went,what's,whatever,whence,where's,whereafter,whereas,whereby,wherein,whereupon,whither,who's,whoever,whole,whose,won't,wonder,wouldn't,yes,you'd,you'll,you're,you've,yours,yourself,yourselves,reuters,ap,e.g, etc, greetings,h,i.e.,inner,j,k,l,m,mr.,ms.,n,o,p,r,s,t,u,uucp,v,w,x,y,z,.com,.ly,.net,.org,aahh,aarrgghh,abt,ftl,ftw,fu,fuck,fucks,gtfo,gtg,haa,hah,hahah,haha,hahaha,hahahaha,hehe,heh,hehehe,hihi,hihihi,http,https,huge,huh,huhu,huhuhu,iirc,im,imho,imo,ini,irl,ish,isn,isnt,j/k,jk,jus,justwit,juz,kinda,kthx,kthxbai,kyou,laa,laaa,lah,lanuch,leavg,leh,lol,lols,mph,mrt,msg,msgs,muahahahahaha,nb,neways,ni,nice,pls,plz,plzz,psd,pte,pwm,pwned,qfmft,qft,tm,tmr,tyty,tyvm,um,umm,viv,vn,wa,wadever,wah,wasn,wassup,wat,watcha,wateva,watever,watnot,wats,wayy,wb,weren,whaha,wham,whammy,whaow,whatcha,whatev,whateva,whatevar,whatnot,whats,whatsoever,whatz,whee,whenz,whey,whore,whores,whoring,win,wo,woah,woh,wooohooo,woot,wow,wrt,wtb,wtf,wth,wts,wtt,www,xs,ya,yaah,yah,yahh,yahoocurrency,yall,yar,yay,yea,yeah,yeahh,yeh,yhoo,ymmv,youre,yr,yum,yummy,yumyum,yw,zomg,zz,zzz,loz,lor,loh,tsk,meh,lmao,wanna,doesn,liao,didn,omg,ohh,ohgod,hoh,hoo,bye,byee,byeee,byeeee,lmaolmao,yeahhh,yeahhhh,yeahhhhh,yup,yupp,hahahahahahaha,hahahahahah,hahhaha,wooohoooo,wahaha,haah,2moro,veh,noo,nooo,noooo,hahas,ooooo,ahahaha,ahahahahah,tomolow,ads,afaik,aft,ago,ain,aint,alot,anycase,anymore,app,apparently,apps,argh,ass,asses,babeh,bai,based,bcos,bcoz,bday,bit,biz,blah,bleh,bro,bros,btw,congrats,contd,conv,cos,couldn,couldnt,coz,crap,curnews,curr,cuz,dat,de,diff,dis,doc,don,dr,dreamt,drs,dun,dunno,duper,eh,ehh,emo,emos,eng,esp,fadein,ffs,fml,frm,fwah,g2g,gajshost,gd,geez,gg,gigs,hasn,hasnt,hav,haven,havent,hee,hey,hmm,ho,hohoho,info,issued,lawl,lfg,lfm,ll,lmfao,lnks,lotsa,lotta,luv,ly,macdailynews,neato,nite,nom,noscript,nvr,nw,ohayo,omfg,omfgwtf,omgwtfbbq,omw,org,pf,pic,pm,pmsing,ppl,pre,rawr,rawrr,rofl,roflmao,rss,rt,sec,secs,sgreinfo,shd,shit,shits,shitz,shld,shouldn,shudder,sq,sqft,sqm,srsly,stfu,stks,su,sux,swf,tat,tgif,thanky,thk,thks,tht,ttyl,ty,tym,tyme,typed,va,var,vc,ve,web,fucking,mrs,mr,ehhh,lot,lots,html,net,hahahahahahahahaha,hahahahaha,hahahahah,zzzzz,#teamfollowback,#teamfollow,#follow,#autofollow,#followgain,#followbackk,#teamautofollow,#followme,#ifollow,#followngain,#followback,#followfriday,#ifollowback,#200aday,#500aday,#1000aday,hahahahha,lolololol,lololol,lolol,dude,hmmm,humm,tumblr,kkkk,fk,yayyyyyy,fffffffuuuuuuuuuuuu,zzzz,noooooooooo,hahahhaha,woohoo,lalalalalalala,lala,lalala,lalalala,whahahaahahahahahah,gimme,gonna,gotta,grats,gratz,happen,happened,nowadays,phew,post,posted,posts,tweet,tweets,#ff,#f4f,#instantfollowback,#autofollowback,#instantfollow\"\n",
    "    additional = another.split(\",\")\n",
    "    stopwords += additional \n",
    "\n",
    "    tokens = re.split('\\W+', text)\n",
    "    text = [lemmatizer.lemmatize(word) for word in tokens]\n",
    "    result = [word for word in tokens if word not in stopwords]       \n",
    "    return result\n",
    "\n",
    "\n",
    "def news_get_everything (query, source):\n",
    "    \n",
    "    data = newsapi.get_everything(q = query, sources = source, language = 'en', sort_by = 'relevancy' , page_size = 15) \n",
    "    df = pd.DataFrame(data['articles'])    \n",
    "    df['clean_description'] =  df['description'].apply(lambda x: preprocessing(x.lower())) \n",
    "    \n",
    "    for index, row in df.iterrows():\n",
    "        published_at = row['publishedAt'].split('T')[0]\n",
    "        row['publishedAt'] = published_at.replace(\"-\", \"/\")\n",
    "         \n",
    "    \n",
    "    senti_score = []\n",
    "    result = []\n",
    "\n",
    "    \n",
    "    for index, row in df.iterrows():\n",
    "        analyzer = SentimentIntensityAnalyzer()\n",
    "        vs = analyzer.polarity_scores(row['description'])  \n",
    "        compound = vs.get('compound') \n",
    "        senti_score.append(compound) \n",
    "        \n",
    "        if compound >= 0.05:\n",
    "            result.append('Positive')\n",
    "        elif compound <= -0.05:\n",
    "            result.append('Negative')\n",
    "        else:\n",
    "            result.append('Neutral')    \n",
    "            \n",
    "    df['sentiment'] = result\n",
    "    df['compound'] = senti_score\n",
    "    \n",
    "    df['date'] = df['publishedAt']\n",
    "   \n",
    "    return df\n",
    "\n",
    "\n",
    "def news_overallsentiment(df):\n",
    "    \n",
    "\n",
    "    postnews = df[df.sentiment == 'Positive']\n",
    "    postnews = postnews['description']\n",
    "    postnews\n",
    "\n",
    "    negnews = df[df.sentiment == 'Negative']\n",
    "    negnews = negnews['description']\n",
    "    \n",
    "\n",
    "    neunews = df[df.sentiment == 'Neutral']\n",
    "    neunews = neunews['description']\n",
    "    \n",
    "    articles = {'Sentiment': ['Positive','Negative','Neutral'],\n",
    "        'Overall Score': [round((postnews.shape[0] / df.shape[0]) *100, 1),round( (negnews.shape[0] / df.shape[0] * 100) , 1), round( (neunews.shape[0] / df.shape[0] * 100) , 1)]\n",
    "        }\n",
    "    df = pd.DataFrame(articles, columns = ['Sentiment', 'Overall Score'])\n",
    "    \n",
    "    \n",
    "    return df\n",
