2,50,000 tweets, Stock opening and closing prices of Microsoft from August 31st, 2015 to August 25th, 2016
EXTRACTIVE
Collected tweets and opening and closing stock prices of Microsoft.
ABSTRACTIVE
A total of 2,50,000 tweets over a period of August 31st, 2015 to August 25th,2016 on Microsoft are extracted from twitter API BIBREF15 . Twitter4J is a java application which helps us to extract tweets from twitter. The tweets were collected using Twitter API and filtered using keywords like $ MSFT, # Microsoft, #Windows etc. Not only the opinion of public about the company's stock but also the opinions about products and services offered by the company would have a significant impact and are worth studying. Based on this principle, the keywords used for filtering are devised with extensive care and tweets are extracted in such a way that they represent the exact emotions of public about Microsoft over a period of time. The news on twitter about Microsoft and tweets regarding the product releases were also included. Stock opening and closing prices of Microsoft from August 31st, 2015 to August 25th, 2016 are obtained from Yahoo! Finance BIBREF16 .
A total of 2,50,000 tweets over a period of August 31st, 2015 to August 25th,2016 on Microsoft are extracted from twitter API BIBREF15 . Twitter4J is a java application which helps us to extract tweets from twitter. The tweets were collected using Twitter API and filtered using keywords like $ MSFT, # Microsoft, #Windows etc. Not only the opinion of public about the company's stock but also the opinions about products and services offered by the company would have a significant impact and are worth studying. Based on this principle, the keywords used for filtering are devised with extensive care and tweets are extracted in such a way that they represent the exact emotions of public about Microsoft over a period of time. The news on twitter about Microsoft and tweets regarding the product releases were also included. Stock opening and closing prices of Microsoft from August 31st, 2015 to August 25th, 2016 are obtained from Yahoo! Finance BIBREF16 .
1610.09225
Introduction </s> Earlier Ġstudies Ġon Ġstock Ġmarket Ġprediction Ġare Ġbased Ġon Ġthe Ġhistorical Ġstock Ġprices . ĠLater Ġstudies Ġhave Ġdebunked Ġthe Ġapproach Ġof Ġpredicting Ġstock Ġmarket Ġmovements Ġusing Ġhistorical Ġprices . ĠStock Ġmarket Ġprices Ġare Ġlargely Ġfluct uating . ĠThe Ġefficient Ġmarket Ġhypothesis Ġ( EM H ) Ġstates Ġthat Ġfinancial Ġmarket Ġmovements Ġdepend Ġon Ġnews , Ġcurrent Ġevents Ġand Ġproduct Ġreleases Ġand Ġall Ġthese Ġfactors Ġwill Ġhave Ġa Ġsignificant Ġimpact Ġon Ġa Ġcompany 's Ġstock Ġvalue ĠB IB REF 0 Ġ. ĠBecause Ġof Ġthe Ġlying Ġunpredict ability Ġin Ġnews Ġand Ġcurrent Ġevents , Ġstock Ġmarket Ġprices Ġfollow Ġa Ġrandom Ġwalk Ġpattern Ġand Ġcannot Ġbe Ġpredicted Ġwith Ġmore Ġthan Ġ50 % Ġaccuracy ĠB IB REF 1 Ġ. </s> With Ġthe Ġadvent Ġof Ġsocial Ġmedia , Ġthe Ġinformation Ġabout Ġpublic Ġfeelings Ġhas Ġbecome Ġabundant . ĠSocial Ġmedia Ġis Ġtransforming Ġlike Ġa Ġperfect Ġplatform Ġto Ġshare Ġpublic Ġemotions Ġabout Ġany Ġtopic Ġand Ġhas Ġa Ġsignificant Ġimpact Ġon Ġoverall Ġpublic Ġopinion . ĠTwitter , Ġa Ġsocial Ġmedia Ġplatform , Ġhas Ġreceived Ġa Ġlot Ġof Ġattention Ġfrom Ġresearchers Ġin Ġthe Ġrecent Ġtimes . ĠTwitter Ġis Ġa Ġmicro - blog ging Ġapplication Ġthat Ġallows Ġusers Ġto Ġfollow Ġand Ġcomment Ġother Ġusers Ġthoughts Ġor Ġshare Ġtheir Ġopinions Ġin Ġreal Ġtime ĠB IB REF 2 Ġ. ĠMore Ġthan Ġmillion Ġusers Ġpost Ġover Ġ140 Ġmillion Ġtweets Ġevery Ġday . ĠThis Ġsituation Ġmakes ĠTwitter Ġlike Ġa Ġcorpus Ġwith Ġvaluable Ġdata Ġfor Ġresearchers ĠB IB REF 3 Ġ. Each Ġtweet Ġis Ġof Ġ140 Ġcharacters Ġlong Ġand Ġspeaks Ġpublic Ġopinion Ġon Ġa Ġtopic Ġconcise ly . ĠThe Ġinformation Ġexploited Ġfrom Ġtweets Ġare Ġvery Ġuseful Ġfor Ġmaking Ġpredictions ĠB IB REF 4 Ġ. </s> In Ġthis Ġpaper , Ġwe Ġcontribute Ġto Ġthe Ġfield Ġof Ġsentiment Ġanalysis Ġof Ġtwitter Ġdata . ĠSent iment Ġclassification Ġis Ġthe Ġtask Ġof Ġjudging Ġopinion Ġin Ġa Ġpiece Ġof Ġtext Ġas Ġpositive , Ġnegative Ġor Ġneutral . </s> There Ġare Ġmany Ġstudies Ġinvolving Ġtwitter Ġas Ġa Ġmajor Ġsource Ġfor Ġpublic - op inion Ġanalysis . ĠAs ur Ġand ĠHu berman ĠB IB REF 5 Ġhave Ġpredicted Ġbox Ġoffice Ġcollections Ġfor Ġa Ġmovie Ġprior Ġto Ġits Ġrelease Ġbased Ġon Ġpublic Ġsentiment Ġrelated Ġto Ġmovies , Ġas Ġexpressed Ġon ĠTwitter . ĠGoogle Ġflu Ġtrends Ġare Ġbeing Ġwidely Ġstudied Ġalong Ġwith Ġtwitter Ġfor Ġearly Ġprediction Ġof Ġdisease Ġoutbreaks . ĠE iji Ġet Ġal . ĠB IB REF 6 Ġhave Ġstudied Ġthe Ġtwitter Ġdata Ġfor Ġcatching Ġthe Ġflu Ġoutbreaks . ĠRu iz Ġet Ġal . ĠB IB REF 7 Ġhave Ġused Ġtime - con str ained Ġgraphs Ġto Ġstudy Ġthe Ġproblem Ġof Ġcorrel ating Ġthe ĠTwitter Ġmicro - blog ging Ġactivity Ġwith Ġchanges Ġin Ġstock Ġprices Ġand Ġtrading Ġvolumes . ĠBord ino Ġet Ġal . ĠB IB REF 8 Ġhave Ġshown Ġthat Ġtrading Ġvolumes Ġof Ġstocks Ġtraded Ġin ĠNAS DAQ - 100 Ġare Ġcorrelated Ġwith Ġtheir Ġquery Ġvolumes Ġ( i . e ., Ġthe Ġnumber Ġof Ġusers Ġrequests Ġsubmitted Ġto Ġsearch Ġengines Ġon Ġthe ĠInternet ). ĠGilbert Ġand ĠKar ah al ios ĠB IB REF 9 Ġhave Ġfound Ġout Ġthat Ġincreases Ġin Ġexpressions Ġof Ġanxiety , Ġworry Ġand Ġfear Ġin Ġwe blogs Ġpredict Ġdownward Ġpressure Ġon Ġthe ĠS & P Ġ500 Ġindex . ĠB ollen ĠB IB REF 10 Ġshowed Ġthat Ġpublic Ġmood Ġanalyzed Ġthrough Ġtwitter Ġfeeds Ġis Ġwell Ġcorrelated Ġwith ĠDow ĠJones ĠIndustrial ĠAverage Ġ( DJ IA ). ĠAll Ġthese Ġstudies Ġshowcased Ġtwitter Ġas Ġa Ġvaluable Ġsource Ġand Ġa Ġpowerful Ġtool Ġfor Ġconducting Ġstudies Ġand Ġmaking Ġpredictions . </s> Rest Ġof Ġthe Ġpaper Ġis Ġorganized Ġas Ġfollows . ĠSection Ġ2 Ġdescribes Ġthe Ġrelated Ġworks Ġand ĠSection Ġ3 Ġdiscusses Ġthe Ġdata Ġportion Ġdemonstrating Ġthe Ġdata Ġcollection Ġand Ġpre - processing Ġpart . ĠIn ĠSection Ġ4 Ġwe Ġdiscuss Ġthe Ġsentiment Ġanalysis Ġpart Ġin Ġour Ġwork Ġfollowed Ġby ĠSection Ġ5 Ġwhich Ġexamines Ġthe Ġcorrelation Ġpart Ġof Ġextracted Ġsentiment Ġwith Ġstocks . ĠIn ĠSection Ġ6 Ġwe Ġpresent Ġthe Ġresults , Ġaccuracy Ġand Ġprecision Ġof Ġour Ġsentiment Ġanaly zer Ġfollowed Ġby Ġthe Ġaccuracy Ġof Ġcorrelation Ġanaly zer . ĠIn ĠSection Ġ7 Ġwe Ġpresent Ġour Ġconclusions Ġand ĠSection Ġ8 Ġdeals Ġwith Ġour Ġfuture Ġwork Ġplan . </s> Related ĠWork </s> The Ġmost Ġwell - known Ġpublication Ġin Ġthis Ġarea Ġis Ġby ĠB ollen ĠB IB REF 10 Ġ. ĠThey Ġinvestigated Ġwhether Ġthe Ġcollective Ġmood Ġstates Ġof Ġpublic Ġ( Happy , Ġcalm , ĠAnxiety ) Ġderived Ġfrom Ġtwitter Ġfeeds Ġare Ġcorrelated Ġto Ġthe Ġvalue Ġof Ġthe ĠDow ĠJones ĠIndustrial ĠIndex . ĠThey Ġused Ġa ĠF uzz y Ġneural Ġnetwork Ġfor Ġtheir Ġprediction . ĠTheir Ġresults Ġshow Ġthat Ġpublic Ġmood Ġstates Ġin Ġtwitter Ġare Ġstrongly Ġcorrelated Ġwith ĠDow ĠJones ĠIndustrial ĠIndex . ĠChen Ġand ĠL azer ĠB IB REF 11 Ġderived Ġinvestment Ġstrategies Ġby Ġobserving Ġand Ġclass ifying Ġthe Ġtwitter Ġfeeds . ĠBing Ġet Ġal . ĠB IB REF 12 Ġstudied Ġthe Ġtweets Ġand Ġconcluded Ġthe Ġpredict ability Ġof Ġstock Ġprices