一种面向观点挖掘的多粒度话题情感联合模型

doi:10.3969/j.issn.1001-2400.2011.03.030

Abstract

Abstract:

Based on extensions to standard topic modeling methods,a novel multi-grain joint model of topic and sentiment is proposed to improve efficiency of opinion mining. This model extracts sentiment and hierarchy topic from the text simultaneously, which distinguishes between local topics and global topics. The proposed model adopts the unsupervised learning method to address the issue of being domain dependent in existing methods. According to experiments, this model achieves an accuracy of 82.6% for sentiment classification. It has a performance comparable to that of supervised sentiment classification methods. Moreover, the acquired collection of topics is hierarchy and semantic related. It is proved that the proposed model is feasible and effective for opinion mining.

Key words: opinion mining, topic model, multi-grain joint topic/sentiment model, unsupervised learning, Monte Carlo simulation

ZHAO Yu；CAI Wandong. Multi-grain joint model of topic and sentiment for opinion mining[J].J4, 2011, 38(3): 181-188.

References

［1］ Kumar R, Novak J. On the Bursty Evolution of Blogspace ［C］//The Twelfth International World Wide Web Conference. Budapest: ACM, 2003: 568-576.
［2］ Gruhl D, Guha R, Kumar R, et al. The Predictive Power of Online Chatter ［C］//The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago: ACM, 2005: 78-87.
［3］ Mei Q, Zhai C. Discovering Evolutionary Theme Patterns from Text-An Exploration of Temporal Text Mining ［C］//Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia: ACM, 2005: 198-207.
［4］ Pang B, Lee L. Opinion Mining and Sentiment Analysis ［J］. Foundations and Trends in Information Retrieval, 2008, 2(1-2): 1-135.
［5］ Tang H, Tan S, Cheng X. A Survey on Sentiment Detection of Reviews ［J］. Expert Systems with Applications, 2009, 36(7): 10760-10773.
［6］ Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment Classification Using Machine Learning Techniques ［C］//Proceedings of EMNLP 2002. Philadelphia: ACL, 2002: 79-86.
［7］ Tan S, Zhang J. An Empirical Study of Sentiment Analysis for Chinese Documents ［J］. Expert Systems with Applications, 2008, 34(4): 2622-2629.
［8］ Carenini G, Ng R, Pauls A. Multi-Document Summarization of Evaluative Text ［C］//Proceedings of the 11th European Chapter of the Association for Computational Linguistics. Trento: ACL, 2006: 3-7.
［9］ Zhuang L, Jing F, Zhu X. Movie Review Mining and Summarization ［C］//The 15th ACM International Conference on Information and Knowledge Management. Virginia: ACM, 2006: 43-50.
［10］ Hu M, Liu B. Mining Opinion Features in Customer Reviews ［C］//Proceedings of 19th National Conference on Artificial Intellgience. California: AAAI, 2004: 755-760.
［11］ Carenini G, Ng R T, Zwart E. Extracting Knowledge from Evaluative Text ［C］//Proceedings of the 3rd International Conference on Knowledge Capture 2005. Alberta: ACM, 2005: 11-18.
［12］ Hu M, Liu B. Mining and Summarizing Customer Reviews ［C］//The 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2004. Washington: ACM, 2004: 168-177.
［13］苏祺. 面向问答系统的情感倾向分析研究［D］. 北京: 北京大学, 2006.
［14］ Mei Q, Ling X, Wondra M, et al. Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs ［C］//Proceedings of the World Wide Conference 2007. Alberta: ACM, 2007: 171-180.
［15］ Lin C, He Y. Joint Sentiment/Topic Model for Sentiment Analysis ［C］//The 18th ACM Conference on Information and Knowledge Management. Hong Kong: ACM, 2009: 375-384.
［16］ Titov I, McDonald R. Modeling Online Reviews with Multi-Grain Topic Models ［C］//The 17th International World Wide Web Conference 2008. Beijing: ACM, 2008: 111-120..
［17］ Titov I, McDonald R. A Joint Model of Text and Aspect Ratings for Sentiment Summarization ［C］//The 46th Meeting of Association for Computational Linguistics. Columbus: ACL, 2008: 308-316.
［18］ Hofmann T. Unsupervised Learning by Probabilistic Latent Semantic Analysis ［J］. Machine Learning, 2001(42): 177-196.
［19］ Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation ［J］. Journal of Machine Learning Research, 2003, 3(4-5): 993-1022.
［20］ Bishop C M. Pattern Recognition and Machine Learning ［M］. Singapore: Springer Science, 2006.
［21］ Griffiths T, Steyvers M. Finding Scientific Topics ［C］//Proceedings of the National Academy of Sciences. Stanford: United States National Academy of Sciences, 2004: 5228-5235.
［22］ Chang C C, Lin C J. LIBSVM: a Library for Support Vector Machines ［CP/OL］. ［2001-10-12］. www.csie.ntu.edu.tw/~cjlin/libsvm/.

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Multi-grain joint model of topic and sentiment for opinion mining

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