Emotion Mining Promotionsvortrag English

7/29/2019 Emotion Mining Promotionsvortrag English

1/5

1MultimediaConcepts

andApplications

AlexanderOsherenko

OpinionMiningand

LexicalAffectSensing

PromotionsvortragAlexanderOsherenko

Betreuer:Prof.Dr.ElisabethAndre,Prof.Dr.Dr.WolfgangMinker

30.06.2010

2MultimediaConcepts

andApplications

AlexanderOsherenko

Outline

Introduction

Challenges Researchquestions

Previous approaches

Studiedapproaches Statistical

Semantic

Hybrid

Viafusion

Summary Contributions

Outlook

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OpinionMining

Movie Review (long text) www.reelviews.net

Grammatically correct text Definitely expressed opinion,but emotionally different

words

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AffectRecognition

Naturallanguage utterances (short text)

Notalways grammatically correct text

Repetitions,repairs,fill words,incorrect wordings

Textis important,but not everything

- We have, Prudence.

- I m okay.

- Er m, wel l , i t s beenr easonabl e day so f ar.Er m, bi t bor i ng, but ,er, hopef ul l y t he day

wi l l pi ck up.

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Challenges

Bigvariability inexpression ofemotions Speaker andautorspecific

Situationspecific

Genrespecific:movie reviews,chats,emals etc.

Emotions are expressed not always clearly Irony

UnterdrckteEmotions

MixedEmotions

Corporaare difficult toobtain Many texts andtalks dont contain emotions that are interesting for us

It is not always easy toEsistnichtimmereinfach,eineGrundwahrheitzufinden.


AlexanderOsherenko

Challenges(Software)

Recognition

Simulation

Modelling

According to taxonomyof applications usingemotional awareness (Batliner et al., 2006):

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Challenges(Applications)

Opinion Mining

Sort documents not according tothe topic,but ratheraccording tothe opinion

Emotionrecognition incall centers Forchoosing the appropriate dialogue strategy Should the caller speak with ahumanoperator?

Emotionrecognition inacar Entertainmentsoftware considers the emotionalstate of

the driver andherdriving style


AlexanderOsherenko

Emotionmodels

Discrete categories

Forinstance,Ekmancategories(1999):Wut,Abscheu,Furcht,Freude,Trauer,berraschung

Continous emotions

Representation through thedimensions (for instance,Erregungundvalence,orEvaluationandactivation)

joy

Higherarousal

Positive valence

surprise

sadness

Lowerarousal

disgust

fear

Negative valence

anger

affection

bored

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AlexanderOsherenko

Emotionsinthethesis

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negative positive

Mapping of continiousemotions onto discretecategories


AlexanderOsherenko

Existingapproaches Informationclassification

Statistical approach:

Movie reviews:[Pang etal.,2002],[Pang,B.,Lee,L.2004]

Product reviews:[Daveetal.,2003]

Weblogs:[Riloff etal.,2006]

Articles from newspapers:[Diederichetal.,2000]

Conversation abstracts:[MairesseF.etal.,2007]

Semantic approach:

Sentences from weblogs:[Neviarouskaya etal.,2007]

Acoustic approaches:

Berlindatabase,Danish corpus,SmartKom corpus:[Vogtetal.,2008]

Lexical,stylometric,acoustic features

Emotionwords,negations, intensifers

Nosystematic combination ofinformation

Nostudy ofmultipletextgenres

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Researchquestionsaccordingto

emotionrecognitionfromspeech1. What linguistic features should be extracted for

automatic opinion mining andhow toevaluatethem?