Ġbased Ġon Ġthe Ġtype Ġof Ġindustry Ġlike ĠFinance , ĠIT Ġetc . ĠZhang ĠB IB REF 13 Ġfound Ġout Ġa Ġhigh Ġnegative Ġcorrelation Ġbetween Ġmood Ġstates Ġlike Ġhope , Ġfear Ġand Ġworry Ġin Ġtweets Ġwith Ġthe ĠDow ĠJones ĠAverage ĠIndex . ĠRecently , ĠBrian Ġet Ġal . ĠB IB REF 14 Ġinvestigated Ġthe Ġcorrelation Ġof Ġsentiments Ġof Ġpublic Ġwith Ġstock Ġincrease Ġand Ġdecreases Ġusing ĠPearson Ġcorrelation Ġcoefficient Ġfor Ġstocks . ĠIn Ġthis Ġpaper , Ġwe Ġtook Ġa Ġnovel Ġapproach Ġof Ġpredicting Ġrise Ġand Ġfall Ġin Ġstock Ġprices Ġbased Ġon Ġthe Ġsentiments Ġextracted Ġfrom Ġtwitter Ġto Ġfind Ġthe Ġcorrelation . ĠThe Ġcore Ġcontribution Ġof Ġour Ġwork Ġis Ġthe Ġdevelopment Ġof Ġa Ġsentiment Ġanaly zer Ġwhich Ġworks Ġbetter Ġthan Ġthe Ġone Ġin ĠBrian 's Ġwork Ġand Ġa Ġnovel Ġapproach Ġto Ġfind Ġthe Ġcorrelation . ĠSent iment Ġanaly zer Ġis Ġused Ġto Ġclassify Ġthe Ġsentiments Ġin Ġtweets Ġextracted . The Ġhuman Ġannot ated Ġdataset Ġin Ġour Ġwork Ġis Ġalso Ġexhaustive . ĠWe Ġhave Ġshown Ġthat Ġa Ġstrong Ġcorrelation Ġexists Ġbetween Ġtwitter Ġsentiments Ġand Ġthe Ġnext Ġday Ġstock Ġprices Ġin Ġthe Ġresults Ġsection . ĠWe Ġdid Ġso Ġby Ġconsidering Ġthe Ġtweets Ġand Ġstock Ġopening Ġand Ġclosing Ġprices Ġof ĠMicrosoft Ġover Ġa Ġyear . </s> Data ĠCollection </s> A Ġtotal Ġof Ġ2 , 50 , 000 Ġtweets Ġover Ġa Ġperiod Ġof ĠAugust Ġ31 st , Ġ2015 Ġto ĠAugust Ġ25 th , 2016 Ġon ĠMicrosoft Ġare Ġextracted Ġfrom Ġtwitter ĠAPI ĠB IB REF 15 Ġ. ĠTwitter 4 J Ġis Ġa Ġjava Ġapplication Ġwhich Ġhelps Ġus Ġto Ġextract Ġtweets Ġfrom Ġtwitter . ĠThe Ġtweets Ġwere Ġcollected Ġusing ĠTwitter ĠAPI Ġand Ġfiltered Ġusing Ġkeywords Ġlike Ġ$ ĠMS FT , Ġ# ĠMicrosoft , Ġ# Windows Ġetc . ĠNot Ġonly Ġthe Ġopinion Ġof Ġpublic Ġabout Ġthe Ġcompany 's Ġstock Ġbut Ġalso Ġthe Ġopinions Ġabout Ġproducts Ġand Ġservices Ġoffered Ġby Ġthe Ġcompany Ġwould Ġhave Ġa Ġsignificant Ġimpact Ġand Ġare Ġworth Ġstudying . ĠBased Ġon Ġthis Ġprinciple , Ġthe Ġkeywords Ġused Ġfor Ġfiltering Ġare Ġdevised Ġwith Ġextensive Ġcare Ġand Ġtweets Ġare Ġextracted Ġin Ġsuch Ġa Ġway Ġthat Ġthey Ġrepresent Ġthe Ġexact Ġemotions Ġof Ġpublic Ġabout ĠMicrosoft Ġover Ġa Ġperiod Ġof Ġtime . ĠThe Ġnews Ġon Ġtwitter Ġabout ĠMicrosoft Ġand Ġtweets Ġregarding Ġthe Ġproduct Ġreleases Ġwere Ġalso Ġincluded . ĠStock Ġopening Ġand Ġclosing Ġprices Ġof ĠMicrosoft Ġfrom ĠAugust Ġ31 st , Ġ2015 Ġto ĠAugust Ġ25 th , Ġ2016 Ġare Ġobtained Ġfrom ĠYahoo ! ĠFinance ĠB IB REF 16 Ġ. </s> Data ĠPre - Process ing </s> Stock Ġprices Ġdata Ġcollected Ġis Ġnot Ġcomplete Ġunderstandably Ġbecause Ġof Ġweekends Ġand Ġpublic Ġholidays Ġwhen Ġthe Ġstock Ġmarket Ġdoes Ġnot Ġfunction . ĠThe Ġmissing Ġdata Ġis Ġapprox imated Ġusing Ġa Ġsimple Ġtechnique Ġby ĠGo el ĠB IB REF 17 Ġ. ĠStock Ġdata Ġusually Ġfollows Ġa Ġconc ave Ġfunction . ĠSo , Ġif Ġthe Ġstock Ġvalue Ġon Ġa Ġday Ġis Ġx Ġand Ġthe Ġnext Ġvalue Ġpresent Ġis Ġy Ġwith Ġsome Ġmissing Ġin Ġbetween . ĠThe Ġfirst Ġmissing Ġvalue Ġis Ġapprox imated Ġto Ġbe Ġ( y + x )/ 2 Ġand Ġthe Ġsame Ġmethod Ġis Ġfollowed Ġto Ġfill Ġall Ġthe Ġgaps . </s> Twe ets Ġconsists Ġof Ġmany Ġacron y ms , Ġemot icons Ġand Ġunnecessary Ġdata Ġlike Ġpictures Ġand ĠURL 's . ĠSo Ġtweets Ġare Ġpre process ed Ġto Ġrepresent Ġcorrect Ġemotions Ġof Ġpublic . ĠFor Ġpre processing Ġof Ġtweets Ġwe Ġemployed Ġthree Ġstages Ġof Ġfiltering : ĠToken ization , ĠStop words Ġremoval Ġand Ġregex Ġmatching Ġfor Ġremoving Ġspecial Ġcharacters . </s> Twe ets Ġare Ġsplit Ġinto Ġindividual Ġwords Ġbased Ġon Ġthe Ġspace Ġand Ġirrelevant Ġsymbols Ġlike Ġemot icons Ġare Ġremoved . ĠWe Ġform Ġa Ġlist Ġof Ġindividual Ġwords Ġfor Ġeach Ġtweet . </s> Words Ġthat Ġdo Ġnot Ġexpress Ġany Ġemotion Ġare Ġcalled ĠStop words . ĠAfter Ġsplitting Ġa Ġtweet , Ġwords Ġlike Ġa , is , Ġthe , Ġwith Ġetc . Ġare Ġremoved Ġfrom Ġthe Ġlist Ġof Ġwords . </s> Re gex Ġmatching Ġin ĠPython Ġis Ġperformed Ġto Ġmatch ĠURL âĢ Ļ s Ġand Ġare Ġreplaced Ġby Ġthe Ġterm ĠURL . ĠOften Ġtweets Ġconsists Ġof Ġhasht ags ( # ) Ġand Ġ@ Ġaddressing Ġother Ġusers . ĠThey Ġare Ġalso Ġreplaced Ġsuit ably . ĠFor Ġexample , Ġ# Microsoft Ġis Ġreplaced Ġwith ĠMicrosoft Ġand Ġ@ Bill g ates Ġis Ġreplaced Ġwith ĠUS ER . ĠPro long ed Ġword Ġshowing Ġintense Ġemotions Ġlike Ġco oooo oo ool ! Ġis Ġreplaced Ġwith Ġcool ! ĠAfter Ġthese Ġstages Ġthe Ġtweets Ġare Ġready Ġfor Ġsentiment Ġclassification . </s> Sent iment ĠAnalysis </s> Sent iment Ġanalysis Ġtask Ġis Ġvery Ġmuch Ġfield Ġspecific . ĠThere Ġis Ġlot Ġof Ġresearch Ġon Ġsentiment Ġanalysis Ġof Ġmovie Ġreviews Ġand Ġnews Ġarticles Ġand Ġmany Ġsentiment Ġanaly zers Ġare Ġavailable Ġas Ġan Ġopen Ġsource . ĠThe Ġmain Ġproblem Ġwith Ġthese Ġanaly zers Ġis Ġthat Ġthey Ġare Ġtrained Ġwith Ġa Ġdifferent Ġcorpus . ĠFor Ġinstance , ĠMovie Ġcorpus Ġand Ġstock Ġcorpus Ġare Ġnot Ġequivalent . ĠSo , Ġwe Ġdeveloped Ġour Ġown Ġsentiment Ġanaly zer . </s> Twe ets Ġare Ġclassified Ġas Ġpositive , Ġnegative Ġand Ġneutral Ġbased Ġon Ġthe Ġsentiment Ġpresent ĠB IB REF 18 Ġ. Ġ3 , 216 Ġtweets Ġout Ġof Ġthe Ġtotal Ġtweets Ġare Ġexamined Ġby Ġhumans Ġand Ġannot ated Ġas Ġ1 Ġfor ĠPositive , Ġ0 Ġfor ĠNeutral Ġand Ġ2 Ġfor ĠNegative Ġemotions . ĠFor Ġclassification Ġof Ġnon human Ġannot ated Ġtweets Ġa Ġmachine Ġlearning Ġmodel Ġis Ġtrained Ġwhose Ġfeatures Ġare Ġextracted Ġfrom Ġthe Ġhuman Ġannot ated Ġtweets . </s> Feature ĠExt raction </s> Text ual Ġrepresentations Ġare Ġdone Ġusing Ġtwo Ġmethods : n - gram s Ġand ĠWord 2 vec </s> N - gram Ġrepresentation Ġis Ġknown Ġfor Ġits Ġspecificity Ġto Ġmatch Ġthe Ġcorpus Ġof Ġtext Ġbeing Ġstudied . ĠIn Ġthese Ġtechniques Ġa Ġfull Ġcorpus Ġof Ġrelated Ġtext Ġis Ġparsed Ġwhich Ġare Ġtweets Ġin Ġthe Ġpresent Ġwork , Ġand Ġevery Ġappearing Ġword Ġsequence Ġof Ġlength Ġn Ġis Ġextracted Ġfrom Ġthe Ġtweets Ġto Ġform Ġa Ġdictionary Ġof Ġwords Ġand Ġphrases . ĠFor Ġexample Ġthe Ġtext ĠâĢ ľ Microsoft Ġis Ġlaunching Ġa Ġnew Ġproduct " Ġhas Ġthe Ġfollowing Ġ3 - gram Ġword Ġfeatures : âĢ ľ Microsoft Ġis Ġlaunching ", ĠâĢ ľ is Ġlaunching Ġa ", ĠâĢ ľ laun ching Ġa Ġnew " Ġand ĠâĢ ľ a Ġnew Ġproduct ". ĠIn Ġour Ġcase , ĠN - gram s Ġfor Ġall Ġthe Ġtweets Ġform Ġthe Ġcorpus . ĠIn Ġthis Ġrepresentation , Ġtweet Ġis Ġsplit Ġinto ĠN - gram s Ġand Ġthe Ġfeatures Ġto Ġthe Ġmodel Ġare Ġa Ġstring Ġof Ġ1 âĢ Ļ s Ġand Ġ0 âĢ Ļ s Ġwhere Ġ1 Ġrepresents Ġthe Ġpresence Ġof Ġthat ĠN - gram Ġof Ġthe Ġtweet Ġin Ġthe Ġcorpus Ġand Ġa Ġ0 Ġindicates Ġthe Ġabsence . </s> Word 2 vec Ġrepresentation Ġis Ġfar Ġbetter , Ġadvanced Ġand Ġa Ġrecent Ġtechnique Ġwhich Ġfunctions Ġby Ġmapping Ġwords Ġto Ġa Ġ300 Ġdimensional Ġvector Ġrepresentations . ĠOnce Ġevery Ġword Ġof Ġthe Ġlanguage Ġhas Ġbeen Ġmapped Ġto Ġa Ġunique Ġvector , Ġvectors Ġof Ġwords Ġcan Ġbe Ġsummed Ġup Ġyielding Ġa Ġresultant Ġvector Ġfor Ġany Ġgiven Ġcollection Ġof Ġwords ĠB IB REF 19 Ġ. ĠRelationship Ġbetween Ġthe Ġwords Ġis Ġexactly Ġretained Ġin Ġthis Ġform Ġof Ġrepresentation . ĠWord Ġvectors Ġdifference Ġbetween ĠRome Ġand ĠItaly Ġis Ġvery Ġclose Ġto Ġthe Ġdifference Ġbetween Ġvectors Ġof ĠFrance Ġand ĠParis ĠThis Ġsustained Ġrelationship