Download - Aspect Based Sentiment Analysis - Columbia DataScience · the Aspect Based Sentiment Analysis(ASBA) problem is raised. This project recommends a method to extract aspects from reviews,

AspectBasedSentimentAnalysis

AcknowledgmentsWe would like to give special thanks to our mentors Prof. Smaranda Muresan and Data ScientistPeter Deng for providing their insight and expertise that greatly assisted the research.

Introduction&DatasetTherapidgrowthofe-commercehasledtoanupsurgeofcustomerreviewsthatconstituteimportantsourceofinformationforbothcustomersandbusinesses.Tosummarisecustomers’opinionsonproductsfromtheoverwhelmingamountofreviews,theAspectBasedSentimentAnalysis(ASBA)problemisraised.Thisprojectrecommendsamethodtoextractaspectsfromreviews,andtoidentifythesentimentpolarityofeachcustomerreviewexpressedtowardsspecificaspects.

ReferencesPavlopoulos,Ioannis(2014).“Aspectbasedsentimentanalysis”.In:AthensUniversityofEconomicsandBusiness.

Pontiki,Mariaetal.(2016).“SemEval-2016task5:Aspectbasedsentimentanalysis”.In:Proceedingsofthe10thinternationalworkshoponsemanticevaluation(SemEval-2016),pp.19– 30.

SemEval-2016ABSARestaurantReviews-English:TrainData(n.d.).url:http://metashare.ilsp.gr:8080/.Levy,O.,&Goldberg,Y.(2014).Dependency-basedwordembeddings.InProceedingsofthe52ndAnnualMeeting

oftheAssociationforComputationalLinguistics(Volume2:ShortPapers)(Vol.2,pp.302-308).

AspectDetectionAspectdetectionaimstoderiveaspectsthataredirectlydiscussedinreviewsanditonlyfocusesonexplicitaspects.Themethodcoupleswordembeddingwithclusteringtechniques.Dependency-basedwordembeddingischosenbecauseitcapturesbothsemanticandsyntacticsimilarities.Then,agglomerativeclusteringmethodwithcosinesimilarityisappliedtoclusterwordvectors

ConclusionsThisprojectexploredandexperimentedwithbothsupervisedandunsupervisedABSAmethodsextensively.AppliedvariousNLPtechniquesfromlinearSVMtotransferlearning(pre-trainedwordembeddings)anddeeplearning.Comparedadvantagesanddrawbacksofdifferentapproaches.IntegratedaspectdetectionwithsentimentanalysisandfinallyproposedchainingagglomerativeclusteringtogetherwithSVM+DNNasageneralmethodtosummarise feedbacksfromcustomerreviewsbyaspects.

AspectSpecificSentimentAnalysisSentimentanalysisincludestwophases.Phase(i),foreachaspectinthecandidatelist,abinaryclassifieristrainedtodetectwhetherornotareviewisdescribingit.Thenaspectsarepredictedusingonevstherest.Featureweightsofaclassifierlearnedinphase(i)arefedasfeaturestoclassifiersinphase(ii)todetectsentimentpolarities.

Topic:AspectBasedSentimentAnalysis

Givenareviewasinput,detectsentimentsexpressedtowardsaspectsthatareofconcerntomostcustomers

WordCloud:wordfrequencyinallreviews

Histogramofhuman-annotatedaspectsinthedata

Liutong Zhou(lz2484),Jiachen Xu(jx2318),ZihanYe(zy2293),Youyang Liu(yl3767)IndustryMentor:PeterDeng

FacultyMentor:Smaranda Muresan

Histogramofpolaritiesinthedata

Dendrogram

WordsExpansionofCluster1 EvaluationofAspectDetection

Food,ambience,serviceaspectsdetectionresultsareevaluatedbyprecision,recallandf1-score.Addingexpansionwords

additionaltoclusteringresultcansignificantlyincreaseprecisionsandrecallsforallaspectsTheoptimalnumberofclusterischosentobe15becausethere

issteepincreaseinoverallsimilarityatthatpoint.

Dependency-BasedContextExtractionExample

NumberofClustersvsOverallSimilarity

Algorithm

DictionarySizeImpactsonAspectDetection