    "\n",
    "def news_top_mentioned(df,query):\n",
    "    word_list = []\n",
    "    for index, rows in df.iterrows():\n",
    "        for each_word in rows.clean_description:            \n",
    "            if len(each_word) > 3 and each_word != query:      \n",
    "                word_list.append(each_word)  \n",
    "\n",
    "    counts = dict()\n",
    "    for word in word_list:\n",
    "        if word in counts:\n",
    "            counts[word] += 1\n",
    "        else:\n",
    "            counts[word] = 1           \n",
    "\n",
    "    df = pd.DataFrame(list(counts.items()),columns = ['word','count'])     \n",
    "    df = df.sort_values(['count'], ascending=[False])[0:10]\n",
    "   \n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "def news_create_wordcloud(df, query):    \n",
    "    word_list = []\n",
    "    for index, rows in df.iterrows():\n",
    "        for each_word in rows.clean_description:\n",
    "            if len(each_word) > 3 and each_word != query:\n",
    "                word_list.append(each_word)\n",
    "\n",
    "    words_joined = \" \".join([w for w in word_list])\n",
    "    my_wordcloud = WordCloud(background_color='white', width=1800, height=1400).generate(words_joined)\n",
    "\n",
    "    \n",
    "    plt.imshow(my_wordcloud)    \n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#All the functions for Twitter\n",
    "\n",
    "\n",
    "def clean_tweets(text):  \n",
    "    \n",
    "    text = re.sub(r'@[A-Za-z0-9]+', '', text)\n",
    "    text = re.sub(r'RT[\\s]+', '',  text) # removing RT\n",
    "    text = re.sub(r'https?:\\/\\/\\S+', '', text) #Remove the hyper link\n",
    "    text = text.replace('\\n','')\n",
    "    text = text.replace(':','')\n",
    "        \n",
    "    return text\n",
    "\n",
    "\n",
    "def clean_txt_list(text):\n",
    "    \n",
    "    stopwords = nltk.corpus.stopwords.words('english')\n",
    "    another = \"idk,amp,&amp,aboard,kindly,about,above,accordance,according,across,magsafe,amp,twitter,back,after,against,ahead,along,alongside,also,although,amid,amidst,neck, use, uses, using, saying, says, said, among,amongst,and,anent,anti,apart,around,as,aside,astride,at,athwart,atop,barring,because,before,behalf,behind,behither,below,beneath,beside,besides,between,betwixt,beyond,both,but,by,cum,despite,down,due,during,either,ere,even,except,far,following,for,fornenst,fornent,from,front,given,if,in,including,inside,instead,into,lest,lieu,like,means,near,neither,next,nor,notwithstanding,of,off,on,once,only,onto,or,out,outside,outwith,over,owing,per,point,prior,pro,qua,re,regard,regarding,regardless,regards,respect,sans,save,since,so,soon,subsequent,than,thanks,that,though,through,throughout,till,times,to,top,toward,towards,under,underneath,unless,unlike,until,unto,up,upon,versus,via,vice,vis-à-vis,well,when,whenever,where,wherever,whether,while,with,within,without,worth,yet,a,able,all,almost,am,an,any,are,aren't,arent,be,been,can,cannot,could,dear,did,didn't,didnt,do,don't,dont,does,doesn't,doesnt,else,ever,every,get,got,had,has,have,he,her,hers,him,his,how,however,i,is,it,its,just,least,let,likely,may,me,might,most,must,my,no,not,often,other,our,own,rather,said,say,says,she,should,shouldn't,shouldnt,some,the,their,them,then,there,these,they,this,tis,too,twas,us ,was,wasn't,wasnt,we,were,weren't,werent,what,which,who,whom,why,will,would,you,your,a's,accordingly,actually,afterwards,again,ain't,allow,allows,alone,already,always,another,anybody,anyhow,anyone,anything,anyway,anyways,anywhere,away,beforehand,being,c'mon,c's,came,can't,cant,co,com,come,comes,concerning,consequently,corresponding,couldn't,currently,definitely,described,doing,done,downwards,each,edu,eg,eight,elsewhere,enough,entirely,especially,et,etc,everybody,everyone,everything,everywhere,ex,exactly,example,few,fifth,first,five,followed,follows,former,formerly,forth,four,further,furthermore,gets,getting,gives,go,goes,going,gone,gotten,hadn't,happens,hardly,hasn't,haven't,having,he's,hello,help,hence,here,here's,hereafter,hereby,herein,hereupon,herself,hi,himself,hither,hopefully,howbeit,i'd,i'll,i'm,i've,ie,ignored,immediate,inasmuch,inc,indeed,insofar,inward,isn't,it'd,it'll,it's,itself,last,lately,later,latter,latterly,less,let's,liked,ltd,mainly,many,maybe,mean,meanwhile,merely,more,moreover,mostly,much,myself,name,namely,nd,nearly,need,needs,never,nevertheless,new,nine,nobody,non,none,noone,normally,nothing,now,nowhere,obviously,oh,ok,okay,old,one,ones,others,otherwise,ought,ours,ourselves,overall,perhaps,placed,please,possible,presumably,probably,provides,que,quite,qv,rd,really,reasonably,relatively,respectively,right,secondly,see,seeing,seem,seemed,seeming,seems,seen,self,selves,seriously,several,shall,somebody,somehow,someone,something,sometime,sometimes,somewhat,somewhere,sorry,still,sub,such,sup,t's,tends,th,thank,thanx,that's,thats,theirs,themselves,thence,there's,thereafter,thereby,therefore,therein,theres,thereupon,they'd,they'll,they're,they've,think,third,thorough,thoroughly,those,three,thru,thus,together,took,tried,tries,truly,try,trying,twice,two,un,unlikely,usually,very,viz,vs,want,way,we'd,we'll,we're,we've,welcome,went,what's,whatever,whence,where's,whereafter,whereas,whereby,wherein