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Conference Paper: LCCT: a semisupervised model for sentiment classification

TitleLCCT: a semisupervised model for sentiment classification
Authors
Issue Date2015
PublisherAssociation for Computational Linguistics (ACL).
Citation
The 2015 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL- HLT), Denver, CO., 31 May-5 June 2015. In Conference Proceedings, 2015, p. 546-555 How to Cite?
AbstractAnalyzing public opinions towards products, services and social events is an important but challenging task. An accurate sentiment analyzer should take both lexicon-level information and corpus-level information into account. It also needs to exploit the domain-specific knowledge and utilize the common knowledge shared across domains. In addition, we want the algorithm being able to deal with missing labels and learning from incomplete sentiment lexicons. This paper presents a LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification. The proposed method combines the idea of lexicon-based learning and corpus-based learning in a unified co-training framework. It is capable of incorporating both domain-specific and domain-independent knowledge. Extensive experiments show that it achieves very competitive classification accuracy, even with a small portion of labeled data. Comparing to state-of-the-art sentiment classification methods, the LCCT approach exhibits significantly better performances on a variety of datasets in both English and Chinese. © 2015 Association for Computational Linguistics
DescriptionConference Theme: Human Language Technologies
Persistent Identifierhttp://hdl.handle.net/10722/219237

 

DC FieldValueLanguage
dc.contributor.authorYang, M-
dc.contributor.authorTu, W-
dc.contributor.authorLu, Z-
dc.contributor.authorYin, W-
dc.contributor.authorChow, KP-
dc.date.accessioned2015-09-18T07:18:30Z-
dc.date.available2015-09-18T07:18:30Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL- HLT), Denver, CO., 31 May-5 June 2015. In Conference Proceedings, 2015, p. 546-555-
dc.identifier.urihttp://hdl.handle.net/10722/219237-
dc.descriptionConference Theme: Human Language Technologies-
dc.description.abstractAnalyzing public opinions towards products, services and social events is an important but challenging task. An accurate sentiment analyzer should take both lexicon-level information and corpus-level information into account. It also needs to exploit the domain-specific knowledge and utilize the common knowledge shared across domains. In addition, we want the algorithm being able to deal with missing labels and learning from incomplete sentiment lexicons. This paper presents a LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification. The proposed method combines the idea of lexicon-based learning and corpus-based learning in a unified co-training framework. It is capable of incorporating both domain-specific and domain-independent knowledge. Extensive experiments show that it achieves very competitive classification accuracy, even with a small portion of labeled data. Comparing to state-of-the-art sentiment classification methods, the LCCT approach exhibits significantly better performances on a variety of datasets in both English and Chinese. © 2015 Association for Computational Linguistics-
dc.languageeng-
dc.publisherAssociation for Computational Linguistics (ACL).-
dc.relation.ispartofHuman Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL-
dc.rightsHuman Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL. © 2015 Association for Computational Linguistics-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleLCCT: a semisupervised model for sentiment classification-
dc.typeConference_Paper-
dc.identifier.emailChow, KP: kpchow@hkucc.hku.hk-
dc.identifier.authorityChow, KP=rp00111-
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros255003-
dc.identifier.spage546-
dc.identifier.epage555-
dc.publisher.placeUnited States-

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