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Conference Paper: Learning domain-specific sentiment lexicon with supervised sentiment-aware LDA

TitleLearning domain-specific sentiment lexicon with supervised sentiment-aware LDA
Authors
Issue Date2014
PublisherIOS Press.
Citation
The 21st European Conference on Artificial Intelligence (ECAI 2014), Prague, Czech Republic, 18-22 August 2014. In Frontiers in Artificial Intelligence and Applications, 2014, v. 263: ECAI 2014, p. 927-932 How to Cite?
AbstractAnalyzing and understanding people's sentiments towards different topics has become an interesting task due to the explosion of opinion-rich resources. In most sentiment analysis applications, sentiment lexicons play a crucial role, to be used as metadata of sentiment polarity. However, most previous works focus on discovering general-purpose sentiment lexicons. They cannot capture domain-specific sentiment words, or implicit and connotative sentiment words that are seemingly objective. In this paper, we propose a supervised sentiment-aware LDA model (ssLDA). The model uses a minimal set of domain-independent seed words and document labels to discover a domain-specific lexicon, learning a lexicon much richer and adaptive to the sentiment of specific document. Experiments on two publicly-available datasets (movie reviews and Obama-McCain debate dataset) show that our model is effective in constructing a comprehensive and high-quality domain-specific sentiment lexicon. Furthermore, the resulting lexicon significantly improves the performance of sentiment classification tasks. © 2014 The Authors and IOS Press.
DescriptionFrontiers in Artificial Intelligence and Applications, v. 263 entitled: ECAI 2014: 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014)
Persistent Identifierhttp://hdl.handle.net/10722/219239
ISBN
ISSN
2015 SCImago Journal Rankings: 0.162
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, M-
dc.contributor.authorZhu, D-
dc.contributor.authorRashed, M-
dc.contributor.authorChow, KP-
dc.date.accessioned2015-09-18T07:18:33Z-
dc.date.available2015-09-18T07:18:33Z-
dc.date.issued2014-
dc.identifier.citationThe 21st European Conference on Artificial Intelligence (ECAI 2014), Prague, Czech Republic, 18-22 August 2014. In Frontiers in Artificial Intelligence and Applications, 2014, v. 263: ECAI 2014, p. 927-932-
dc.identifier.isbn978-1-61499-418-3-
dc.identifier.issn0922-6389-
dc.identifier.urihttp://hdl.handle.net/10722/219239-
dc.descriptionFrontiers in Artificial Intelligence and Applications, v. 263 entitled: ECAI 2014: 21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014)-
dc.description.abstractAnalyzing and understanding people's sentiments towards different topics has become an interesting task due to the explosion of opinion-rich resources. In most sentiment analysis applications, sentiment lexicons play a crucial role, to be used as metadata of sentiment polarity. However, most previous works focus on discovering general-purpose sentiment lexicons. They cannot capture domain-specific sentiment words, or implicit and connotative sentiment words that are seemingly objective. In this paper, we propose a supervised sentiment-aware LDA model (ssLDA). The model uses a minimal set of domain-independent seed words and document labels to discover a domain-specific lexicon, learning a lexicon much richer and adaptive to the sentiment of specific document. Experiments on two publicly-available datasets (movie reviews and Obama-McCain debate dataset) show that our model is effective in constructing a comprehensive and high-quality domain-specific sentiment lexicon. Furthermore, the resulting lexicon significantly improves the performance of sentiment classification tasks. © 2014 The Authors and IOS Press.-
dc.languageeng-
dc.publisherIOS Press.-
dc.relation.ispartofFrontiers in Artificial Intelligence and Applications-
dc.rights© 2014 The Authors and IOS Press.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleLearning domain-specific sentiment lexicon with supervised sentiment-aware LDA-
dc.typeConference_Paper-
dc.identifier.emailChow, KP: kpchow@hkucc.hku.hk-
dc.identifier.authorityChow, KP=rp00111-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3233/978-1-61499-419-0-927-
dc.identifier.scopuseid_2-s2.0-84923195301-
dc.identifier.hkuros255012-
dc.identifier.volume263: ECAI 2014-
dc.identifier.spage927-
dc.identifier.epage932-
dc.identifier.isiWOS:000349444700156-
dc.publisher.placeNetherlands-
dc.customcontrol.immutablesml 151230-

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