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1、ExplainingtheStars:WeightedMultiple-InstanceLearningforAspect-BasedSentimentAnalysisNikolaosPappasAndreiPopescu-BelisEPFLandIdiapResearchInstituteIdiapResearchInstituteRueMarconi19RueMarconi19CH-1920Martigny,SwitzerlandCH-1920Martigny,Switzerlandnikolaos.pappas@idiap.chandrei.pop
2、escu-belis@idiap.chAbstractandthemusic,butnottoomuchtheactors.De-terminingtheratingsofeachaspectautomaticallyThispaperintroducesamodelofmultiple-isachallengingtask,whichmayseemtorequireinstancelearningappliedtothepredic-theengineeringofalargenumberoffeaturesde-tionofaspectratings
3、orjudgmentsofsignedtocaptureeachaspect.Ourgoalistoputspecificpropertiesofanitemfromuser-forwardanewfeature-agnosticsolutionforana-contributedtextssuchasproductreviews.lyzingaspect-relatedratingsexpressedinatext,Eachvariable-lengthtextisrepresentedbythusaimingforafiner-grained,deepe
4、ranalysisofseveralindependentfeaturevectors;onetextmeaningthanoverallsentimentanalysis.wordvectorpersentenceorparagraph.Currentstate-of-the-artapproachestosentimentForlearningfromtextswithknownas-analysisandaspect-basedsentimentanalysis,at-pectratings,themodelperformsmultiple-tem
5、pttogobeyondword-levelfeatureseitherbyinstanceregression(MIR)andassignsim-usinghigher-levellinguisticfeaturessuchasPOSportanceweightstoeachofthesentencestagging,parsing,andknowledgeinfusion,orbyorparagraphsofatext,uncoveringtheirlearningfeaturesthatcapturesyntacticandseman-contri
6、butiontotheaspectratings.Next,ticdependenciesbetweenwords.Onceanappro-themodelisusedtopredictaspectratingspriatefeaturespaceisfound,theratingsaretypi-inpreviouslyunseentexts,demonstratingcallymodeledusingalinearmodel,suchasSup-interpretabilityandexplanatorypowerforportVectorRegre
7、ssion(SVR)with`2normforitspredictions.WeevaluatethemodelonregularizationorLassoRegressionwith`1norm.sevenmulti-aspectsentimentanalysisdataBytreatingatextglobally,thesemodelsignorethesets,improvingoverfourMIRbaselinesfactthatthesentencesofatexthavediversecon-andtwostrongbag-of-wor
8、dslinearmod-tributionstotheoverallsentim
