Abstract
We present the study of sentiment classification of Chinese contrast sentences in this paper, which are one of the commonly used language constructs in text. In a typical review, there are at least around 6% of such sentences. Due to the complex contrast phenomenon, it is hard to use the traditional bag-of-words to model such sentences. In this paper, we propose a Two-Layer Logistic Regression (TLLR) model to leverage such relationship in sentiment classification. According to different connectives, our model can treat different clauses differently in sentiment classification. Experimental results show that TLLR model can effectively improve the performance of sentiment classification of Chinese contrast sentences.
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References
Balahur, A., Mihalcea, R., Montoyo, A.: Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications. Computer Speech & Language 28(1), 1–6 (2014)
Heerschop, B., Goossen, F., Hogenboom, A., Frasincar, F., Kaymak, U., de Jong, F.: Polarity analysis of texts using discourse structure. In: Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), pp. 1061–1070 (2011)
Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22(2), 110–125 (2006)
Kim, S.-M., Hovy, E.: Determining the Sentiment of Opinions. In: Proceeding of the Intenerational Conference of Computational Linguistics (COLING), pp. 1367–1373 (2004)
Li, S., Huang, C.-R.: Sentiment Classification Considering Negation and Contrast Transition. In: Proceedings of the Pacific Asia Conference on Language, Information, and Computation, PACLIC (2009)
Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Narayanan, R., Liu, B., Choudhary, A.: Sentiment Analysis of Conditional Sentences. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2009)
Pang, B., Lee, L., Vaithyanatha, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Pang, B., Lee, L.: A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pp. 271–278 (2004)
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval 2(2), 1–135 (2008)
Taboada, M., Voll, K., Brooke, J.: Extracting sentiment as a function of discourse structure and topicality. Simon Fraser University, Tech. Rep, 20 (2008)
Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pp. 417–424 (2002)
Wang, F., Wu, Y., Qiu, L.: Exploiting Discourse Relations for Sentiment Analysis. In: Proceeding of the Intenerational Conference of Computational Linguistics (COLING), pp. 1311–1319 (2012)
Xia, R., Hu, X., Lu, J., Yang, J., Zong, C.: Instance Selection and Instance Weighting for Cross- Domain Sentiment Classification via PU Learning. In: Proceedings of the International Joint Conference on Artificial Intelligence(IJCAI), pp. 2176–2182 (2013)
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Li, J., Zhou, Y., Liu, C., Pang, L. (2014). Sentiment Classification of Chinese Contrast Sentences. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_19
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DOI: https://doi.org/10.1007/978-3-662-45924-9_19
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