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1、ClusteringProductFeaturesforOpinionMining†‡††ZhongwuZhaiBingLiuHuaXuPeifaJia†StateKeyLabofIntelligentTech.&Sys.,TsinghuaNationalLabforInfo.Sci.andTech.,Dept.ofComp.Sci.&Tech.,TsinghuaUniversityzhaizhongwu@gmail.com‡Dept.ofComp.Sci.,UniversityofIllinoisatChicagoliub@cs.uic.eduABS
2、TRACTwordsorphrasesthatexpressthesamefeature,featureexpressions.Forexample,“picture”and“photo”arefeatureInsentimentanalysisofproductreviews,oneimportantproblemexpressionsreferringtothesamefeatureofcameras.Inthispaper,istoproduceasummaryofopinionsbasedonproductweassumethatallfeat
3、ureexpressionshavebeenidentifiedbyanfeatures/attributes(alsocalledaspects).However,forthesameexistingalgorithm.Therearemanysuchalgorithms[17-20,29,feature,peoplecanexpressitwithmanydifferentwordsor35,38].phrases.Toproduceausefulsummary,thesewordsandphrases,whicharedomainsynonyms
4、,needtobegroupedunderthesameGroupingfeatureexpressions,whicharedomainsynonyms,isfeaturegroup.Althoughseveralmethodshavebeenproposedtocriticalforeffectiveopinionsummary[26].Sincethereareextractproductfeaturesfromreviews,limitedworkhasbeendonetypicallyhundredsoffeatureexpressionst
5、hatcanbediscoveredonclusteringorgroupingofsynonymfeatures.Thispaperfocusesfromtextforanopinionminingapplication,it’sverytime-onthistask.Classicmethodsforsolvingthisproblemarebasedconsumingandtediousforhumanuserstogroupthemintofeatureonunsupervisedlearningusingsomeformsofdistribu
6、tionalcategories.Someautomatedassistanceisneeded.Unsupervisedsimilarity.However,wefoundthatthesemethodsdonotdowell.learningorclusteringisthenaturaltechniqueforsolvingtheWethenmodelitasasemi-supervisedlearningproblem.Lexicalproblem.Thesimilaritymeasuresusedinclusteringareusuallyc
7、haracteristicsoftheproblemareexploitedtoautomaticallybasedonsomeformofdistributionalsimilarity[6,10,22,24,32,identifysomelabeledexamples.Empiricalevaluationshowsthat34,37].Recentworkalsousedtopicmodeling[12,40].However,theproposedmethodoutperformsexistingstate-of-the-artweshowth
8、atthesemethodsdonotperformwell.Eventhelatestmet
