Joint Aspect Discovery, Sentiment Classification, Aspect-Level Ratings and Weights Approximation for Recommender Systems by Rating Supervised Latent Topic Model
Wei Ou and Van-Nam Huynh
Japan Advanced Institute of Science and Technology
1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
Textual product reviews posted by previous shoppers have been serving as an important source of information that helps on-line shoppers to make their decisions. However, reading through all the reviews of a product is usually a time-demanding and frustrating task, especially when those reviews deliver conflicting information. Therefore, it is of great practical value to develop techniques to automatically generate brief but accurate summaries for the numerous reviews on shopping websites. There are currently two main research streams in review mining: one is joint aspect discovery and sentiment classification, the other one is aspect-level ratings and weights approximation. There exist a number of models in each of the two areas. However, no previous work that aims to solve the two problems simultaneously has been proposed. In this paper we propose Rating Supervised Latent Topic Model to integrate the two problems into an unified optimisation problem. In the proposed model, we employ a latent topic model for aspect discovery and sentiment classification and use a regression model to approximate aspect-level ratings and weights based on the output of the topic model. We test the proposed model on a review dataset crawled from Amazon.com. The preliminary experiment results show that the proposed model outperforms a number of state-of-the-art models by a considerable margin.
-  L. Zhuang, F. Jing, and X.-Y. Zhu, “Movie review mining and summarization,” Proc. of the 15th ACM Int. Conf. on Information and Knowledge Management, pp. 43-50, 2006.
-  Y. Lu, C. Zhai, and N. Sundaresan, “Rated aspect summarization of short comments,” Proc. of the 18th Int. Conf. on World Wide Web, pp. 131-140, 2009.
-  S. Moghaddam and M. Ester, “Aspect-based opinion mining from online reviews,” Tutorial at SIGIR Conf., 2012.
-  M. Farhadloo, R. A. Patterson, and E. Rolland, “Modeling customer satisfaction from unstructured data using a Bayesian approach,” Decision Support Systems, Vol.90, pp. 1-11, 2016.
-  Y. Jo and A. H. Oh, “Aspect and sentiment unification model for online review analysis,” Proc. of the fourth ACM Int. Conf. on Web Search and Data Mining, pp. 815-824, 2011.
-  S. Brody and N. Elhadad, “An Unsupervised Aspect-sentiment Model for Online Reviews,” Human Language Technologies: The 2010 Annual Conf. of the North American Chapter of the Association for Computational Linguistics (HLT ’10), pp. 804-812, 2010.
-  C. Lin and Y. He, “Joint sentiment/topic model for sentiment analysis,” Proc. of the 18th ACM Conf. on Information and Knowledge Management, pp. 375-384, 2009.
-  W. X. Zhao, J. Jiang, H. Yan, and X. Li, “Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid,” Proc. of the 2010 Conf. on Empirical Methods in Natural Language Processing (EMNLP ’10), pp. 56-65, 2010.
-  A. Mukherjee and B. Liu, “Aspect extraction through semi-supervised modeling,” Proc. of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers – Volume 1, pp. 339-348, 2012.
-  S. Moghaddam and M. Ester, “ILDA: Interdependent LDA Model for Learning Latent Aspects and Their Ratings from Online Product Reviews,” Proc. of the 34th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR ’11), pp. 665-674, 2011.
-  J. McAuley and J. Leskovec, “Hidden factors and hidden topics: understanding rating dimensions with review text,” pp. 165-172, 2013.
-  H. Wang, Y. Lu, and C. Zhai, “Latent aspect rating analysis on review text data: a rating regression approach,” Proc. of the 16th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 783-792, 2010.
-  J. Yu, Z.-J. Zha, M. Wang, and T.-S. Chua, “Aspect ranking: identifying important product aspects from online consumer reviews,” Proc. of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies – Volume 1, pp. 1496-1505, 2011.
-  J. Parker, A. Yates, N. Goharian, and W. G. Yee, “Efficient estimation of aspect weights,” Proc. of the 35th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 1057-1058, 2012.
-  B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, 167pp., 2012.
-  D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” the J. of Machine Learning Research, Vol.3, pp. 993-1022, 2003.
-  S. Li, S. Y. M. Lee, Y. Chen, C.-R. Huang, and G. Zhou, “Sentiment Classification and Polarity Shifting,” Proc. of the 23rd Int. Conf. on Computational Linguistics (COLING ’10), pp. 635-643, 2010.
-  C. D. Manning, M. Surdeanu, J. Bauer, J. R. Finkel, S. Bethard, and D. McClosky, “The stanford corenlp natural language processing toolkit,” Proc. of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60, 2014.
-  M. Hu and B. Liu, “Mining and summarizing customer reviews,” In Proc. of the 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 168-177, 2004.
-  Z. Zhai, B. Liu, H. Xu, and P. Jia, “Grouping product features using semi-supervised learning with soft-constraints,” Proc. of the 23rd Int. Conf. on Computational Linguistics, pp. 1272-1280, 2010.
-  H. Guo, H. Zhu, Z. Guo, X. Zhang, and Z. Su, “Product Feature Categorization with Multilevel Latent Semantic Association,” Proc. of the 18th ACM Conf. on Information and Knowledge Management (CIKM ’09), pp. 1087-1096, 2009.
-  Z. Zhai, B. Liu, H. Xu, and P. Jia, “Constrained LDA for Grouping Product Features in Opinion Mining,” Proc. of Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD), 2011.
-  I. Titov and R. McDonald, “Modeling online reviews with multi-grain topic models,” Proc. of the 17th Int. Conf. on World Wide Web, pp. 111-120, 2008.
-  A. Fahrni and M. Klenner, “Old wine or warm beer: Target-specific sentiment analysis of adjectives,” Proc. of the Symposium on Affective Language in Human and Machine, AISB, pp. 60-63, 2008.
-  F. Li, C. Han, M. Huang, X. Zhu, Y.-J. Xia, S. Zhang, and H. Yu, “Structure-aware review mining and summarization,” Proc. of the 23rd Int. Conf. on Computational Linguistics, pp. 653-661, 2010.
-  F. Li, M. Huang, and X. Zhu, “Sentiment Analysis with Global Topics and Local Dependency,” Proc. of the 24th AAAI Conf. on Artificial Intelligence (AAAI-10), pp. 1371-1376, 2010.
-  Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai, “Topic sentiment mixture: modeling facets and opinions in weblogs,” Proc. of the 16th Int. Conf. on World Wide Web, pp. 171-180, 2007.
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