Chinese Sentiment Classification Based on the Sentiment Drop Point
The exploding Web opinion data has the essential need for automatic tools to analyze people’s sentiments in many fields. Predicting the polarity of a product review is an important work in applications such as market investigation and trend analysis. In this paper, we focus on analyzing the Chinese sentiment word strengths and the sentiment drop point. We propose a novel algorithm based on the sentiment drop point algorithm to conduct sentiment polarity assignment. It predicts the sentiment polarity by a determinative policy which involves two classifiers simultaneously. The experiments show that our approach is efficient and suited for reviews analysis in different domains.
KeywordsSentiment drop point Sentiment strength Normalized Google distance
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- 1.Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics (2004)Google Scholar
- 2.Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)Google Scholar
- 3.Zagibalov, T., Carroll, J.: Automatic seed word selection for unsupervised sentiment classification of Chinese text. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 1073–1080. Association for Computational Linguistics (2008)Google Scholar
- 4.Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Annual Meeting-Association for Computational Linguistics 43(1), 115 (2005)Google Scholar
- 6.Wan, X.: Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 553–561. Association for Computational Linguistics (2008)Google Scholar
- 8.Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology (2012)Google Scholar
- 9.Lu, Y., Kong, X., Quan, X., Liu, W., Xu, Y.: Exploring the sentiment strength of user reviews. Web-Age Information Management, 471–482 (2010)Google Scholar
- 12.Zhang, J., Tang, J., Li, J.: Expert finding in a social network. Advances in Databases: Concepts. Systems and Applications, 1066–1069 (2007)Google Scholar