Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

384 Pages·2015·3.01 MB·English

by  Bing Liu

SentimentAnalysis
MiningOpinions,Sentiments,andEmotions
Sentimentanalysisisthecomputationalstudyofpeople’sopinions,sentiments,emo-
tions,andattitudes.Thisfascinatingproblemisincreasinglyimportantinbusinessand
society.Itoffersnumerousresearchchallengesbutpromisesinsightusefultoanyone
interestedinopinionanalysisandsocialmediaanalysis.
Thisbookgivesacomprehensiveintroductiontothetopicfromaprimarilynatural
languageprocessingpointofviewtohelpreadersunderstandtheunderlyingstructure
oftheproblemandthelanguageconstructsthatarecommonlyusedtoexpressopinions
andsentiments.Itcoversallcoreareasofsentimentanalysis;includesmanyemerging
themes, such as debate analysis, intention mining, and fake-opinion detection; and
presents computational methods to analyze and summarize opinions. It will be a
valuable resource for researchers and practitioners in natural language processing,
computerscience,managementsciences,andthesocialsciences.
BingLiuisaprofessorofcomputerscienceattheUniversityofIllinoisatChicago.His
currentresearchinterestsincludesentimentanalysisandopinionmining,datamining,
machine learning, and natural language processing. He has published extensively in
topconferencesandjournals,andhisresearchhasbeencitedonthefrontpageofthe
NewYorkTimes.Heisalsotheauthoroftwobooks:SentimentAnalysisandOpinion
Mining (2012) and Web Data Mining: Exploring Hyperlinks, Contents, and Usage
Data (first edition, 2007; secondedition, 2011).He currently serves asthe Chair of
ACMSIGKDDandisanIEEEFellow.

Sentiment Analysis
Mining Opinions, Sentiments, and Emotions
BING LIU
UniversityofIllinoisatChicago
32AvenueoftheAmericas,NewYork,NY10013-2473,USA
CambridgeUniversityPressispartoftheUniversityofCambridge.
ItfurtherstheUniversity’smissionbydisseminatingknowledgeinthepursuitof
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www.cambridge.org
Informationonthistitle:www.cambridge.org/9781107017894
(cid:2)C BingLiu2015
Thispublicationisincopyright.Subjecttostatutoryexception
andtotheprovisionsofrelevantcollectivelicensingagreements,
noreproductionofanypartmaytakeplacewithoutthewritten
permissionofCambridgeUniversityPress.
Firstpublished2015
PrintedintheUnitedStatesofAmerica
AcatalogrecordforthispublicationisavailablefromtheBritishLibrary.
LibraryofCongressCataloginginPublicationData
Liu,Bing,1963–
Sentimentanalysis:miningopinions,sentiments,andemotions/BingLiu.
pages cm
Includesbibliographicalreferencesandindex.
ISBN978-1-107-01789-4(hardback)
1.Naturallanguageprocessing(Computerscience) 2.Computationallinguistics. 3.Public
opinion–Dataprocessing. 4.Datamining. I.Title.