Ġbetween Ġword Ġconcepts Ġmakes Ġword 2 vec Ġmodel Ġvery Ġattractive Ġfor Ġtextual Ġanalysis . ĠIn Ġthis Ġrepresentation , Ġresultant Ġvector Ġwhich Ġis Ġsum Ġof Ġ300 Ġdimensional Ġvectors Ġof Ġall Ġwords Ġin Ġa Ġtweet Ġacts Ġas Ġfeatures Ġto Ġthe Ġmodel . </s> Model ĠTraining </s> The Ġfeatures Ġextracted Ġusing Ġthe Ġabove Ġmethods Ġfor Ġthe Ġhuman Ġannot ated Ġtweets Ġare Ġfed Ġto Ġthe Ġclass ifier Ġand Ġtrained Ġusing Ġrandom Ġforest Ġalgorithm . ĠBoth Ġthe Ġtextual Ġrepresentations Ġperformed Ġwell Ġand Ġthe Ġresults Ġare Ġcomparable . ĠOut Ġof Ġthe Ġtwo , Ġmodel Ġtrained Ġwith Ġword 2 vec Ġrepresentation Ġis Ġpicked Ġbecause Ġof Ġits Ġsustainability Ġof Ġmeaning Ġand Ġpromising Ġperformance Ġover Ġlarge Ġdatasets . ĠThe Ġresults Ġof Ġsentiment Ġclassification Ġare Ġdiscussed Ġin Ġthe Ġfollowing Ġsections . ĠThe Ġdevised Ġclass ifier Ġis Ġused Ġto Ġpredict Ġthe Ġemotions Ġof Ġnon - human Ġannot ated Ġtweets . ĠTable - 1 Ġshows Ġa Ġsample Ġof Ġannot ated Ġtweets Ġby Ġthe Ġsentiment Ġanaly zer . </s> Cor relation ĠAnalysis Ġof ĠPrice Ġand ĠSent iment Ġ </s> The Ġstock Ġprice Ġdata Ġof ĠMicrosoft Ġare Ġlabeled Ġsuit ably Ġfor Ġtraining Ġusing Ġa Ġsimple Ġprogram . ĠIf Ġthe Ġprevious Ġday Ġstock Ġprice Ġis Ġmore Ġthan Ġthe Ġcurrent Ġday Ġstock Ġprice , Ġthe Ġcurrent Ġday Ġis Ġmarked Ġwith Ġa Ġnumeric Ġvalue Ġof Ġ0 , Ġelse Ġmarked Ġwith Ġa Ġnumeric Ġvalue Ġof Ġ1 . ĠNow , Ġthis Ġcorrelation Ġanalysis Ġturns Ġout Ġto Ġbe Ġa Ġclassification Ġproblem . ĠThe Ġtotal Ġpositive , Ġnegative Ġand Ġneutral Ġemotions Ġin Ġtweets Ġin Ġa Ġ3 Ġday Ġperiod Ġare Ġcalculated Ġsuccess ively Ġwhich Ġare Ġused Ġas Ġfeatures Ġfor Ġthe Ġclass ifier Ġmodel Ġand Ġthe Ġoutput Ġis Ġthe Ġlabeled Ġnext Ġday Ġvalue Ġof Ġstock Ġ0 Ġor Ġ1 . The Ġwindow Ġsize Ġis Ġexperimented Ġand Ġbest Ġresults Ġare Ġachieved Ġwhen Ġthe Ġsentiment Ġvalues Ġpreced e Ġ3 Ġdays Ġto Ġthe Ġstock Ġprice . ĠA Ġtotal Ġof Ġ355 Ġinstances , Ġeach Ġwith Ġ3 Ġattributes Ġare Ġfed Ġto Ġthe Ġclass ifier Ġwith Ġa Ġsplit Ġproportions Ġof Ġ80 % Ġtrain Ġdataset Ġand Ġthe Ġremaining Ġdataset Ġfor Ġtesting . ĠThe Ġaccuracy Ġof Ġthe Ġclass ifier Ġis Ġdiscussed Ġin Ġthe Ġresults Ġsection . </s> Results Ġand ĠDiscussion </s> This Ġsection Ġgives Ġan Ġoverview Ġof Ġaccuracy Ġrates Ġof Ġthe Ġtrained Ġclass ifiers . ĠAll Ġthe Ġcalculations Ġare Ġdone Ġin ĠWe ka Ġtool Ġwhich Ġruns Ġon Ġjava Ġvirtual Ġmachine ĠB IB REF 20 </s> Sent iment ĠAnaly zer ĠResults </s> The Ġabove Ġsections Ġdiscussed Ġthe Ġmethod Ġfollowed Ġto Ġtrain Ġthe Ġclass ifier Ġused Ġfor Ġsentiment Ġanalysis Ġof Ġtweets . ĠThe Ġclass ifier Ġwith Ġfeatures Ġas ĠWord 2 vec Ġrepresentations Ġof Ġhuman Ġannot ated Ġtweets Ġtrained Ġon ĠRandom ĠForest Ġalgorithm Ġwith Ġa Ġsplit Ġpercentage Ġof Ġ90 Ġfor Ġtraining Ġthe Ġmodel Ġand Ġremaining Ġfor Ġtesting Ġthe Ġmodel Ġshowed Ġan Ġaccuracy Ġof Ġ70 . 2 %. ĠWith ĠN - gram Ġrepresentations , Ġthe Ġclass ifier Ġmodel Ġwith Ġsame Ġalgorithm Ġand Ġwith Ġsame Ġdataset Ġshowed Ġan Ġaccuracy Ġof Ġ70 . 5 %. ĠThough Ġthe Ġresults Ġare Ġvery Ġclose , Ġmodel Ġtrained Ġwith Ġword 2 vec Ġrepresentations Ġis Ġpicked Ġto Ġclassify Ġthe Ġnon human Ġannot ated Ġtweets Ġbecause Ġof Ġits Ġpromising Ġaccuracy Ġfor Ġlarge Ġdatasets Ġand Ġthe Ġsustainability Ġin Ġword Ġmeaning . ĠNumerous Ġstudies Ġhave Ġbeen Ġconducted Ġon Ġpeople Ġand Ġthey Ġconcluded Ġthat Ġthe Ġrate Ġof Ġhuman Ġconc ord ance , Ġthat Ġis Ġthe Ġdegree Ġof Ġagreement Ġamong Ġhumans Ġon Ġthe Ġsentiment Ġof Ġa Ġtext , Ġis Ġbetween Ġ70 % Ġand Ġ79 % ĠB IB REF 21 Ġ. ĠThey Ġhave Ġalso Ġsynthes ized Ġthat Ġsentiment Ġanaly zers Ġabove Ġ70 % Ġare Ġvery Ġaccurate Ġin Ġmost Ġof Ġthe Ġcases . ĠProvided Ġthis Ġinformation , Ġthe Ġresults Ġwe Ġobtained Ġfrom Ġthe Ġsentiment Ġclassification Ġcan Ġbe Ġobserved Ġas Ġvery Ġgood Ġfigures Ġwhile Ġpredicting Ġthe Ġsentiments Ġin Ġshort Ġtexts , Ġtweets , Ġless Ġthan Ġ140 Ġcharacters Ġin Ġlength . ĠTable - 2 Ġdepicts Ġthe Ġresults Ġof Ġsentiment Ġclassification Ġincluding Ġaccuracy , Ġprecision , ĠF - me asure Ġand Ġrecall Ġwhen Ġtrained Ġwith Ġdifferent Ġmachine Ġlearning Ġalgorithms . ĠR OC Ġcurves Ġare Ġplotted Ġfor Ġdetailed Ġanalysis . </s> Stock ĠPrice Ġand ĠSent iment ĠCor relation ĠResults </s> A Ġclass ifier Ġis Ġpresented Ġin Ġthe Ġprevious Ġsections Ġthat Ġis Ġtrained Ġwith Ġaggregate Ġsentiment Ġvalues Ġfor Ġ3 - day Ġperiod Ġas Ġfeatures Ġand Ġthe Ġincrease / dec re ase Ġin Ġstock Ġprice Ġrepresented Ġby Ġ1 / 0 Ġas Ġthe Ġoutput . ĠTotal Ġdata Ġis Ġsplit Ġinto Ġtwo Ġparts , Ġ80 Ġpercent Ġto Ġtrain Ġthe Ġmodel Ġand Ġremaining Ġfor Ġtesting Ġoperations . ĠThe Ġclass ifier Ġresults Ġshow Ġan Ġaccuracy Ġvalue Ġof Ġ69 . 01 % Ġwhen Ġtrained Ġusing ĠLog istic Ġregression Ġalgorithm Ġand Ġthe Ġaccuracy Ġrate Ġvaried Ġwith Ġthe Ġtraining Ġset . ĠWhen Ġthe Ġmodel Ġwith ĠLib S VM Ġis Ġtrained Ġwith Ġ90 Ġpercent Ġof Ġdata , Ġit Ġgave Ġa Ġresult Ġof Ġ71 . 82 %. ĠThese Ġresults Ġgive Ġa Ġsignificant Ġedge Ġto Ġthe Ġinvestors Ġand Ġthey Ġshow Ġgood Ġcorrelation Ġbetween Ġstock Ġmarket Ġmovements Ġand Ġthe Ġsentiments Ġof Ġpublic Ġexpressed Ġin Ġtwitter . ĠThis Ġtrend Ġshows Ġthat Ġwith Ġincreasing Ġdataset Ġthe Ġmodels Ġare Ġperforming Ġwell . ĠWe Ġwould Ġlike Ġto Ġincorporate Ġmore Ġdata Ġin Ġour Ġfuture Ġwork . </s> Conclusion </s> In Ġthis Ġpaper , Ġwe Ġhave Ġshown Ġthat Ġa Ġstrong Ġcorrelation Ġexists Ġbetween Ġrise / fall Ġin Ġstock Ġprices Ġof Ġa Ġcompany Ġto Ġthe Ġpublic Ġopinions Ġor Ġemotions Ġabout Ġthat Ġcompany Ġexpressed Ġon Ġtwitter Ġthrough Ġtweets . ĠThe Ġmain Ġcontribution Ġof Ġour Ġwork Ġis Ġthe Ġdevelopment Ġof Ġa Ġsentiment Ġanaly zer Ġthat Ġcan Ġjudge Ġthe Ġtype Ġof Ġsentiment Ġpresent Ġin Ġthe Ġtweet . ĠThe Ġtweets Ġare Ġclassified Ġinto Ġthree Ġcategories : Ġpositive , Ġnegative Ġand Ġneutral . ĠAt Ġthe Ġbeginning , Ġwe Ġclaimed Ġthat Ġpositive Ġemotions Ġor Ġsentiment Ġof Ġpublic Ġin Ġtwitter Ġabout Ġa Ġcompany Ġwould Ġreflect Ġin Ġits Ġstock Ġprice . ĠOur Ġspeculation Ġis Ġwell Ġsupported Ġby Ġthe Ġresults Ġachieved Ġand Ġseems Ġto Ġhave Ġa Ġpromising Ġfuture Ġin Ġresearch . </s> Future ĠWork </s> In Ġthis Ġwork , Ġwe Ġhave Ġconsidered Ġonly Ġtwitter Ġdata Ġfor Ġanalyzing Ġpeople 's Ġsentiment Ġwhich Ġmay Ġbe Ġbiased Ġbecause Ġnot Ġall Ġthe Ġpeople Ġwho Ġtrade Ġin Ġstocks Ġshare Ġtheir Ġopinions Ġon Ġtwitter . ĠStock tw its ĠB IB REF 22 Ġis Ġa Ġfinancial Ġcommunication Ġplatform Ġdesigned Ġsolely Ġfor Ġsharing Ġideas Ġand Ġinsights Ġof Ġinvestors , Ġentrepreneurs Ġand Ġtraders . ĠThe Ġcurrent Ġstudy Ġcan Ġbe Ġextended Ġby Ġincorporating Ġstock tw its Ġdata . ĠIn Ġaddition Ġto Ġthis , Ġdata Ġfrom Ġnews Ġcan Ġalso Ġbe Ġincluded Ġfor Ġan Ġexhaustive Ġpublic Ġopinion Ġcollection . </s> While Ġtraining Ġthe Ġsentiment Ġanaly zer , Ġ3 , 216 Ġtweets Ġare Ġused Ġwhich Ġis Ġcomparatively Ġa Ġless Ġnumber Ġto Ġtrain Ġa Ġsentiment Ġanaly zer . ĠIn Ġfuture , Ġwe Ġlook Ġforward Ġto Ġhuman Ġannot ate Ġmore Ġthan Ġ10 , 000 Ġtweets Ġand Ġtrain Ġthe Ġclass ifiers . ĠWith Ġincreasing Ġsize Ġof Ġtraining Ġdatasets , Ġthe Ġmodels Ġtend Ġto Ġperform Ġbetter . </s> Acknowled gment </s> The Ġauthors Ġwould Ġlike Ġto Ġthank Ġthe Ġstudents Ġof ĠI IT ĠBh ub anes war Ġwho Ġcontributed Ġto Ġthe Ġhuman Ġannotation Ġof Ġtweets .
