Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Yulan He, Harith Alani, Chenghua Lin

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Abstract

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning.
Original languageEnglish
Title of host publicationProceeding : HLT '11 proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies
EditorsDekang Lin
Place of PublicationStroudsburg, PA (US)
PublisherAssociation for Computational Linguistics
Pages123-131
Number of pages9
Volume1
ISBN (Print)978-1-932432-87-9
Publication statusPublished - 2011
Event49th annual meeting of the association for computational linguistics, HLT '11 - Portland, OR, United States
Duration: 19 Jun 201124 Jun 2011

Conference

Conference49th annual meeting of the association for computational linguistics, HLT '11
CountryUnited States
CityPortland, OR
Period19/06/1124/06/11

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  • Cite this

    He, Y., Alani, H., & Lin, C. (2011). Automatically extracting polarity-bearing topics for cross-domain sentiment classification. In D. Lin (Ed.), Proceeding : HLT '11 proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies (Vol. 1, pp. 123-131). Association for Computational Linguistics.