QA76.9.N38L58 2015
006.3ʹ12–dc23 2014036113
ISBN978-1-107-01789-4Hardback
CambridgeUniversityPresshasnoresponsibilityforthepersistenceoraccuracyofURLsfor
externalorthird-partyInternetwebsitesreferredtointhispublicationanddoesnotguaranteethat
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Contents
Preface page xi
Acknowledgments xv
1 Introduction 1
1.1 SentimentAnalysisApplications 4
1.2 SentimentAnalysisResearch 8
1.2.1 DifferentLevelsofAnalysis 9
1.2.2 SentimentLexiconandItsIssues 10
1.2.3 AnalyzingDebatesandComments 11
1.2.4 MiningIntentions 12
1.2.5 OpinionSpamDetectionandQualityofReviews 12
1.3 SentimentAnalysisasMiniNLP 14
1.4 MyApproachtoWritingThisBook 14
2 TheProblemofSentimentAnalysis 16
2.1 DefinitionofOpinion 17
2.1.1 OpinionDefinition 17
2.1.2 SentimentTarget 19
2.1.3 SentimentofOpinion 20
2.1.4 OpinionDefinitionSimplified 22
2.1.5 ReasonandQualifierforOpinion 24
2.1.6 ObjectiveandTasksofSentimentAnalysis 25
2.2 DefinitionofOpinionSummary 29
2.3 Affect,Emotion,andMood 31
2.3.1 Affect,Emotion,andMoodinPsychology 31
2.3.2 Affect,Emotion,andMoodinSentimentAnalysis 36
2.4 DifferentTypesofOpinions 39
2.4.1 RegularandComparativeOpinions 39
2.4.2 SubjectiveandFact-ImpliedOpinions 40
2.4.3 First-PersonandNon-First-PersonOpinions 44
v
vi Contents
2.4.4 Meta-Opinions 44
2.5 AuthorandReaderStandpoint 45
2.6 Summary 45
3 DocumentSentimentClassification 47
3.1 SupervisedSentimentClassification 49
3.1.1 ClassificationUsingMachineLearningAlgorithms 49
3.1.2 ClassificationUsingaCustomScoreFunction 56
3.2 UnsupervisedSentimentClassification 57
3.2.1 ClassificationUsingSyntacticPatternsandWebSearch 57
3.2.2 ClassificationUsingSentimentLexicons 59
3.3 SentimentRatingPrediction 61
3.4 Cross-DomainSentimentClassification 63
3.5 Cross-LanguageSentimentClassification 65
3.6 EmotionClassificationofDocuments 67
3.7 Summary 68
4 SentenceSubjectivityandSentimentClassification 70
4.1 Subjectivity 72
4.2 SentenceSubjectivityClassification 73
4.3 SentenceSentimentClassification 76
4.3.1 AssumptionofSentenceSentimentClassification 77
4.3.2 ClassificationMethods 78
4.4 DealingwithConditionalSentences 80
4.5 DealingwithSarcasticSentences 82
4.6 Cross-LanguageSubjectivityandSentimentClassification 84
4.7 UsingDiscourseInformationforSentimentClassification 86
4.8 EmotionClassificationofSentences 87
4.9 Discussion 88
5 AspectSentimentClassification 90
5.1 AspectSentimentClassification 91
5.1.1 SupervisedLearning 92
5.1.2 Lexicon-BasedApproach 93
5.1.3 ProsandConsoftheTwoApproaches 96
5.2 RulesofSentimentComposition 98
5.2.1 SentimentCompositionRules 99
5.2.2 DECREASEandINCREASEExpressions 106
5.2.3 SMALL_OR_LESSandLARGE_OR_MOREExpressions 109
5.2.4 EmotionandSentimentIntensity 112
5.2.5 SensesofSentimentWords 112
5.2.6 SurveyofOtherApproaches 114
5.3 NegationandSentiment 116
5.3.1 NegationWords 116
5.3.2 Never 119
Contents vii
5.3.3 SomeOtherCommonSentimentShifters 121
5.3.4 ShiftedorTransferredNegations 122
5.3.5 ScopeofNegations 122
5.4 ModalityandSentiment 123
5.5 CoordinatingConjunctionBut 127
5.6 SentimentWordsinNon-opinionContexts 129
5.7 RuleRepresentation 131
5.8 WordSenseDisambiguationandCoreferenceResolution 133
5.9 Summary 135
6 AspectandEntityExtraction 137
6.1 Frequency-BasedAspectExtraction 138
6.2 ExploitingSyntacticRelations 140
6.2.1 UsingOpinionandTargetRelations 141
6.2.2 UsingPart-ofandAttribute-ofRelations 147
6.3 UsingSupervisedLearning 149
6.3.1 HiddenMarkovModels 150