Introduction Earlier studies on stock market prediction are based on the historical stock prices. Later studies have debunked the approach of predicting stock market movements using historical prices. Stock market prices are largely fluctuating. The efficient market hypothesis (EMH) states that financial market movements depend on news, current events and product releases and all these factors will have a significant impact on a company's stock value BIBREF0 . Because of the lying unpredictability in news and current events, stock market prices follow a random walk pattern and cannot be predicted with more than 50% accuracy BIBREF1 . With the advent of social media, the information about public feelings has become abundant. Social media is transforming like a perfect platform to share public emotions about any topic and has a significant impact on overall public opinion. Twitter, a social media platform, has received a lot of attention from researchers in the recent times. Twitter is a micro-blogging application that allows users to follow and comment other users thoughts or share their opinions in real time BIBREF2 . More than million users post over 140 million tweets every day. This situation makes Twitter like a corpus with valuable data for researchers BIBREF3 .Each tweet is of 140 characters long and speaks public opinion on a topic concisely. The information exploited from tweets are very useful for making predictions BIBREF4 . In this paper, we contribute to the field of sentiment analysis of twitter data. Sentiment classification is the task of judging opinion in a piece of text as positive, negative or neutral. There are many studies involving twitter as a major source for public-opinion analysis. Asur and Huberman BIBREF5 have predicted box office collections for a movie prior to its release based on public sentiment related to movies, as expressed on Twitter. Google flu trends are being widely studied along with twitter for early prediction of disease outbreaks. Eiji et al. BIBREF6 have studied the twitter data for catching the flu outbreaks. Ruiz et al. BIBREF7 have used time-constrained graphs to study the problem of correlating the Twitter micro-blogging activity with changes in stock prices and trading volumes. Bordino et al. BIBREF8 have shown that trading volumes of stocks traded in NASDAQ-100 are correlated with their query volumes (i.e., the number of users requests submitted to search engines on the Internet). Gilbert and Karahalios BIBREF9 have found out that increases in expressions of anxiety, worry and fear in weblogs predict downward pressure on the S&P 500 index. Bollen BIBREF10 showed that public mood analyzed through twitter feeds is well correlated with Dow Jones Industrial Average (DJIA). All these studies showcased twitter as a valuable source and a powerful tool for conducting studies and making predictions. Rest of the paper is organized as follows. Section 2 describes the related works and Section 3 discusses the data portion demonstrating the data collection and pre-processing part. In Section 4 we discuss the sentiment analysis part in our work followed by Section 5 which examines the correlation part of extracted sentiment with stocks. In Section 6 we present the results, accuracy and precision of our sentiment analyzer followed by the accuracy of correlation analyzer. In Section 7 we present our conclusions and Section 8 deals with our future work plan. Related Work The most well-known publication in this area is by Bollen BIBREF10 . They investigated whether the collective mood states of public (Happy, calm, Anxiety) derived from twitter feeds are correlated to the value of the Dow Jones Industrial Index. They used a Fuzzy neural network for their prediction. Their results show that public mood states in twitter are strongly correlated with Dow Jones Industrial Index. Chen and Lazer BIBREF11 derived investment strategies by observing and classifying the twitter feeds. Bing et al. BIBREF12 studied the tweets and concluded the predictability of stock prices based on the type of industry like Finance, IT etc. Zhang BIBREF13 found out a high negative correlation between mood states like hope, fear and worry in tweets with the Dow Jones Average Index. Recently, Brian et al. BIBREF14 investigated the correlation of sentiments of public with stock increase and decreases using Pearson correlation coefficient for stocks. In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation. Sentiment analyzer is used to classify the sentiments in tweets extracted.The human annotated dataset in our work is also exhaustive. We have shown that a strong correlation exists between twitter sentiments and the next day stock prices in the results section. We did so by considering the tweets and stock opening and closing prices of Microsoft over a year. Data Collection A total of 2,50,000 tweets over a period of August 31st, 2015 to August 25th,2016 on Microsoft are extracted from twitter API BIBREF15 . Twitter4J is a java application which helps us to extract tweets from twitter. The tweets were collected using Twitter API and filtered using keywords like $ MSFT, # Microsoft, #Windows etc. Not only the opinion of public about the company's stock but also the opinions about products and services offered by the company would have a significant impact and are worth studying. Based on this principle, the keywords used for filtering are devised with extensive care and tweets are extracted in such a way that they represent the exact emotions of public about Microsoft over a period of time. The news on twitter about Microsoft and tweets regarding the product releases were also included. Stock opening and closing prices of Microsoft from August 31st, 2015 to August 25th, 2016 are obtained from Yahoo! Finance BIBREF16 . Data Pre-Processing Stock prices data collected is not complete understandably because of weekends and public holidays when the stock market does not function. The missing data is approximated using a simple technique by Goel BIBREF17 . Stock data usually follows a concave function. So, if the stock value on a day is x and the next value present is y with some missing in between. The first missing value is approximated to be (y+x)/2 and the same method is followed to fill all the gaps. Tweets consists of many acronyms, emoticons and unnecessary data like pictures and URL's. So tweets are preprocessed to represent correct emotions of public. For preprocessing of tweets we employed three stages of filtering: Tokenization, Stopwords removal and regex matching for removing special characters. Tweets are split into individual words based on the space and irrelevant symbols like emoticons are removed. We form a list of individual words for each tweet. Words that do not express any emotion are called Stopwords. After splitting a tweet, words like a,is, the, with etc. are removed from the list of words. Regex matching in Python is performed to match URL’s and are replaced by the term URL. Often tweets consists of hashtags(#) and @ addressing other users. They are also replaced suitably. For example, #Microsoft is replaced with Microsoft and @Billgates is replaced with USER. Prolonged word showing intense emotions like coooooooool! is replaced with cool! After these stages the tweets are ready for sentiment classification. Sentiment Analysis Sentiment analysis task is very much field specific. There is lot of research on sentiment analysis of movie reviews and news articles and many sentiment analyzers are available as an open source. The main problem with these analyzers is that they are trained with a different corpus. For instance, Movie corpus and stock corpus are not equivalent. So, we developed our own sentiment analyzer. Tweets are classified as positive, negative and neutral based on the sentiment present BIBREF18 . 