,whereupon,whither,who's,whoever,whole,whose,won't,wonder,wouldn't,yes,you'd,you'll,you're,you've,yours,yourself,yourselves,reuters,ap,e.g, etc, greetings,h,i.e.,inner,j,k,l,m,mr.,ms.,n,o,p,r,s,t,u,uucp,v,w,x,y,z,.com,.ly,.net,.org,aahh,aarrgghh,abt,ftl,ftw,fu,fuck,fucks,gtfo,gtg,haa,hah,hahah,haha,hahaha,hahahaha,hehe,heh,hehehe,hihi,hihihi,http,https,huge,huh,huhu,huhuhu,iirc,im,imho,imo,ini,irl,ish,isn,isnt,j/k,jk,jus,justwit,juz,kinda,kthx,kthxbai,kyou,laa,laaa,lah,lanuch,leavg,leh,lol,lols,mph,mrt,msg,msgs,muahahahahaha,nb,neways,ni,nice,pls,plz,plzz,psd,pte,pwm,pwned,qfmft,qft,tm,tmr,tyty,tyvm,um,umm,viv,vn,wa,wadever,wah,wasn,wassup,wat,watcha,wateva,watever,watnot,wats,wayy,wb,weren,whaha,wham,whammy,whaow,whatcha,whatev,whateva,whatevar,whatnot,whats,whatsoever,whatz,whee,whenz,whey,whore,whores,whoring,win,wo,woah,woh,wooohooo,woot,wow,wrt,wtb,wtf,wth,wts,wtt,www,xs,ya,yaah,yah,yahh,yahoocurrency,yall,yar,yay,yea,yeah,yeahh,yeh,yhoo,ymmv,youre,yr,yum,yummy,yumyum,yw,zomg,zz,zzz,loz,lor,loh,tsk,meh,lmao,wanna,doesn,liao,didn,omg,ohh,ohgod,hoh,hoo,bye,byee,byeee,byeeee,lmaolmao,yeahhh,yeahhhh,yeahhhhh,yup,yupp,hahahahahahaha,hahahahahah,hahhaha,wooohoooo,wahaha,haah,2moro,veh,noo,nooo,noooo,hahas,ooooo,ahahaha,ahahahahah,tomolow,ads,afaik,aft,ago,ain,aint,alot,anycase,anymore,app,apparently,apps,argh,ass,asses,babeh,bai,based,bcos,bcoz,bday,bit,biz,blah,bleh,bro,bros,btw,congrats,contd,conv,cos,couldn,couldnt,coz,crap,curnews,curr,cuz,dat,de,diff,dis,doc,don,dr,dreamt,drs,dun,dunno,duper,eh,ehh,emo,emos,eng,esp,fadein,ffs,fml,frm,fwah,g2g,gajshost,gd,geez,gg,gigs,hasn,hasnt,hav,haven,havent,hee,hey,hmm,ho,hohoho,info,issued,lawl,lfg,lfm,ll,lmfao,lnks,lotsa,lotta,luv,ly,macdailynews,neato,nite,nom,noscript,nvr,nw,ohayo,omfg,omfgwtf,omgwtfbbq,omw,org,pf,pic,pm,pmsing,ppl,pre,rawr,rawrr,rofl,roflmao,rss,rt,sec,secs,sgreinfo,shd,shit,shits,shitz,shld,shouldn,shudder,sq,sqft,sqm,srsly,stfu,stks,su,sux,swf,tat,tgif,thanky,thk,thks,tht,ttyl,ty,tym,tyme,typed,va,var,vc,ve,web,fucking,mrs,mr,ehhh,lot,lots,html,net,hahahahahahahahaha,hahahahaha,hahahahah,zzzzz,#teamfollowback,#teamfollow,#follow,#autofollow,#followgain,#followbackk,#teamautofollow,#followme,#ifollow,#followngain,#followback,#followfriday,#ifollowback,#200aday,#500aday,#1000aday,hahahahha,lolololol,lololol,lolol,dude,hmmm,humm,tumblr,kkkk,fk,yayyyyyy,fffffffuuuuuuuuuuuu,zzzz,noooooooooo,hahahhaha,woohoo,lalalalalalala,lala,lalala,lalalala,whahahaahahahahahah,gimme,gonna,gotta,grats,gratz,happen,happened,nowadays,phew,post,posted,posts,tweet,tweets,#ff,#f4f,#instantfollowback,#autofollowback,#instantfollow\"\n",
    "    additional = another.split(\",\")\n",
    "    stopwords += additional \n",
    "   \n",
    "    text = re.sub(r'@[A-Za-z0-9]+', '', text) \n",
    "    text = re.sub(r'#', '', text) \n",
    "    text = re.sub(r'RT[\\s]+', '',  text) \n",
    "    text = re.sub(r'\\n[\\s]+', '',  text) \n",
    "    text = ''.join([i for i in text if not i.isdigit()]) \n",
    "    text = re.sub(r'https?:\\/\\/\\S+', '', text) \n",
    "    text = text.lower() \n",
    "    text =  ' '.join(word.strip(string.punctuation) for word in text.split()) \n",
    "    tokens = re.split('\\W+', text)\n",
    "    text = [lemmatizer.lemmatize(word) for word in tokens]\n",
    "    text = [word for word in text if word not in stopwords] \n",
    "    \n",
    "    return text    \n",
    "\n",
    "\n",
    "def tweet_search(query, resulttype):\n",
    "    \n",
    "    tweets_search = tweepy.Cursor(api.search, q = query+ ' -filter:retweets', lang = 'en' , result_type = resulttype, tweet_mode='extended').items(15)\n",
    "    tweets_list = []\n",
    "    url_list = []\n",
    "    date_list = []\n",
    "\n",
    "\n",
    "    for tweet in tweets_search:\n",
    "        tweets_list.append(tweet.full_text) \n",
    "        date = tweet.created_at.strftime(\"%Y/%m/%d\")\n",
    "        date_list.append(date)        \n",
    "        url = f\"https://twitter.com/{tweet.user.screen_name}/status/{tweet.id}\"\n",
    "        url_list.append(url)     \n",
    "\n",
    "    data = {'Tweets': tweets_list, 'URL': url_list, 'Date': date_list}\n",
    "    df = pd.DataFrame(data) \n",
    "    \n",
    "    df['Tweets'] = df['Tweets'].apply(clean_tweets)  \n",
    "    df['CleanedList'] = df['Tweets'].apply(clean_txt_list)\n",
    "    \n",
    "    senti_score = []    \n",
    "    result = []\n",
    "   \n",
    "    for index, row in df.iterrows():\n",
    "        analyzer = SentimentIntensityAnalyzer()\n",
    "        vs = analyzer.polarity_scores(row['Tweets'])  \n",
    "        compound = vs.get('compound')         \n",
    "       \n",
    "       \n",
    "        \n",
    "        if compound >= 0.05:\n",
    "            result.append('Positive')\n",
    "        elif compound <= -0.05:\n",
    "            result.append('Negative')\n",
    "        else:\n",
    "            result.append('Neutral')    \n",
    "\n",
    "  \n",
    "    df['Sentiment'] = result   \n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "def twt_top_mentioned(df,query):\n",
    "    word_list = []\n",
    "    for index, rows in df.iterrows():\n",
    "        for each_word in rows.CleanedList:\n",
