Lexicon-basedSentiment lexicon GitHubSentiment analysis dictionarysentiment lexicon meaningSentiment polarityTextBlob polaritySentiment lexiconLexicon-based methods for sentiment analysisLexicon enhanced sentiment analysis framework using rule based classification schemeSentiment analysisTwitter sentiment analysis: lexicon method, machine learning method and their combinationAfinn

Social media sentiment analysis: lexicon versus machine ...

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In the present research, we compare lexicon-based and machine learning approaches to automated sentiment analysis. We aim to provide evidence of any performance. Toreadthefullversionofthiscontentpleaseselectoneoftheoptionsbelow: Otheraccessoptions YoumaybeabletoaccessthiscontentbylogginginviayourEmeraldprofile. Login RentthiscontentfromDeepDyve RentfromDeepDyve Ifyouthinkyoushouldhaveaccesstothiscontent,clicktocontactoursupportteam. Contactus Pleasenoteyoudonothaveaccesstoteachingnotes Otheraccessoptions YoumaybeabletoaccessteachingnotesbylogginginviayourEmeraldprofile. Login Ifyouthinkyoushouldhaveaccesstothiscontent,clicktocontactoursupportteam. Contactus Abstract Purpose Withthesoaringvolumesofbrand-relatedsocialmediaconversations,digitalmarketershaveextensiveopportunitiestotrackandanalyseconsumers’feelingsandopinionsaboutbrands,productsorservicesembeddedwithinconsumer-generatedcontent(CGC).These“BigData”opportunitiesrendermanualapproachestosentimentanalysisimpracticalandraisetheneedtodevelopautomatedtoolstoanalyseconsumersentimentexpressedintextformat.ThispaperaimstoevaluateandcomparetheperformanceoftwoprominentapproachestoautomatedsentimentanalysisappliedtoCGConsocialmediaandexploresthebenefitsofcombiningthem. Design/methodology/approach Asampleof850consumercommentsfrom83Facebookbrandpagesareusedtotestandcomparelexicon-basedandmachinelearningapproachestosentimentanalysis,aswellastheircombination,usingtheLIWC2015lexiconandRTextToolsmachinelearningpackage. Findings Resultsshowthetwoapproachesaresimilarinaccuracy,bothachievinghigheraccuracywhenclassifyingpositivesentimentthannegativesentiment.However,theydiffersubstantiallyintheirclassificationensembles.Thecombinedapproachdemonstratessignificantlyimprovedperformanceinclassifyingpositivesentiment. Researchlimitations/implications Furtherresearchisrequiredtoimprovetheaccuracyofnegativesentimentclassification.ThecombinedapproachneedstobeappliedtootherkindsofCGCsonsocialmediasuchastweets. Practicalimplications Thefindingsinformdecision-makingaroundwhichsentimentanalysisapproaches(oracombinationthereof)isbesttoanalyseCGConsocialmedia. Originality/value Thisstudycombinestwosentimentanalysisapproachesanddemonstratessignificantlyimprovedperformance. Keywords Sentimentanalysis Socialmedia Consumer-generatedcontent Citation Dhaoui,C.,Webster,C.M.andTan,L.P.(2017),"Socialmediasentimentanalysis:lexiconversusmachinelearning",JournalofConsumerMarketing,Vol.34No.6,pp.480-488.https://doi.org/10.1108/JCM-03-2017-2141 Publisher: EmeraldPublishingLimited Copyright©2017,EmeraldPublishingLimited × Support&Feedback Managecookies We’relistening—telluswhatyouthink Somethingdidn’twork… Reportbugshere Allfeedbackisvaluable Pleaseshareyourgeneralfeedback MemberofEmeraldEngage? Youcanjoininthediscussionbyjoiningthecommunityorlogginginhere.YoucanalsofindoutmoreaboutEmeraldEngage. Joinusonourjourney Platformupdatepage Visitemeraldpublishing.com/platformupdatetodiscoverthelatestnewsandupdates Questions&MoreInformation Answerstothemostcommonlyaskedquestionshere

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