3,216 tweets out of the total tweets are examined by humans and annotated as 1 for Positive, 0 for Neutral and 2 for Negative emotions. For classification of nonhuman annotated tweets a machine learning model is trained whose features are extracted from the human annotated tweets. Feature Extraction Textual representations are done using two methods:n-grams and Word2vec N-gram representation is known for its specificity to match the corpus of text being studied. In these techniques a full corpus of related text is parsed which are tweets in the present work, and every appearing word sequence of length n is extracted from the tweets to form a dictionary of words and phrases. For example the text “Microsoft is launching a new product" has the following 3-gram word features:“Microsoft is launching", “is launching a", “launching a new" and “a new product". In our case, N-grams for all the tweets form the corpus. In this representation, tweet is split into N-grams and the features to the model are a string of 1’s and 0’s where 1 represents the presence of that N-gram of the tweet in the corpus and a 0 indicates the absence. Word2vec representation is far better, advanced and a recent technique which functions by mapping words to a 300 dimensional vector representations. Once every word of the language has been mapped to a unique vector, vectors of words can be summed up yielding a resultant vector for any given collection of words BIBREF19 . Relationship between the words is exactly retained in this form of representation. Word vectors difference between Rome and Italy is very close to the difference between vectors of France and Paris This sustained relationship between word concepts makes word2vec model very attractive for textual analysis. In this representation, resultant vector which is sum of 300 dimensional vectors of all words in a tweet acts as features to the model. Model Training The features extracted using the above methods for the human annotated tweets are fed to the classifier and trained using random forest algorithm. Both the textual representations performed well and the results are comparable. Out of the two, model trained with word2vec representation is picked because of its sustainability of meaning and promising performance over large datasets. The results of sentiment classification are discussed in the following sections. The devised classifier is used to predict the emotions of non-human annotated tweets. Table-1 shows a sample of annotated tweets by the sentiment analyzer. Correlation Analysis of Price and Sentiment  The stock price data of Microsoft are labeled suitably for training using a simple program. If the previous day stock price is more than the current day stock price, the current day is marked with a numeric value of 0, else marked with a numeric value of 1. Now, this correlation analysis turns out to be a classification problem. The total positive, negative and neutral emotions in tweets in a 3 day period are calculated successively which are used as features for the classifier model and the output is the labeled next day value of stock 0 or 1.The window size is experimented and best results are achieved when the sentiment values precede 3 days to the stock price. A total of 355 instances, each with 3 attributes are fed to the classifier with a split proportions of 80% train dataset and the remaining dataset for testing. The accuracy of the classifier is discussed in the results section. Results and Discussion This section gives an overview of accuracy rates of the trained classifiers. All the calculations are done in Weka tool which runs on java virtual machine BIBREF20 Sentiment Analyzer Results The above sections discussed the method followed to train the classifier used for sentiment analysis of tweets. The classifier with features as Word2vec representations of human annotated tweets trained on Random Forest algorithm with a split percentage of 90 for training the model and remaining for testing the model showed an accuracy of 70.2%. With N-gram representations, the classifier model with same algorithm and with same dataset showed an accuracy of 70.5%. Though the results are very close, model trained with word2vec representations is picked to classify the nonhuman annotated tweets because of its promising accuracy for large datasets and the sustainability in word meaning. Numerous studies have been conducted on people and they concluded that the rate of human concordance, that is the degree of agreement among humans on the sentiment of a text, is between 70% and 79% BIBREF21 . They have also synthesized that sentiment analyzers above 70% are very accurate in most of the cases. Provided this information, the results we obtained from the sentiment classification can be observed as very good figures while predicting the sentiments in short texts, tweets, less than 140 characters in length. Table-2 depicts the results of sentiment classification including accuracy, precision, F-measure and recall when trained with different machine learning algorithms. ROC curves are plotted for detailed analysis. Stock Price and Sentiment Correlation Results A classifier is presented in the previous sections that is trained with aggregate sentiment values for 3-day period as features and the increase/decrease in stock price represented by 1/0 as the output. Total data is split into two parts, 80 percent to train the model and remaining for testing operations. The classifier results show an accuracy value of 69.01% when trained using Logistic regression algorithm and the accuracy rate varied with the training set. When the model with LibSVM is trained with 90 percent of data, it gave a result of 71.82%. These results give a significant edge to the investors and they show good correlation between stock market movements and the sentiments of public expressed in twitter. This trend shows that with increasing dataset the models are performing well. We would like