Open AccessMachine Learning based Twitter Sentimental Analysis in Business Field
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - Uncategorized
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1033.1292s19
Subject(s) - computer science
iSocial inetworking isites ilike itwitter ihave imillions iof ipeople ishare itheir ithoughts iday iby iday ias itweets. iThis ipaper iaddresses ithe iproblem iof isentiment ianalysis iin itwitter; ithat iis iclassifying itweets iaccording ito ithe isentiment iexpressed iin ithem: ipositive, inegative ior ineutral. iTwitter iis ian ionline imicro-blogging iand isocial-networking iplatform iwhich iallows iusers ito iwrite ishort istatus iupdates iof imaximum ilength i140 icharacters. iIt iis ia irapidly iexpanding iservice iwith iover i200 imillion iregistered iusers, iout iof iwhich i100 imillion iare iactive iusers iand ihalf iof ithem ilog ion itwitter ion ia idaily ibasis i- igenerating inearly i250 imillion itweets iper iday. iDue ito ithis ilarge iamount iof iusage iwe ihope ito iachieve ia ireflection iof ipublic isentiment iby ianalyzing ithe isentiments iexpressed iin ithe itweets. iAnalyzing ithe ipublic isentiment iis iimportant ifor imany iapplications isuch ias ifirms itrying ito ifind iout ithe iresponse iof itheir iproducts iin ithe imarket, ipredicting ipolitical ielections iand ipredicting isocioeconomic iphenomena ilike istock iexchange. iThe iproject iis ito idevelop ia ifunctional iclassifier ifor iaccurate iand iautomatic isentiment iclassification iof ian iunknown itweet istream.