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Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu
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Diversiweb2011 04 Expressing Opinion Diversity - Delia Rusu

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  • 1. Expressing Opinion DiversityAndreea Bizău – Babeș Bolyai University,Cluj-Napoca, RomaniaDelia Rusu, Dunja Mladenić - Jožef StefanInstitute, Ljubljana, Slovenia ailab.ijs.si
  • 2. Motivation: Opinion Diversity ailab.ijs.si
  • 3. OverviewTerminologyRelated workDomain Driven Opinion VocabularyUse Case: Twitter Movie CommentsConclusions and Future Work ailab.ijs.si
  • 4. Terminologyopinion - a subjective expression of sentiments,appraisals or feelingsopinion words - a set of keywords/phrases usedin expressing an opinionorientation of an opinion word indicates whetherthe opinion expressed is positive, negative orneutralthe totality of opinion words forms an opinionvocabulary ailab.ijs.si
  • 5. Related Work – Opinion VocabularyDictionary based Esuli and Sebastiani, 2006 - SentiWordNet: three numerical scores (objective, positive, negative)Corpus based Kanayama and Nasukawa, 2006 - context coherency (same polarity tend to appear successively) Jialin Pan et al, 2010 - feature bipartite graph modeling the relationship between domain-specific words and domain- independent words.Dictionary and Corpus based Jijkoun et al, 2010 - dependency parsing on a set of relevant documents, extracting patterns of the form (clue word, syntactic context, target of sentiment) ailab.ijs.si
  • 6. Related Work – Opinion AnalysisApproaches: Hatzivassiloglou et al, 1997 – relevance of using connectives (conjunctions: and, or, but, etc) Kim and Hovy, 2004 – use word seed lists and WordNet synsets to determine the strength of the opinion orientation for the identified opinion words Gamon and Aue, 2005 – assign opinion orientation to candidate words, assuming that opinion terms with similar orientation tend to co-occurTwitter Applications: Pang and Lee, 2002 – classifying movie reviews by overall document sentiment Asur and Huberman, 2010 – sentiments extracted from Twitter can be used to build a prediction model for box- office revenue ailab.ijs.si
  • 7. Our Approach Domain-specific IMDb Movie reviews opinion (sample) vocabulary Weird, odd, 2 Clusters bad Weird, odd IMDb Movie reviews amazing, amazing, (Training data) awesome, awesome perfect, fantasticVocabulary Twitter comments analysis applied to Movie tweets (Test data) ailab.ijs.si
  • 8. Domain Driven Opinion VocabularyGiven a positive word seed list anda negative word seed list, expandthe initial seed lists using synonymy Domain-specific/ antonymy relations (WordNet) opinion vocabulary the initial words will be assigned a score of 1 for positive words and -1 for negative words Weird, odd,Given a corpus, parse and extract bad 2 Clustersall adjectives and conjunctions – Weird,obtain a graph with two types of odd amazing,relationships between nodes: awesome, amazing, same context orientation (words awesome perfect, connected by and, or, nor) or fantastic opposite context orientation (words connected by but, yet)Clean the resulting set of words andrelationship graph by removing stopwords and self-reference relations ailab.ijs.si
  • 9. Domain Driven Opinion Vocabulary Some of the characters are fictitious, but not grotesque synonym fictional fictitious real score(“fictitious”) = max(s_syn, s_ant) score(“fictitious”) = s_syn antonym score(“fictitious”) = - 0.3874 if f = 0.9fictitious but grotesque – ContextOpposite relationshipfictitious but not grotesque – ContextSame relationship ailab.ijs.si
  • 10. Domain Driven Opinion Vocabulary4. Propagate scores through the graph, by determining for each word w Positivity score sPos Negativity score sNeg if relij is a ContextSame relation sPos(wi) += weigth(relij) * prevSPos(wj) sNeg (wi) += weigth(relij) * prevSNeg(wj) else if relij is a ContextOpposite relation sPos(wi) += weigth(relij) * prevSNeg(wj) sNeg (wi) += weigth(relij) * prevSPos(wj) ailab.ijs.si
  • 11. Use Case: Twitter Movie Reviewsdomain specific document corpus of 27,886 IMDbmovie reviewsdomain specific opinion vocabulary: 9,318 words: 4,925 have a negative orientation and 4393 have a positive orientation Inception (2010) Meet the Spartans (2008)Positive words: good, great, Positive words: funny, awesome,awesome, amazing, favorite, fantastic, greatincredible, thrilling, different,speechless Negative words: bad, stupid, dumb, weird, silly, common, ridiculous,Negative words: bad, confusing, terribleweird, stupid, dumb, boring,predictable, horrible, disappointing ailab.ijs.si
  • 12. Use Case: Twitter Movie Reviews220,387 tweets crawled over a two monthinterval, keyed on 84 movies Movie Genre Tweets IMDb Our score scoreInception (2010) mystery, sci-fi, 19,256 8.9 66.52 thriller Megamind animation, comedy, 8,109 7.3 67.71 (2010) family Unstoppable drama, thriller 15,349 7 63.67 (2010) Burlesque drama, music, 1,244 6.2 70.78 (2010) romance Meet the comedy, war 44 2.5 40.67Spartans (2008) ailab.ijs.si
  • 13. Twitter comments analysis• Sentiment words distribution for a movie• Sentiment orientation evolution per week, day, hour• Movie comparison ailab.ijs.si
  • 14. Conclusions and Future Workidentifying opinion diversity expressed within text,with the aid of a domain-specific vocabularyprocessing a corpus of IMDb movie reviews,generated an opinion lexicon and analyzed adifferent opinion source corpus, i.e. a tweet collectionfurther extend our algorithm to include opinion wordsexpressed by verbs and adverbs, as well as morecomplex expressionsconduct experiments in order to determine the correlation between positive opinion words for a given movie and the IMDb movie rating evaluate the opinion lexicon directly ailab.ijs.si
  • 15. Thank You for Your Attention! ailab.ijs.si

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