Using key sentence to improve sentiment classification

Authors:
Zheng Lin;Songbo Tan;Xueqi Cheng
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Year:
2011

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Abstract

When predicting the polarity of a review, not all sentences are equally informative. In this paper, we divide a document into key sentence and trivial sentences. The key sentence expresses the author's overall view while trivial sentences describe the details. To take full advantage of the differences and complementarity between the two kinds of sentences, we incorporate them in supervised and semi-supervised learning respectively. In supervised sentiment classification, a classifier combination approach is adopted; in semi-supervised sentiment classification, a co-training algorithm is proposed. Experiments carried out on eight domains show that our approach performs better than the baseline method and the key sentence extraction is effective.