    "            if len(each_word) > 3 and each_word != query:\n",
    "                    word_list.append(each_word)   \n",
    "    \n",
    "    counts = dict()\n",
    "    for word in word_list:\n",
    "        if word in counts:\n",
    "            counts[word] += 1\n",
    "        else:\n",
    "            counts[word] = 1           \n",
    "\n",
    "    df = pd.DataFrame(list(counts.items()),columns = ['word','count'])     \n",
    "    df = df.sort_values(['count'], ascending=[False])[0:10]  \n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "def twt_overallsentiment(df):\n",
    "\n",
    "    postweets = df[df.Sentiment == 'Positive']\n",
    "    postweets = postweets['Tweets']\n",
    "    \n",
    "\n",
    "    negtweets = df[df.Sentiment == 'Negative']\n",
    "    negtweets = negtweets['Tweets']\n",
    "    \n",
    "\n",
    "    neutweets = df[df.Sentiment == 'Neutral']\n",
    "    neutweets = neutweets['Tweets']\n",
    "    \n",
    "    tweets = {'Sentiment': ['Positive','Negative','Neutral'],\n",
    "        'Overall Score': [round((postweets.shape[0] / df.shape[0]) *100, 1),round( (negtweets.shape[0] / df.shape[0] * 100) , 1), round( (neutweets.shape[0] / df.shape[0] * 100) , 1)]\n",
    "        }\n",
    "    df = pd.DataFrame(tweets, columns = ['Sentiment', 'Overall Score'])    \n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "def twt_create_wordcloud(df,query):    \n",
    "    word_list = []\n",
    "    for index, rows in df.iterrows():\n",
    "        for each_word in rows.CleanedList:\n",
    "            if  len(each_word) > 3 and each_word != query:\n",
    "                word_list.append(each_word)\n",
    "\n",
    "                \n",
    "    words_joined = \" \".join([w for w in word_list])\n",
    "    my_wordcloud = WordCloud(background_color='white', width=1800, height=1400).generate(words_joined)\n",
    "    \n",
    "    plt.imshow(my_wordcloud)    \n",
    "    plt.axis('off')\n",
    "    plt.show()  \n",
    "\n",
    "    \n",
    "def time_series(query):  \n",
    "\n",
    "    tweets_search = tweepy.Cursor(api.search, q = query+ ' -filter:retweets', result_type = 'recent', lang = 'en' , tweet_mode='extended').items(100)    \n",
    "\n",
    "    tweets_list = []\n",
    "    date_time = []\n",
    "\n",
    "    for tweet in tweets_search:\n",
    "        tweets_list.append(tweet.full_text)\n",
    "        tweet.created_at = tweet.created_at + timedelta(hours=8)\n",
    "        date_time.append(tweet.created_at)\n",
    "\n",
    "    data = {'Tweets': tweets_list,  'Date_Time': date_time}\n",
    "    df = pd.DataFrame(data) \n",
    "          \n",
    "\n",
    "    df['Tweets'] = df['Tweets'].apply(clean_tweets)\n",
    "\n",
    "    \n",
    "    senti_score = []    \n",
    "    for index, row in df.iterrows():\n",
    "        analyzer = SentimentIntensityAnalyzer()\n",
    "        vs = analyzer.polarity_scores(row['Tweets'])  \n",
    "        compound = vs.get('compound')         \n",
    "        senti_score.append(round(compound,2))          \n",
    "  \n",
    "    df['Sentiment'] = senti_score   \n",
    "\n",
    "    df_sampled = df.set_index('Date_Time').resample('5S').mean().reset_index()\n",
    "    df_sampled['Sentiment'] = df_sampled['Sentiment'].fillna(0)\n",
    "    df_sampled['Date_Time'] = df_sampled['Date_Time'].dt.time\n",
    "    \n",
    "    return df_sampled\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WmFm1JeqfYTqjTiMIjcXT6awndf207QLEpyah/sVF1D09DXk/9uFeBHx9Q4Vd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hP9e6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\nkCooKCgYiSKkCgoKCkbyf/e+0l/EZquUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "temp_df = news_get_everything('stock' , 'bbc-news')  #always call this very basic dataframe first\n",
    "\n",
    "\n",
    "df_source = get_sources()\n",
    "df_selected = temp_df[['title', 'url', 'urlToImage', 'sentiment', 'description','date']][0:5] # for table\n",
    "df_topword = news_top_mentioned(temp_df, 'stock') #  for bar chart\n",
    "news_create_wordcloud(temp_df, 'stock')  # for wordcloud\n",
    "df_overallsent = news_overallsentiment(df_selected)   # for pie chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tYpOc+Q35jUsiPTsJe160F2TNhsevVJjGvaEPQm7588b7xXSXa40mpH/Cyqcj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\nzQv6x7RIwdHa3igRHVxzEt779G30m9hN1n7wwR7GZxS+xuqoIYptEX1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F/KMXqNmY3+eU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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OI+VbxjPLlOvVLfu9/a7zllMaqb4waPVBotmcjRwNsqfHHsE2tSd7E8/ef2LJ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\ni5pohVSLFi1a1EQrpFq0aNGiJv8DRLmPnmtD2zcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_recent = tweet_search('stock', 'recent')   # always call this basic data frame first\n",
    "temp_pop = tweet_search('stock', 'popular')     \n",
    "\n",
    "\n",
    "# Below are 9 data frames Harvey needs\n",
    "\n",
    "tweets_recent = temp_recent[['Tweets', 'Sentiment', 'URL', 'Date']][0:5]    # for table \n",
    "tweets_pop =  temp_pop[['Tweets', 'Sentiment','URL', 'Date']][0:5]      \n",
    "\n",
    "\n",
    "\n",