to incorporate more data in our future work. Conclusion In this paper, we have shown that a strong correlation exists between rise/fall in stock prices of a company to the public opinions or emotions about that company expressed on twitter through tweets. The main contribution of our work is the development of a sentiment analyzer that can judge the type of sentiment present in the tweet. The tweets are classified into three categories: positive, negative and neutral. At the beginning, we claimed that positive emotions or sentiment of public in twitter about a company would reflect in its stock price. Our speculation is well supported by the results achieved and seems to have a promising future in research. Future Work In this work, we have considered only twitter data for analyzing people's sentiment which may be biased because not all the people who trade in stocks share their opinions on twitter. Stocktwits BIBREF22 is a financial communication platform designed solely for sharing ideas and insights of investors, entrepreneurs and traders. The current study can be extended by incorporating stocktwits data. In addition to this, data from news can also be included for an exhaustive public opinion collection. While training the sentiment analyzer, 3,216 tweets are used which is comparatively a less number to train a sentiment analyzer. In future, we look forward to human annotate more than 10,000 tweets and train the classifiers. With increasing size of training datasets, the models tend to perform better. Acknowledgment The authors would like to thank the students of IIT Bhubaneswar who contributed to the human annotation of tweets.
What dataset is used to train the model?
b1ce129678e37070e69f01332f1a8587e18e06b0
What Ġdataset Ġis Ġused Ġto Ġtrain Ġthe Ġmodel ?
2 , 50 , 000 Ġtweets , ĠStock Ġopening Ġand Ġclosing Ġprices Ġof ĠMicrosoft Ġfrom ĠAugust Ġ31 st , Ġ2015 Ġto ĠAugust Ġ25 th , Ġ2016
<s> What Ġdataset Ġis Ġused Ġto Ġtrain Ġthe Ġmodel ? </s> Introduction </s> Earlier Ġstudies Ġon Ġstock Ġmarket Ġprediction Ġare Ġbased Ġon Ġthe Ġhistorical Ġstock Ġprices . ĠLater Ġstudies Ġhave Ġdebunked Ġthe Ġapproach Ġof Ġpredicting Ġstock Ġmarket Ġmovements Ġusing Ġhistorical Ġprices . ĠStock Ġmarket Ġprices Ġare Ġlargely Ġfluct uating . ĠThe Ġefficient Ġmarket Ġhypothesis Ġ( EM H ) Ġstates Ġthat Ġfinancial Ġmarket Ġmovements Ġdepend Ġon Ġnews , Ġcurrent Ġevents Ġand Ġproduct Ġreleases Ġand Ġall Ġthese Ġfactors Ġwill Ġhave Ġa Ġsignificant Ġimpact Ġon Ġa Ġcompany 's Ġstock Ġvalue ĠB IB REF 0 Ġ. ĠBecause Ġof Ġthe Ġlying Ġunpredict ability Ġin Ġnews Ġand Ġcurrent Ġevents , Ġstock Ġmarket Ġprices Ġfollow Ġa Ġrandom Ġwalk Ġpattern Ġand Ġcannot Ġbe Ġpredicted Ġwith Ġmore Ġthan Ġ50 % Ġaccuracy ĠB IB REF 1 Ġ. </s> With Ġthe Ġadvent Ġof Ġsocial Ġmedia , Ġthe Ġinformation Ġabout Ġpublic Ġfeelings Ġhas Ġbecome Ġabundant . ĠSocial Ġmedia Ġis Ġtransforming Ġlike Ġa Ġperfect Ġplatform Ġto Ġshare Ġpublic Ġemotions Ġabout Ġany Ġtopic Ġand Ġhas Ġa Ġsignificant Ġimpact Ġon Ġoverall Ġpublic Ġopinion . ĠTwitter , Ġa Ġsocial Ġmedia Ġplatform , Ġhas Ġreceived Ġa Ġlot Ġof Ġattention Ġfrom Ġresearchers Ġin Ġthe Ġrecent Ġtimes . ĠTwitter Ġis Ġa Ġmicro - blog ging Ġapplication Ġthat Ġallows Ġusers Ġto Ġfollow Ġand Ġcomment Ġother Ġusers Ġthoughts Ġor Ġshare Ġtheir Ġopinions Ġin Ġreal Ġtime ĠB IB REF 2 Ġ. ĠMore Ġthan Ġmillion Ġusers Ġpost Ġover Ġ140 Ġmillion Ġtweets Ġevery Ġday . ĠThis Ġsituation Ġmakes ĠTwitter Ġlike Ġa Ġcorpus Ġwith Ġvaluable Ġdata Ġfor Ġresearchers ĠB IB REF 3 Ġ. Each Ġtweet Ġis Ġof Ġ140 Ġcharacters Ġlong Ġand Ġspeaks Ġpublic Ġopinion Ġon Ġa Ġtopic Ġconcise ly . ĠThe Ġinformation Ġexploited Ġfrom Ġtweets Ġare Ġvery Ġuseful Ġfor Ġmaking Ġpredictions ĠB IB REF 4 Ġ. </s> In Ġthis Ġpaper , Ġwe Ġcontribute Ġto Ġthe Ġfield Ġof Ġsentiment Ġanalysis Ġof Ġtwitter Ġdata . ĠSent iment Ġclassification Ġis Ġthe Ġtask Ġof Ġjudging Ġopinion Ġin Ġa Ġpiece Ġof Ġtext Ġas Ġpositive , Ġnegative Ġor Ġneutral . </s> There Ġare Ġmany Ġstudies Ġinvolving Ġtwitter Ġas Ġa Ġmajor Ġsource Ġfor Ġpublic - op inion Ġanalysis . ĠAs ur Ġand ĠHu berman ĠB IB REF 5 Ġhave Ġpredicted Ġbox Ġoffice Ġcollections Ġfor Ġa Ġmovie Ġprior Ġto Ġits Ġrelease Ġbased Ġon Ġpublic Ġsentiment Ġrelated Ġto Ġmovies , Ġas Ġexpressed Ġon ĠTwitter . ĠGoogle Ġflu Ġtrends Ġare Ġbeing Ġwidely Ġstudied Ġalong Ġwith Ġtwitter Ġfor Ġearly Ġprediction Ġof Ġdisease Ġoutbreaks . ĠE iji Ġet Ġal . ĠB IB REF 6 Ġhave Ġstudied Ġthe Ġtwitter Ġdata Ġfor Ġcatching Ġthe Ġflu Ġoutbreaks . ĠRu iz Ġet Ġal . ĠB IB REF 7 Ġhave Ġused Ġtime - con str ained Ġgraphs Ġto Ġstudy Ġthe Ġproblem Ġof Ġcorrel ating Ġthe ĠTwitter Ġmicro - blog ging Ġactivity Ġwith Ġchanges Ġin Ġstock Ġprices Ġand Ġtrading Ġvolumes . ĠBord ino Ġet Ġal . ĠB IB REF 8 Ġhave Ġshown Ġthat Ġtrading Ġvolumes Ġof Ġstocks Ġtraded Ġin ĠNAS DAQ - 100 Ġare Ġcorrelated Ġwith Ġtheir Ġquery Ġvolumes Ġ( i . e ., Ġthe Ġnumber Ġof Ġusers Ġrequests Ġsubmitted Ġto Ġsearch Ġengines Ġon Ġthe ĠInternet ). ĠGilbert Ġand ĠKar ah al ios ĠB IB REF 9 Ġhave Ġfound Ġout Ġthat Ġincreases Ġin Ġexpressions Ġof Ġanxiety , Ġworry Ġand Ġfear Ġin Ġwe blogs Ġpredict Ġdownward Ġpressure Ġon Ġthe ĠS & P Ġ500 Ġindex . ĠB ollen ĠB IB REF 10 Ġshowed Ġthat Ġpublic Ġmood Ġanalyzed Ġthrough Ġtwitter Ġfeeds Ġis Ġwell Ġcorrelated Ġwith ĠDow ĠJones ĠIndustrial ĠAverage Ġ( DJ IA ). ĠAll Ġthese Ġstudies Ġshowcased Ġtwitter Ġas Ġa Ġvaluable Ġsource Ġand Ġa Ġpowerful Ġtool Ġfor Ġconducting Ġstudies Ġand Ġmaking Ġpredictions . </s> Rest Ġof Ġthe Ġpaper Ġis Ġorganized Ġas Ġfollows . ĠSection Ġ2 Ġdescribes Ġthe Ġrelated Ġworks Ġand ĠSection Ġ3 Ġdiscusses Ġthe Ġdata Ġportion Ġdemonstrating Ġthe Ġdata Ġcollection Ġand Ġpre - processing Ġpart . ĠIn ĠSection Ġ4 Ġwe Ġdiscuss Ġthe Ġsentiment Ġanalysis Ġpart Ġin Ġour Ġwork Ġfollowed Ġby ĠSection Ġ5 Ġwhich Ġexamines Ġthe Ġcorrelation Ġpart Ġof Ġextracted Ġsentiment Ġwith Ġstocks . ĠIn ĠSection Ġ6 Ġwe Ġpresent Ġthe Ġresults , Ġaccuracy Ġand Ġprecision Ġof Ġour Ġsentiment Ġanaly zer Ġfollowed Ġby Ġthe Ġaccuracy Ġof Ġcorrelation Ġanaly zer . ĠIn ĠSection Ġ7 Ġwe Ġpresent Ġour Ġconclusions Ġand ĠSection Ġ8 Ġdeals Ġwith Ġour Ġfuture Ġwork Ġplan . </s> Related ĠWork </s> The Ġmost Ġwell - known Ġpublication Ġin Ġthis Ġarea Ġis Ġby ĠB ollen ĠB IB REF 10 Ġ. ĠThey Ġinvestigated Ġwhether Ġthe Ġcollective Ġmood Ġstates Ġof Ġpublic Ġ( Happy , Ġcalm , ĠAnxiety ) Ġderived Ġfrom Ġtwitter Ġfeeds Ġare Ġcorrelated Ġto Ġthe Ġvalue Ġof Ġthe ĠDow ĠJones ĠIndustrial ĠIndex . ĠThey Ġused Ġa ĠF uzz y Ġneural Ġnetwork Ġfor Ġtheir Ġprediction . ĠTheir Ġresults Ġshow Ġthat Ġpublic Ġmood Ġstates Ġin Ġtwitter Ġare Ġstrongly Ġcorrelated Ġwith ĠDow ĠJones ĠIndustrial ĠIndex . ĠChen Ġand ĠL azer ĠB IB REF 11 Ġderived Ġinvestment Ġstrategies Ġby Ġobserving Ġand Ġclass ifying Ġthe Ġtwitter Ġfeeds . ĠBing Ġet Ġal . ĠB IB REF 12 Ġstudied Ġthe Ġtweets Ġand Ġconcluded Ġthe Ġpredict ability Ġof Ġstock Ġprices Ġbased Ġon Ġthe Ġtype Ġof Ġindustry Ġlike ĠFinance , ĠIT Ġetc . ĠZhang ĠB IB REF 13 Ġfound Ġout Ġa Ġhigh Ġnegative Ġcorrelation Ġbetween Ġmood