6.3.2 ConditionalRandomFields 151
6.4 MappingImplicitAspects 153
6.4.1 Corpus-BasedApproach 153
6.4.2 Dictionary-BasedApproach 154
6.5 GroupingAspectsintoCategories 157
6.6 ExploitingTopicModels 159
6.6.1 LatentDirichletAllocation 160
6.6.2 UsingUnsupervisedTopicModels 163
6.6.3 UsingPriorDomainKnowledgeinModeling 168
6.6.4 LifelongTopicModels:LearnasHumansDo 171
6.6.5 UsingPhrasesasTopicalTerms 174
6.7 EntityExtractionandResolution 179
6.7.1 ProblemofEntityExtractionandResolution 179
6.7.2 EntityExtraction 183
6.7.3 EntityLinking 184
6.7.4 EntitySearchandLinking 185
6.8 OpinionHolderandTimeExtraction 186
6.9 Summary 187
7 SentimentLexiconGeneration 189
7.1 Dictionary-BasedApproach 190
7.2 Corpus-BasedApproach 193
7.2.1 IdentifyingSentimentWordsfromaCorpus 194
7.2.2 DealingwithContext-DependentSentimentWords 195
7.2.3 LexiconAdaptation 197
7.2.4 SomeOtherRelatedWork 198
7.3 DesirableandUndesirableFacts 199
7.4 Summary 200
viii Contents
8 AnalysisofComparativeOpinions 202
8.1 ProblemDefinition 202
8.2 IdentifyComparativeSentences 206
8.3 IdentifyingthePreferredEntitySet 207
8.4 SpecialTypesofComparison 209
8.4.1 NonstandardComparison 209
8.4.2 Cross-TypeComparison 211
8.4.3 Single-EntityComparison 212
8.4.4 SentencesInvolvingCompareandComparison 214
8.5 EntityandAspectExtraction 215
8.6 Summary 216
9 OpinionSummarizationandSearch 218
9.1 Aspect-BasedOpinionSummarization 219
9.2 EnhancementstoAspect-BasedSummary 221
9.3 ContrastiveViewSummarization 224
9.4 TraditionalSummarization 225
9.5 SummarizationofComparativeOpinions 225
9.6 OpinionSearch 226
9.7 ExistingOpinionRetrievalTechniques 227
9.8 Summary 229
10 AnalysisofDebatesandComments 231
10.1 RecognizingStancesinDebates 232
10.2 ModelingDebates/Discussions 235
10.2.1 JTEModel 236
10.2.2 JTE-RModel:EncodingReplyRelations 240
10.2.3 JTE-PModel:EncodingPairStructures 243
10.2.4 AnalysisofToleranceinOnlineDiscussions 245
10.3 ModelingComments 246
10.4 Summary 248
11 MiningIntentions 250
11.1 ProblemofIntentionMining 250
11.2 IntentionClassification 254
11.3 Fine-GrainedMiningofIntentions 256
11.4 Summary 258
12 DetectingFakeorDeceptiveOpinions 259
12.1 DifferentTypesofSpam 262
12.1.1 HarmfulFakeReviews 262
12.1.2 TypesofSpammersandSpamming 263
12.1.3 TypesofData,Features,andDetection 265
12.1.4 FakeReviewsversusConventionalLies 267
12.2 SupervisedFakeReviewDetection 269
12.3 SupervisedYelpDataExperiment 272
Contents ix
12.3.1 SupervisedLearningUsingLinguisticFeatures 273
12.3.2 SupervisedLearningUsingBahavioralFeatures 274
12.4 AutomatedDiscoveryofAbnormalPatterns 275
12.4.1 ClassAssociationRules 276
12.4.2 UnexpectednessofOne-ConditionRules 277
12.4.3 UnexpectednessofTwo-ConditionRules 280
12.5 Model-BasedBehavioralAnalysis 282
12.5.1 SpamDetectionBasedonAtypicalBehaviors 282
12.5.2 SpamDetectionUsingReviewGraph 283
12.5.3 SpamDetectionUsingBayesianModels 284
12.6 GroupSpamDetection 285
12.6.1 GroupBehaviorFeatures 288
12.6.2 IndividualMemberBehaviorFeatures 290
12.7 IdentifyingReviewerswithMultipleUserids 291
12.7.1 LearninginaSimilaritySpace 292
12.7.2 TrainingDataPreparation 293
12.7.3 d-Featuresands-Features 294
12.7.4 IdentifyingUseridsoftheSameAuthor 295
12.8 ExploitingBurstinessinReviews 298
12.9 SomeFutureResearchDirections 300
12.10 Summary 301
13 QualityofReviews 303
13.1 QualityPredictionasaRegressionProblem 303
13.2 OtherMethods 305
13.3 SomeNewFrontiers 306
13.4 Summary 307
14 Conclusions 309
Appendix 315
Bibliography 327
Index 363