    "topword_recent = twt_top_mentioned(temp_recent, 'stock') # for bar chart \n",
    "topword_pop = twt_top_mentioned(temp_pop, 'stock') \n",
    "\n",
    "overall_sent_recent= twt_overallsentiment(temp_recent)   # for pie chart \n",
    "overall_sent_pop = twt_overallsentiment(temp_pop)   \n",
    "\n",
    "\n",
    "twt_create_wordcloud(temp_recent,'stock')  #word cloud \n",
    "twt_create_wordcloud(temp_pop, 'stock')  \n",
    "\n",
    "\n",
    "df_timeseries = time_series('stock') # for time series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>source name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>associated-press</td>\n",
       "      <td>Associated Press</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>australian-financial-review</td>\n",
       "      <td>Australian Financial Review</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bbc-news</td>\n",
       "      <td>BBC News</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bloomberg</td>\n",
       "      <td>Bloomberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>breitbart-news</td>\n",
       "      <td>Breitbart News</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>business-insider</td>\n",
       "      <td>Business Insider</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cnn</td>\n",
       "      <td>CNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cbc-news</td>\n",
       "      <td>CBC News</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>financial-post</td>\n",
       "      <td>Financial Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fortune</td>\n",
       "      <td>Fortune</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>google-news-uk</td>\n",
       "      <td>Google News UK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>reuters</td>\n",
       "      <td>Reuters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>the-huffington-post</td>\n",
       "      <td>The Huffington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>the-wall-street-journal</td>\n",
       "      <td>The Wall Street Journal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>the-washington-post</td>\n",
       "      <td>The Washington Post</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>time</td>\n",
       "      <td>Time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>usa-today</td>\n",
       "      <td>USA Today</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         source                  source name\n",
       "0              associated-press             Associated Press\n",
       "1   australian-financial-review  Australian Financial Review\n",
       "2                      bbc-news                     BBC News\n",
       "3                     bloomberg                    Bloomberg\n",
       "4                breitbart-news               Breitbart News\n",
       "5              business-insider             Business Insider\n",
       "6                           cnn                          CNN\n",
       "7                      cbc-news                    CBC News \n",
       "8                financial-post               Financial Post\n",
       "9                       fortune                      Fortune\n",
       "10               google-news-uk               Google News UK\n",
       "11                      reuters                      Reuters\n",
       "12          the-huffington-post          The Huffington Post\n",
       "13      the-wall-street-journal      The Wall Street Journal\n",
       "14          the-washington-post          The Washington Post\n",
       "15                         time                         Time\n",
       "16                    usa-today                    USA Today"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>urlToImage</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>description</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Playboy goes from private to public in new venture</td>\n",
       "      <td>https://www.bbc.co.uk/news/business-54381937</td>\n",
       "      <td>https://ichef.bbci.co.uk/news/1024/branded_news/0E76/production/_114720730_gettyimages-914010152.jpg</td>\n",
       "      <td>Positive</td>\n",
       "      <td>The adult entertainment empire is to go public again nine years after founder Hugh Hefner took it private</td>\n",
       "      <td>2020/10/02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Samsung Group titan Lee Kun-hee dies aged 78</td>\n",
       "      <td>https://www.bbc.co.uk/news/world-asia-54679562</td>\n",
       "      <td>https://ichef.bbci.co.uk/news/1024/branded_news/892C/production/_115061153_hi064004163.jpg</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Mr Lee helped to grow his father's small trading business into a global industrial powerhouse.</td>\n",
       "      <td>2020/10/25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stock markets sink amid alarm at Covid surge</td>\n",
       "      <td>https://www.bbc.co.uk/news/business-54723911</td>\n",
       "      <td>https://ichef.bbci.co.uk/news/1024/branded_news/FA96/production/_115105146_hi064034904.jpg</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Financial markets in the US and Europe fall as concerns rise about the coronavirus impact on economic growth.</td>\n",