Ġstates Ġlike Ġhope , Ġfear Ġand Ġworry Ġin Ġtweets Ġwith Ġthe ĠDow ĠJones ĠAverage ĠIndex . ĠRecently , ĠBrian Ġet Ġal . ĠB IB REF 14 Ġinvestigated Ġthe Ġcorrelation Ġof Ġsentiments Ġof Ġpublic Ġwith Ġstock Ġincrease Ġand Ġdecreases Ġusing ĠPearson Ġcorrelation Ġcoefficient Ġfor Ġstocks . ĠIn Ġthis Ġpaper , Ġwe Ġtook Ġa Ġnovel Ġapproach Ġof Ġpredicting Ġrise Ġand Ġfall Ġin Ġstock Ġprices Ġbased Ġon Ġthe Ġsentiments Ġextracted Ġfrom Ġtwitter Ġto Ġfind Ġthe Ġcorrelation . ĠThe Ġcore Ġcontribution Ġof Ġour Ġwork Ġis Ġthe Ġdevelopment Ġof Ġa Ġsentiment Ġanaly zer Ġwhich Ġworks Ġbetter Ġthan Ġthe Ġone Ġin ĠBrian 's Ġwork Ġand Ġa Ġnovel Ġapproach Ġto Ġfind Ġthe Ġcorrelation . ĠSent iment Ġanaly zer Ġis Ġused Ġto Ġclassify Ġthe Ġsentiments Ġin Ġtweets Ġextracted . The Ġhuman Ġannot ated Ġdataset Ġin Ġour Ġwork Ġis Ġalso Ġexhaustive . ĠWe Ġhave Ġshown Ġthat Ġa Ġstrong Ġcorrelation Ġexists Ġbetween Ġtwitter Ġsentiments Ġand Ġthe Ġnext Ġday Ġstock Ġprices Ġin Ġthe Ġresults Ġsection . ĠWe Ġdid Ġso Ġby Ġconsidering Ġthe Ġtweets Ġand Ġstock Ġopening Ġand Ġclosing Ġprices Ġof ĠMicrosoft Ġover Ġa Ġyear . </s> Data ĠCollection </s> A Ġtotal Ġof Ġ2 , 50 , 000 Ġtweets Ġover Ġa Ġperiod Ġof ĠAugust Ġ31 st , Ġ2015 Ġto ĠAugust Ġ25 th , 2016 Ġon ĠMicrosoft Ġare Ġextracted Ġfrom Ġtwitter ĠAPI ĠB IB REF 15 Ġ. ĠTwitter 4 J Ġis Ġa Ġjava Ġapplication Ġwhich Ġhelps Ġus Ġto Ġextract Ġtweets Ġfrom Ġtwitter . ĠThe Ġtweets Ġwere Ġcollected Ġusing ĠTwitter ĠAPI Ġand Ġfiltered Ġusing Ġkeywords Ġlike Ġ$ ĠMS FT , Ġ# ĠMicrosoft , Ġ# Windows Ġetc . ĠNot Ġonly Ġthe Ġopinion Ġof Ġpublic Ġabout Ġthe Ġcompany 's Ġstock Ġbut Ġalso Ġthe Ġopinions Ġabout Ġproducts Ġand Ġservices Ġoffered Ġby Ġthe Ġcompany Ġwould Ġhave Ġa Ġsignificant Ġimpact Ġand Ġare Ġworth Ġstudying . ĠBased Ġon Ġthis Ġprinciple , Ġthe Ġkeywords Ġused Ġfor Ġfiltering Ġare Ġdevised Ġwith Ġextensive Ġcare Ġand Ġtweets Ġare Ġextracted Ġin Ġsuch Ġa Ġway Ġthat Ġthey Ġrepresent Ġthe Ġexact Ġemotions Ġof Ġpublic Ġabout ĠMicrosoft Ġover Ġa Ġperiod Ġof Ġtime . ĠThe Ġnews Ġon Ġtwitter Ġabout ĠMicrosoft Ġand Ġtweets Ġregarding Ġthe Ġproduct Ġreleases Ġwere Ġalso Ġincluded . ĠStock Ġopening Ġand Ġclosing Ġprices Ġof ĠMicrosoft Ġfrom ĠAugust Ġ31 st , Ġ2015 Ġto ĠAugust Ġ25 th , Ġ2016 Ġare Ġobtained Ġfrom ĠYahoo ! ĠFinance ĠB IB REF 16 Ġ. </s> Data ĠPre - Process ing </s> Stock Ġprices Ġdata Ġcollected Ġis Ġnot Ġcomplete Ġunderstandably Ġbecause Ġof Ġweekends Ġand Ġpublic Ġholidays Ġwhen Ġthe Ġstock Ġmarket Ġdoes Ġnot Ġfunction . ĠThe Ġmissing Ġdata Ġis Ġapprox imated Ġusing Ġa Ġsimple Ġtechnique Ġby ĠGo el ĠB IB REF 17 Ġ. ĠStock Ġdata Ġusually Ġfollows Ġa Ġconc ave Ġfunction . ĠSo , Ġif Ġthe Ġstock Ġvalue Ġon Ġa Ġday Ġis Ġx Ġand Ġthe Ġnext Ġvalue Ġpresent Ġis Ġy Ġwith Ġsome Ġmissing Ġin Ġbetween . ĠThe Ġfirst Ġmissing Ġvalue Ġis Ġapprox imated Ġto Ġbe Ġ( y + x )/ 2 Ġand Ġthe Ġsame Ġmethod Ġis Ġfollowed Ġto Ġfill Ġall Ġthe Ġgaps . </s> Twe ets Ġconsists Ġof Ġmany Ġacron y ms , Ġemot icons Ġand Ġunnecessary Ġdata Ġlike Ġpictures Ġand ĠURL 's . ĠSo Ġtweets Ġare Ġpre process ed Ġto Ġrepresent Ġcorrect Ġemotions Ġof Ġpublic . ĠFor Ġpre processing Ġof Ġtweets Ġwe Ġemployed Ġthree Ġstages Ġof Ġfiltering : ĠToken ization , ĠStop words Ġremoval Ġand Ġregex Ġmatching Ġfor Ġremoving Ġspecial Ġcharacters . </s> Twe ets Ġare Ġsplit Ġinto Ġindividual Ġwords Ġbased Ġon Ġthe Ġspace Ġand Ġirrelevant Ġsymbols Ġlike Ġemot icons Ġare Ġremoved . ĠWe Ġform Ġa Ġlist Ġof Ġindividual Ġwords Ġfor Ġeach Ġtweet . </s> Words Ġthat Ġdo Ġnot Ġexpress Ġany Ġemotion Ġare Ġcalled ĠStop words . ĠAfter Ġsplitting Ġa Ġtweet , Ġwords Ġlike Ġa , is , Ġthe , Ġwith Ġetc . Ġare Ġremoved Ġfrom Ġthe Ġlist Ġof Ġwords . </s> Re gex Ġmatching Ġin ĠPython Ġis Ġperformed Ġto Ġmatch ĠURL âĢ Ļ s Ġand Ġare Ġreplaced Ġby Ġthe Ġterm ĠURL . ĠOften Ġtweets Ġconsists Ġof Ġhasht ags ( # ) Ġand Ġ@ Ġaddressing Ġother Ġusers . ĠThey Ġare Ġalso Ġreplaced Ġsuit ably . ĠFor Ġexample , Ġ# Microsoft Ġis Ġreplaced Ġwith ĠMicrosoft Ġand Ġ@ Bill g ates Ġis Ġreplaced Ġwith ĠUS ER . ĠPro long ed Ġword Ġshowing Ġintense Ġemotions Ġlike Ġco oooo oo ool ! Ġis Ġreplaced Ġwith Ġcool ! ĠAfter Ġthese Ġstages Ġthe Ġtweets Ġare Ġready Ġfor Ġsentiment Ġclassification . </s> Sent iment ĠAnalysis </s> Sent iment Ġanalysis Ġtask Ġis Ġvery Ġmuch Ġfield Ġspecific . ĠThere Ġis Ġlot Ġof Ġresearch Ġon Ġsentiment Ġanalysis Ġof Ġmovie Ġreviews Ġand Ġnews Ġarticles Ġand Ġmany Ġsentiment Ġanaly zers Ġare Ġavailable Ġas Ġan Ġopen Ġsource . ĠThe Ġmain Ġproblem Ġwith Ġthese Ġanaly zers Ġis Ġthat Ġthey Ġare Ġtrained Ġwith Ġa Ġdifferent Ġcorpus . ĠFor Ġinstance , ĠMovie Ġcorpus Ġand Ġstock Ġcorpus Ġare Ġnot Ġequivalent . ĠSo , Ġwe Ġdeveloped Ġour Ġown Ġsentiment Ġanaly zer . </s> Twe ets Ġare Ġclassified Ġas Ġpositive , Ġnegative Ġand Ġneutral Ġbased Ġon Ġthe Ġsentiment Ġpresent ĠB IB REF 18 Ġ. Ġ3 , 216 Ġtweets Ġout Ġof Ġthe Ġtotal Ġtweets Ġare Ġexamined Ġby Ġhumans Ġand Ġannot ated Ġas Ġ1 Ġfor ĠPositive , Ġ0 Ġfor ĠNeutral Ġand Ġ2 Ġfor ĠNegative Ġemotions . ĠFor Ġclassification Ġof Ġnon human Ġannot ated Ġtweets Ġa Ġmachine Ġlearning Ġmodel Ġis Ġtrained Ġwhose Ġfeatures Ġare Ġextracted Ġfrom Ġthe Ġhuman Ġannot ated Ġtweets . </s> Feature ĠExt raction </s> Text ual Ġrepresentations Ġare Ġdone Ġusing Ġtwo Ġmethods : n - gram s Ġand ĠWord 2 vec </s> N - gram Ġrepresentation Ġis Ġknown Ġfor Ġits Ġspecificity Ġto Ġmatch Ġthe Ġcorpus Ġof Ġtext Ġbeing Ġstudied . ĠIn Ġthese Ġtechniques Ġa Ġfull Ġcorpus Ġof Ġrelated Ġtext Ġis Ġparsed Ġwhich Ġare Ġtweets Ġin Ġthe Ġpresent Ġwork , Ġand Ġevery Ġappearing Ġword Ġsequence Ġof Ġlength Ġn Ġis Ġextracted Ġfrom Ġthe Ġtweets Ġto Ġform Ġa Ġdictionary Ġof Ġwords Ġand Ġphrases . ĠFor Ġexample Ġthe Ġtext ĠâĢ ľ Microsoft Ġis Ġlaunching Ġa Ġnew Ġproduct " Ġhas Ġthe Ġfollowing Ġ3 - gram Ġword Ġfeatures : âĢ ľ Microsoft Ġis Ġlaunching ", ĠâĢ ľ is Ġlaunching Ġa ", ĠâĢ ľ laun ching Ġa Ġnew " Ġand ĠâĢ ľ a Ġnew Ġproduct ". ĠIn Ġour Ġcase , ĠN - gram s Ġfor Ġall Ġthe Ġtweets Ġform Ġthe Ġcorpus . ĠIn Ġthis Ġrepresentation , Ġtweet Ġis Ġsplit Ġinto ĠN - gram s Ġand Ġthe Ġfeatures Ġto Ġthe Ġmodel Ġare Ġa Ġstring Ġof Ġ1 âĢ Ļ s Ġand Ġ0 âĢ Ļ s Ġwhere Ġ1 Ġrepresents Ġthe Ġpresence Ġof Ġthat ĠN - gram Ġof Ġthe Ġtweet Ġin Ġthe Ġcorpus Ġand Ġa Ġ0 Ġindicates Ġthe Ġabsence . </s> Word 2 vec Ġrepresentation Ġis Ġfar Ġbetter , Ġadvanced Ġand Ġa Ġrecent Ġtechnique Ġwhich Ġfunctions Ġby Ġmapping Ġwords Ġto Ġa Ġ300 Ġdimensional Ġvector Ġrepresentations . ĠOnce Ġevery Ġword Ġof Ġthe Ġlanguage Ġhas Ġbeen Ġmapped Ġto Ġa Ġunique Ġvector , Ġvectors Ġof Ġwords Ġcan Ġbe Ġsummed Ġup Ġyielding Ġa Ġresultant Ġvector Ġfor Ġany Ġgiven Ġcollection Ġof Ġwords ĠB IB REF 19 Ġ. ĠRelationship Ġbetween Ġthe Ġwords Ġis Ġexactly Ġretained Ġin Ġthis Ġform Ġof Ġrepresentation . ĠWord Ġvectors Ġdifference Ġbetween ĠRome Ġand ĠItaly Ġis Ġvery Ġclose Ġto Ġthe Ġdifference Ġbetween Ġvectors Ġof ĠFrance Ġand ĠParis ĠThis Ġsustained Ġrelationship Ġbetween Ġword Ġconcepts Ġmakes Ġword 2 vec Ġmodel Ġvery Ġattractive Ġfor Ġtextual Ġanalysis . ĠIn Ġthis Ġrepresentation , Ġresultant Ġvector Ġwhich