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Emotion Mining Promotionsvortrag English
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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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AlexanderOsherenko
OpinionMining
Movie Review (long text) www.reelviews.net
Grammatically correct text Definitely expressed opinion,but emotionally different
words
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AlexanderOsherenko
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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andApplications
AlexanderOsherenko
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.
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Challenges(Software)
Recognition
Simulation
Modelling
According to taxonomyof applications usingemotional awareness (Batliner et al., 2006):
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andApplications
AlexanderOsherenko
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
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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
9
negative positive
Mapping of continiousemotions onto discretecategories
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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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andApplications
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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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AlexanderOsherenko
Studiedcorpora
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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
andApplications
AlexanderOsherenko
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
AlexanderOsherenko
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.
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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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andApplications
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Dissertationdefence
Thank you!
30.06.2010