       "      <td>2020/10/28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Technical glitch halts trading on Japan's exchanges</td>\n",
       "      <td>https://www.bbc.co.uk/news/business-54366606</td>\n",
       "      <td>https://ichef.bbci.co.uk/news/1024/branded_news/4682/production/_114705081_gettyimages-1219407783.jpg</td>\n",
       "      <td>Negative</td>\n",
       "      <td>Stock markets in Tokyo and other cities suffered suspended trading on Thursday.</td>\n",
       "      <td>2020/10/01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jack Ma's Ant Group set for record stock market listing</td>\n",
       "      <td>https://www.bbc.co.uk/news/business-54692537</td>\n",
       "      <td>https://ichef.bbci.co.uk/news/1024/branded_news/15515/production/_111271378_gettyimages-1192058124.jpg</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>The Chinese financial firm backed by the billionaire behind Alibaba aims to raise more than $34bn.</td>\n",
       "      <td>2020/10/26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     title  \\\n",
       "0       Playboy goes from private to public in new venture   \n",
       "1             Samsung Group titan Lee Kun-hee dies aged 78   \n",
       "2             Stock markets sink amid alarm at Covid surge   \n",
       "3      Technical glitch halts trading on Japan's exchanges   \n",
       "4  Jack Ma's Ant Group set for record stock market listing   \n",
       "\n",
       "                                              url  \\\n",
       "0    https://www.bbc.co.uk/news/business-54381937   \n",
       "1  https://www.bbc.co.uk/news/world-asia-54679562   \n",
       "2    https://www.bbc.co.uk/news/business-54723911   \n",
       "3    https://www.bbc.co.uk/news/business-54366606   \n",
       "4    https://www.bbc.co.uk/news/business-54692537   \n",
       "\n",
       "                                                                                               urlToImage  \\\n",
       "0    https://ichef.bbci.co.uk/news/1024/branded_news/0E76/production/_114720730_gettyimages-914010152.jpg   \n",
       "1              https://ichef.bbci.co.uk/news/1024/branded_news/892C/production/_115061153_hi064004163.jpg   \n",
       "2              https://ichef.bbci.co.uk/news/1024/branded_news/FA96/production/_115105146_hi064034904.jpg   \n",
       "3   https://ichef.bbci.co.uk/news/1024/branded_news/4682/production/_114705081_gettyimages-1219407783.jpg   \n",
       "4  https://ichef.bbci.co.uk/news/1024/branded_news/15515/production/_111271378_gettyimages-1192058124.jpg   \n",
       "\n",
       "  sentiment  \\\n",
       "0  Positive   \n",
       "1   Neutral   \n",
       "2  Positive   \n",
       "3  Negative   \n",
       "4   Neutral   \n",
       "\n",
       "                                                                                                     description  \\\n",
       "0      The adult entertainment empire is to go public again nine years after founder Hugh Hefner took it private   \n",
       "1                 Mr Lee helped to grow his father's small trading business into a global industrial powerhouse.   \n",
       "2  Financial markets in the US and Europe fall as concerns rise about the coronavirus impact on economic growth.   \n",
       "3                                Stock markets in Tokyo and other cities suffered suspended trading on Thursday.   \n",
       "4             The Chinese financial firm backed by the billionaire behind Alibaba aims to raise more than $34bn.   \n",
       "\n",
       "         date  \n",
       "0  2020/10/02  \n",
       "1  2020/10/25  \n",
       "2  2020/10/28  \n",
       "3  2020/10/01  \n",
       "4  2020/10/26  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>firm</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>wait</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>online</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>financial</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>president</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>trading</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>markets</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>promises</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>false</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>pledges</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         word  count\n",
       "34       firm      3\n",
       "55       wait      2\n",
       "41     online      2\n",
       "18  financial      2\n",
       "51  president      2\n",
       "13    trading      2\n",
       "19    markets      2\n",
       "87   promises      1\n",
       "86      false      1\n",
       "85    pledges      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Overall Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Positive</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Negative</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sentiment  Overall Score\n",
       "0  Positive           40.0\n",
       "1  Negative           20.0\n",