Ġis Ġsum Ġof Ġ300 Ġdimensional Ġvectors Ġof Ġall Ġwords Ġin Ġa Ġtweet Ġacts Ġas Ġfeatures Ġto Ġthe Ġmodel . </s> Model ĠTraining </s> The Ġfeatures Ġextracted Ġusing Ġthe Ġabove Ġmethods Ġfor Ġthe Ġhuman Ġannot ated Ġtweets Ġare Ġfed Ġto Ġthe Ġclass ifier Ġand Ġtrained Ġusing Ġrandom Ġforest Ġalgorithm . ĠBoth Ġthe Ġtextual Ġrepresentations Ġperformed Ġwell Ġand Ġthe Ġresults Ġare Ġcomparable . ĠOut Ġof Ġthe Ġtwo , Ġmodel Ġtrained Ġwith Ġword 2 vec Ġrepresentation Ġis Ġpicked Ġbecause Ġof Ġits Ġsustainability Ġof Ġmeaning Ġand Ġpromising Ġperformance Ġover Ġlarge Ġdatasets . ĠThe Ġresults Ġof Ġsentiment Ġclassification Ġare Ġdiscussed Ġin Ġthe Ġfollowing Ġsections . ĠThe Ġdevised Ġclass ifier Ġis Ġused Ġto Ġpredict Ġthe Ġemotions Ġof Ġnon - human Ġannot ated Ġtweets . ĠTable - 1 Ġshows Ġa Ġsample Ġof Ġannot ated Ġtweets Ġby Ġthe Ġsentiment Ġanaly zer . </s> Cor relation ĠAnalysis Ġof ĠPrice Ġand ĠSent iment Ġ </s> The Ġstock Ġprice Ġdata Ġof ĠMicrosoft Ġare Ġlabeled Ġsuit ably Ġfor Ġtraining Ġusing Ġa Ġsimple Ġprogram . ĠIf Ġthe Ġprevious Ġday Ġstock Ġprice Ġis Ġmore Ġthan Ġthe Ġcurrent Ġday Ġstock Ġprice , Ġthe Ġcurrent Ġday Ġis Ġmarked Ġwith Ġa Ġnumeric Ġvalue Ġof Ġ0 , Ġelse Ġmarked Ġwith Ġa Ġnumeric Ġvalue Ġof Ġ1 . ĠNow , Ġthis Ġcorrelation Ġanalysis Ġturns Ġout Ġto Ġbe Ġa Ġclassification Ġproblem . ĠThe Ġtotal Ġpositive , Ġnegative Ġand Ġneutral Ġemotions Ġin Ġtweets Ġin Ġa Ġ3 Ġday Ġperiod Ġare Ġcalculated Ġsuccess ively Ġwhich Ġare Ġused Ġas Ġfeatures Ġfor Ġthe Ġclass ifier Ġmodel Ġand Ġthe Ġoutput Ġis Ġthe Ġlabeled Ġnext Ġday Ġvalue Ġof Ġstock Ġ0 Ġor Ġ1 . The Ġwindow Ġsize Ġis Ġexperimented Ġand Ġbest Ġresults Ġare Ġachieved Ġwhen Ġthe Ġsentiment Ġvalues Ġpreced e Ġ3 Ġdays Ġto Ġthe Ġstock Ġprice . ĠA Ġtotal Ġof Ġ355 Ġinstances , Ġeach Ġwith Ġ3 Ġattributes Ġare Ġfed Ġto Ġthe Ġclass ifier Ġwith Ġa Ġsplit Ġproportions Ġof Ġ80 % Ġtrain Ġdataset Ġand Ġthe Ġremaining Ġdataset Ġfor Ġtesting . ĠThe Ġaccuracy Ġof Ġthe Ġclass ifier Ġis Ġdiscussed Ġin Ġthe Ġresults Ġsection . </s> Results Ġand ĠDiscussion </s> This Ġsection Ġgives Ġan Ġoverview Ġof Ġaccuracy Ġrates Ġof Ġthe Ġtrained Ġclass ifiers . ĠAll Ġthe Ġcalculations Ġare Ġdone Ġin ĠWe ka Ġtool Ġwhich Ġruns Ġon Ġjava Ġvirtual Ġmachine ĠB IB REF 20 </s> Sent iment ĠAnaly zer ĠResults </s> The Ġabove Ġsections Ġdiscussed Ġthe Ġmethod Ġfollowed Ġto Ġtrain Ġthe Ġclass ifier Ġused Ġfor Ġsentiment Ġanalysis Ġof Ġtweets . ĠThe Ġclass ifier Ġwith Ġfeatures Ġas ĠWord 2 vec Ġrepresentations Ġof Ġhuman Ġannot ated Ġtweets Ġtrained Ġon ĠRandom ĠForest Ġalgorithm Ġwith Ġa Ġsplit Ġpercentage Ġof Ġ90 Ġfor Ġtraining Ġthe Ġmodel Ġand Ġremaining Ġfor Ġtesting Ġthe Ġmodel Ġshowed Ġan Ġaccuracy Ġof Ġ70 . 2 %. ĠWith ĠN - gram Ġrepresentations , Ġthe Ġclass ifier Ġmodel Ġwith Ġsame Ġalgorithm Ġand Ġwith Ġsame Ġdataset Ġshowed Ġan Ġaccuracy Ġof Ġ70 . 5 %. ĠThough Ġthe Ġresults Ġare Ġvery Ġclose , Ġmodel Ġtrained Ġwith Ġword 2 vec Ġrepresentations Ġis Ġpicked Ġto Ġclassify Ġthe Ġnon human Ġannot ated Ġtweets Ġbecause Ġof Ġits Ġpromising Ġaccuracy Ġfor Ġlarge Ġdatasets Ġand Ġthe Ġsustainability Ġin Ġword Ġmeaning . ĠNumerous Ġstudies Ġhave Ġbeen Ġconducted Ġon Ġpeople Ġand Ġthey Ġconcluded Ġthat Ġthe Ġrate Ġof Ġhuman Ġconc ord ance , Ġthat Ġis Ġthe Ġdegree Ġof Ġagreement Ġamong Ġhumans Ġon Ġthe Ġsentiment Ġof Ġa Ġtext , Ġis Ġbetween Ġ70 % Ġand Ġ79 % ĠB IB REF 21 Ġ. ĠThey Ġhave Ġalso Ġsynthes ized Ġthat Ġsentiment Ġanaly zers Ġabove Ġ70 % Ġare Ġvery Ġaccurate Ġin Ġmost Ġof Ġthe Ġcases . ĠProvided Ġthis Ġinformation , Ġthe Ġresults Ġwe Ġobtained Ġfrom Ġthe Ġsentiment Ġclassification Ġcan Ġbe Ġobserved Ġas Ġvery Ġgood Ġfigures Ġwhile Ġpredicting Ġthe Ġsentiments Ġin Ġshort Ġtexts , Ġtweets , Ġless Ġthan Ġ140 Ġcharacters Ġin Ġlength . ĠTable - 2 Ġdepicts Ġthe Ġresults Ġof Ġsentiment Ġclassification Ġincluding Ġaccuracy , Ġprecision , ĠF - me asure Ġand Ġrecall Ġwhen Ġtrained Ġwith Ġdifferent Ġmachine Ġlearning Ġalgorithms . ĠR OC Ġcurves Ġare Ġplotted Ġfor Ġdetailed Ġanalysis . </s> Stock ĠPrice Ġand ĠSent iment ĠCor relation ĠResults </s> A Ġclass ifier Ġis Ġpresented Ġin Ġthe Ġprevious Ġsections Ġthat Ġis Ġtrained Ġwith Ġaggregate Ġsentiment Ġvalues Ġfor Ġ3 - day Ġperiod Ġas Ġfeatures Ġand Ġthe Ġincrease / dec re ase Ġin Ġstock Ġprice Ġrepresented Ġby Ġ1 / 0 Ġas Ġthe Ġoutput . ĠTotal Ġdata Ġis Ġsplit Ġinto Ġtwo Ġparts , Ġ80 Ġpercent Ġto Ġtrain Ġthe Ġmodel Ġand Ġremaining Ġfor Ġtesting Ġoperations . ĠThe Ġclass ifier Ġresults Ġshow Ġan Ġaccuracy Ġvalue Ġof Ġ69 . 01 % Ġwhen Ġtrained Ġusing ĠLog istic Ġregression Ġalgorithm Ġand Ġthe Ġaccuracy Ġrate Ġvaried Ġwith Ġthe Ġtraining Ġset . ĠWhen Ġthe Ġmodel Ġwith ĠLib S VM Ġis Ġtrained Ġwith Ġ90 Ġpercent Ġof Ġdata , Ġit Ġgave Ġa Ġresult Ġof Ġ71 . 82 %. ĠThese Ġresults Ġgive Ġa Ġsignificant Ġedge Ġto Ġthe Ġinvestors Ġand Ġthey Ġshow Ġgood Ġcorrelation Ġbetween Ġstock Ġmarket Ġmovements Ġand Ġthe Ġsentiments Ġof Ġpublic Ġexpressed Ġin Ġtwitter . ĠThis Ġtrend Ġshows Ġthat Ġwith Ġincreasing Ġdataset Ġthe Ġmodels Ġare Ġperforming Ġwell . ĠWe Ġwould Ġlike Ġto Ġincorporate Ġmore Ġdata Ġin Ġour Ġfuture Ġwork . </s> Conclusion </s> In Ġthis Ġpaper , Ġwe Ġhave Ġshown Ġthat Ġa Ġstrong Ġcorrelation Ġexists Ġbetween Ġrise / fall Ġin Ġstock Ġprices Ġof Ġa Ġcompany Ġto Ġthe Ġpublic Ġopinions Ġor Ġemotions Ġabout Ġthat Ġcompany Ġexpressed Ġon Ġtwitter Ġthrough Ġtweets . ĠThe Ġmain Ġcontribution Ġof Ġour Ġwork Ġis Ġthe Ġdevelopment Ġof Ġa Ġsentiment Ġanaly zer Ġthat Ġcan Ġjudge Ġthe Ġtype Ġof Ġsentiment Ġpresent Ġin Ġthe Ġtweet . ĠThe Ġtweets Ġare Ġclassified Ġinto Ġthree Ġcategories : Ġpositive , Ġnegative Ġand Ġneutral . ĠAt Ġthe Ġbeginning , Ġwe Ġclaimed Ġthat Ġpositive Ġemotions Ġor Ġsentiment Ġof Ġpublic Ġin Ġtwitter Ġabout Ġa Ġcompany Ġwould Ġreflect Ġin Ġits Ġstock Ġprice . ĠOur Ġspeculation Ġis Ġwell Ġsupported Ġby Ġthe Ġresults Ġachieved Ġand Ġseems Ġto Ġhave Ġa Ġpromising Ġfuture Ġin Ġresearch . </s> Future ĠWork </s> In Ġthis Ġwork , Ġwe Ġhave Ġconsidered Ġonly Ġtwitter Ġdata Ġfor Ġanalyzing Ġpeople 's Ġsentiment Ġwhich Ġmay Ġbe Ġbiased Ġbecause Ġnot Ġall Ġthe Ġpeople Ġwho Ġtrade Ġin Ġstocks Ġshare Ġtheir Ġopinions Ġon Ġtwitter . ĠStock tw its ĠB IB REF 22 Ġis Ġa Ġfinancial Ġcommunication Ġplatform Ġdesigned Ġsolely Ġfor Ġsharing Ġideas Ġand Ġinsights Ġof Ġinvestors , Ġentrepreneurs Ġand Ġtraders . ĠThe Ġcurrent Ġstudy Ġcan Ġbe Ġextended Ġby Ġincorporating Ġstock tw its Ġdata . ĠIn Ġaddition Ġto Ġthis , Ġdata Ġfrom Ġnews Ġcan Ġalso Ġbe Ġincluded Ġfor Ġan Ġexhaustive Ġpublic Ġopinion Ġcollection . </s> While Ġtraining Ġthe Ġsentiment Ġanaly zer , Ġ3 , 216 Ġtweets Ġare Ġused Ġwhich Ġis Ġcomparatively Ġa Ġless Ġnumber Ġto Ġtrain Ġa Ġsentiment Ġanaly zer . ĠIn Ġfuture , Ġwe Ġlook Ġforward Ġto Ġhuman Ġannot ate Ġmore Ġthan Ġ10 , 000 Ġtweets Ġand Ġtrain Ġthe Ġclass ifiers . ĠWith Ġincreasing Ġsize Ġof Ġtraining Ġdatasets , Ġthe Ġmodels Ġtend Ġto Ġperform Ġbetter . </s> Acknowled gment </s> The Ġauthors Ġwould Ġlike Ġto Ġthank Ġthe Ġstudents Ġof ĠI IT ĠBh ub anes war Ġwho Ġcontributed Ġto Ġthe Ġhuman Ġannotation Ġof Ġtweets .