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.
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References
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning Techniques. In: Proceedings of EMNLP (2002)
Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of ACL (2002)
Tan, S., Zhang, J.: An empirical study of sentiment analysis for chinese documents. Expert Systems with Applications 34(4), 2622–2629 (2008)
Tan, S., Cheng, X., et al.: A novel refinement approach for text categorization. In: Proceedings of the ACM CIKM (2005)
Dasgupta, S., Ng, V.: Mine the Easy and Classify the Hard: Experiments with Automatic Sentiment Classification. In: Proceedings of ACL (2009)
Yessenalina, A., Yue, Y., Cardie, C.: Multi-level Structured Models for Document Sentiment Classification. In: Proceedings of EMNLP (2010)
Mullen, T., Collier, N.: Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of EMNLP (2004)
Michael, G.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: International Conference on Computational Linguistics (2004)
Koppel, M., Schler, J.: The importance of neutral examples for learning sentiment. Computational Intelligence 22(2), 100–109 (2006)
McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured Models for Fine-to-coarse Sentiment Analysis. In: Proceedings of ACL (2007)
Shoushan, L., et al.: Employing Personal/Impersonal Views in Supervised and Semi-supervised Sentiment Classification. In: Proceedings of ACL (2010)
Zhu, X.: Semi-supervised learning literature survey. University of Wisconsin, Madison (2007)
Goldberg, A.B., Zhu, X.: Seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization. In: Proceedings of TextGraphs: the First Workshop on Graph Based Methods for Natural Language (2006)
Li, T., Zhang, Y., et al.: A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge. In: Proceedings of ACL (2009)
Becker, I., Aharonson, V.: Last but Definitely not Least: On the Role of the Last Sentence in Automatic Polarity-Classification. In: Proceedings of ACL (2010)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of ACL (2007)
Joachims, T.: Transductive Inference for Text Classification using Support Vector Machines. In: ICML (1999)
Tan, S., Cheng, X., Wang, Y., Xu, H.: Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 337–349. Springer, Heidelberg (2009)
Tan, S., Wang, Y., Wu, G., Cheng, X.: A novel scheme for domain-transfer problem in the context of sentiment analysis. In: CIKM 2007 (2007)
Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Syst. Appl. 36(7), 10760–10773 (2009)
Tan, S.: An effective refinement strategy for KNN text classifier. Expert Syst. Appl. 30(2), 290–298 (2006)
Tan, S., Wang, Y., Wu, G.: Adapting centroid classifier for document categorization. Expert Syst. Appl. 38(8), 10264–10273 (2011)
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Lin, Z., Tan, S., Cheng, X. (2011). Using Key Sentence to Improve Sentiment Classification. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_38
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DOI: https://doi.org/10.1007/978-3-642-25631-8_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25630-1
Online ISBN: 978-3-642-25631-8
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