       "2   Neutral           40.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overallsent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>URL</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mmm dong gripbut ewww wired stock</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>https://twitter.com/EyDatsPretyGuud/status/1322044922862723073</td>\n",
       "      <td>2020/10/30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Google revenue jumps to $46 billion, stock soars</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>https://twitter.com/FutureJusticeR1/status/1322044908333662209</td>\n",
       "      <td>2020/10/30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Is Stawberry Lemonade going away? Seems to be out of stock everywhere in Nashville &amp;amp; we buy 8+ cases of 12 each month.</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>https://twitter.com/adnamay/status/1322044893850730496</td>\n",
       "      <td>2020/10/30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>As long as he doesn't just dump the shares onto the market all at once without regard to share price, he could get most of the expected value -- especially on a stock that has decent trading volume and range per day. More time to sell off yields greater return, less time = less.</td>\n",
       "      <td>Positive</td>\n",
       "      <td>https://twitter.com/Epinnoia/status/1322044856982675457</td>\n",
       "      <td>2020/10/30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[READY STOCK] OFFICIAL LIGHTSTICK EXO Rp 400.000 ✉ 0812.8265.4637 line ID  FBG3250G</td>\n",
       "      <td>Positive</td>\n",
       "      <td>https://twitter.com/tenstarshop/status/1322044844051636225</td>\n",
       "      <td>2020/10/30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                      Tweets  \\\n",
       "0                                                                                                                                                                                                                                                          mmm dong gripbut ewww wired stock   \n",
       "1                                                                                                                                                                                                                                          Google revenue jumps to $46 billion, stock soars    \n",
       "2                                                                                                                                                                 Is Stawberry Lemonade going away? Seems to be out of stock everywhere in Nashville &amp; we buy 8+ cases of 12 each month.   \n",
       "3    As long as he doesn't just dump the shares onto the market all at once without regard to share price, he could get most of the expected value -- especially on a stock that has decent trading volume and range per day. More time to sell off yields greater return, less time = less.   \n",
       "4                                                                                                                                                                                                       [READY STOCK] OFFICIAL LIGHTSTICK EXO Rp 400.000 ✉ 0812.8265.4637 line ID  FBG3250G    \n",
       "\n",
       "  Sentiment                                                             URL  \\\n",
       "0   Neutral  https://twitter.com/EyDatsPretyGuud/status/1322044922862723073   \n",
       "1   Neutral  https://twitter.com/FutureJusticeR1/status/1322044908333662209   \n",
       "2   Neutral          https://twitter.com/adnamay/status/1322044893850730496   \n",
       "3  Positive         https://twitter.com/Epinnoia/status/1322044856982675457   \n",
       "4  Positive      https://twitter.com/tenstarshop/status/1322044844051636225   \n",
       "\n",
       "         Date  \n",
       "0  2020/10/30  \n",
       "1  2020/10/30  \n",
       "2  2020/10/30  \n",
       "3  2020/10/30  \n",
       "4  2020/10/30  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_recent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Success in a Biden-Harris Administration will not be measured just by the stock market or GDP growth, but by the extent to which growth is raising the pay, dignity, and economic security of our working families—especially those who have been left behind.</td>\n",
       "      <td>Positive</td>\n",
       "      <td>https://twitter.com/JoeBiden/status/1321823865165778944</td>\n",
       "      <td>2020/10/29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stock market tumblesCases reach record highsStranded voters in OmahaWelcome to Trump's closing week.</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>https://twitter.com/Amy_Siskind/status/1321461871787036679</td>\n",
       "      <td>2020/10/28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The virus is spreading uncontrolled. 80,000 cases, 1,000 deaths today. The stock market dropped by over 900 points. Trump has given up, and if he’s re-elected he will see it as a mandate to keep undermining efforts to control the spread. 2021 will be worse than 2020.</td>\n",
       "      <td>Negative</td>\n",
       "      <td>https://twitter.com/ChrisMurphyCT/status/1321629333040017408</td>\n",