2. Datadriven or knowledgebased emotionrecognition?

3. How could other modalities,for instance,acousticinformation contribute toimprovement ofrecognition rates?


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Studiedcorpora

12

215movie reviews04stars inincrement 0.5stars

Movie reviewsBMRC

759sentencesPositive,negative,unclassifiable

Englishsentences

Fifty WordFiction

(FWF)

Genre Emotionclasses Data amount

Pang MovieReview

Movie reviews Positive,negative 2000moviereviews

SensitiveArtificial

Listener (SAL)

Naturallanguagedialogues

Positiveaktive,negativeaktive,positivpassive,negativepassive,neutral

574uerungen

CwPR Productreviews

1 5stars 300productreviews

BMRCS Englishsentences

Positiveaktive,negativeaktive,positivpassive,negativepassive,neutral

1010sentences

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Mainideaofthethesis Noexplicit rules for mapping texts onto emotions

Statistical Approach Extract relevantfeatures from texts andtrain classifiers

Emotionrecognition difficult without meaningconsideration Semantic Approach

Search for emotionalpatterns inrelevantparts ofsentences andmap them onto emotions

Combination ofthe semantic andthe statisticapproaches HybridApproach

Classification improvement through consideration ofadditionalmodalities Fusion


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Statisticalapproach

Learning phase

14

Testing phase

Feature extraktion/Feature evalutation

(Preprocessing)Learningdata

Classifiertraining

OpinionTestingdata

Classification

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StatisticalApproach(Dissertation) Corpora(2,5,5and9classes)

Features Lexikalical features:

(Lemmatized)words inthe frequency list,Whissell,BNC

Stylometricfeatures: Featuressuchasstatndard deviation ofword lengths,ofsentence lengths,

digramsetc.

Deictic features:

Timeandlocation references,pronouns,stopwordsetc.

Grammatical features: Interjections,repetitions etc.

Klassifizierung(SVM)1530.06.2010 16MultimediaConcepts

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SALresults

Bestresults:words,buttheirnumberisverybig Wordfeaturesarenotknownforeverycorpusincontrasttoother

featuregroups16

31.35%Grammatical features

59.65%Deictic features

58.97%Stylometrical features

59.6%Lemmatized word lists

60.21%Non-lemmatized word lists

SALCorpus/Features

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andApplications

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SemanticApproach

Recognition oftypical patterns inemotionalutterances

Interjections:Oh!It is disgusting!

Repetitions:It is very very expensive!

Intensifiers:It is very unplesant!

Negations:Nomovie is sogood asthis one!vs. Itis not agoodmovie.


AlexanderOsherenko

SemanticApproach(Dissertation)

18

Syntactic Analysis- Stanford Parser -

Semantic Analysis- SPIN Parser-

I am nothappy.

Output of Stanford Parser: (ROOT (S (NP (PRP I)) (VP(VBP am) (RB not) (ADJ P (J J happy))) (. .)))

Output of SPIN parser: Negation(not) EmotionalWord(happy) EmotionalPhrase(semCat: low_neg)

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FWFresults

Statisticalapproach:37.20%19

44.22Average

45.21Last phrase

44.79First phrasePhrases

46.04Average

47.24Last phrase

47.20First phraseSubsentences

42.79Average

47.45Last phrase

45.41First phraseWhole text

45.92Average

47.64Last phrase

47.20First phraseMajority

RStrategyGranularity


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HybridApproach

Longtexts

20

Shorttexts

Result:better than statistic approach but worse than semantic

approach

Statistical analysisSemantic analysisSentences Opinion

Statistic analysisSemanticanalysisSentence Emotion

Semanticanalysis

Statistic analysisSentence Emotion

Result doublechoice by chance

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Fusion

Featurefusion:combines features from differentmodalities

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Classifier

Classifier

Acoustic features

Linguistic features

Choice

Deicision fusion:makes choice ofdecisions ofmultipleclassifiers

Classifier

Acoustic features

Linguistic features


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1. Corpus(additionally acoustic information)

2. FeatureandDecision fusion

3. Visualization astree

Fusionis beneficial especially if nolanguage context isconsidered.

Fusion(Dissertation)

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andApplications

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Contributions

Comprehensive analysis ofapproaches toopinion miningandlexical affect sensing using differentcorporarealization inanew software

Extraction andevaluation offeatures toopinion mining andlexical affect sensing

Differentiated semantic approach

Implementation ofintroduced approaches inEmoText

Hybridapproach

Multimodalfusion


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Outlook

1. Newmodalities

2. Application development

3. Combinated emotion andpersonality modeling

BigFive

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Dissertationdefence

Thank you!

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Emotion Mining Promotionsvortrag English

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