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- Publisher Website: 10.3233/978-1-61499-419-0-927
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Conference Paper: Learning domain-specific sentiment lexicon with supervised sentiment-aware LDA
Title | Learning domain-specific sentiment lexicon with supervised sentiment-aware LDA |
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Authors | |
Issue Date | 2014 |
Publisher | IOS 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? |
Abstract | Analyzing 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. |
Description | Frontiers 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 Identifier | http://hdl.handle.net/10722/219239 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.281 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yang, M | - |
dc.contributor.author | Zhu, D | - |
dc.contributor.author | Rashed, M | - |
dc.contributor.author | Chow, KP | - |
dc.date.accessioned | 2015-09-18T07:18:33Z | - |
dc.date.available | 2015-09-18T07:18:33Z | - |
dc.date.issued | 2014 | - |
dc.identifier.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 | - |
dc.identifier.isbn | 978-1-61499-418-3 | - |
dc.identifier.issn | 0922-6389 | - |
dc.identifier.uri | http://hdl.handle.net/10722/219239 | - |
dc.description | Frontiers 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.abstract | Analyzing 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.language | eng | - |
dc.publisher | IOS Press. | - |
dc.relation.ispartof | Frontiers in Artificial Intelligence and Applications | - |
dc.rights | © 2014 The Authors and IOS Press. | - |
dc.title | Learning domain-specific sentiment lexicon with supervised sentiment-aware LDA | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Chow, KP: kpchow@hkucc.hku.hk | - |
dc.identifier.authority | Chow, KP=rp00111 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3233/978-1-61499-419-0-927 | - |
dc.identifier.scopus | eid_2-s2.0-84923195301 | - |
dc.identifier.hkuros | 255012 | - |
dc.identifier.volume | 263: ECAI 2014 | - |
dc.identifier.spage | 927 | - |
dc.identifier.epage | 932 | - |
dc.identifier.isi | WOS:000349444700156 | - |
dc.publisher.place | Netherlands | - |
dc.customcontrol.immutable | sml 151230 | - |
dc.identifier.issnl | 0922-6389 | - |