       "      <td>2020/10/29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Imagine working so many hours that in June your studio head sent out an email apologizing to everyone’s spouses/partners because it “often means [the devs] cannot participate on the home front” and then seeing quotes like this from an executive who owns $70 million in stock</td>\n",
       "      <td>Positive</td>\n",
       "      <td>https://twitter.com/jasonschreier/status/1321776461062295558</td>\n",
       "      <td>2020/10/29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Economics lesson for the soon to be ex-President  the virus needed to be contained to help the economy and stock. Fed stimulus would never be enough. You crushed the stock market and the economy by not understanding this.</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>https://twitter.com/Scaramucci/status/1321547476738936835</td>\n",
       "      <td>2020/10/28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                Tweets  \\\n",
       "0                      Success in a Biden-Harris Administration will not be measured just by the stock market or GDP growth, but by the extent to which growth is raising the pay, dignity, and economic security of our working families—especially those who have been left behind.    \n",
       "1                                                                                                                                                                                 Stock market tumblesCases reach record highsStranded voters in OmahaWelcome to Trump's closing week.   \n",
       "2          The virus is spreading uncontrolled. 80,000 cases, 1,000 deaths today. The stock market dropped by over 900 points. Trump has given up, and if he’s re-elected he will see it as a mandate to keep undermining efforts to control the spread. 2021 will be worse than 2020.   \n",
       "3  Imagine working so many hours that in June your studio head sent out an email apologizing to everyone’s spouses/partners because it “often means [the devs] cannot participate on the home front” and then seeing quotes like this from an executive who owns $70 million in stock    \n",
       "4                                                        Economics lesson for the soon to be ex-President  the virus needed to be contained to help the economy and stock. Fed stimulus would never be enough. You crushed the stock market and the economy by not understanding this.   \n",
       "\n",
       "  Sentiment                                                           URL  \\\n",
       "0  Positive       https://twitter.com/JoeBiden/status/1321823865165778944   \n",
       "1   Neutral    https://twitter.com/Amy_Siskind/status/1321461871787036679   \n",
       "2  Negative  https://twitter.com/ChrisMurphyCT/status/1321629333040017408   \n",
       "3  Positive  https://twitter.com/jasonschreier/status/1321776461062295558   \n",
       "4   Neutral     https://twitter.com/Scaramucci/status/1321547476738936835   \n",
       "\n",
       "         Date  \n",
       "0  2020/10/29  \n",
       "1  2020/10/28  \n",
       "2  2020/10/29  \n",
       "3  2020/10/29  \n",
       "4  2020/10/28  "
      ]
     },
     "execution_count": 11,
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   "source": [
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       "        word  count\n",
       "55      leaf      2\n",
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       "48    celery      2\n",
       "16     share      2\n",
       "25      time      2\n",
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       "      <th>24</th>\n",
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       "      <th>72</th>\n",
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       "      <td>2</td>\n",
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       "      <th>66</th>\n",
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       "         word  count\n",
       "5      market     14\n",
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       "21      trump      5\n",
       "24      virus      3\n",
       "1       biden      3\n",
       "86   interest      2\n",
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       "  Sentiment  Overall Score\n",
       "0  Positive           33.3\n",
       "1  Negative           13.3\n",
       "2   Neutral           53.3"
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       "  Sentiment  Overall Score\n",
       "0  Positive           40.0\n",
       "1  Negative           26.7\n",
       "2   Neutral           33.3"
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       "      <th>62</th>\n",
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       "   Date_Time  Sentiment\n",
       "0   13:12:15   0.615000\n",
       "1   13:12:20  -0.690000\n",
       "2   13:12:25   0.405000\n",
       "3   13:12:30   0.220000\n",
       "4   13:12:35  -0.090000\n",
       "..       ...        ...\n",
       "58  13:17:05   0.066667\n",
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       "60  13:17:15   0.000000\n",
       "61  13:17:20   0.000000\n",
       "62  13:17:25   0.000000\n",
       "\n",
       "[63 rows x 2 columns]"
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