acl acl2012 acl2012-144 acl2012-144-reference knowledge-graph by maker-knowledge-mining
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Author: Arjun Mukherjee ; Bing Liu
Abstract: Writing comments about news articles, blogs, or reviews have become a popular activity in social media. In this paper, we analyze reader comments about reviews. Analyzing review comments is important because reviews only tell the experiences and evaluations of reviewers about the reviewed products or services. Comments, on the other hand, are readers’ evaluations of reviews, their questions and concerns. Clearly, the information in comments is valuable for both future readers and brands. This paper proposes two latent variable models to simultaneously model and extract these key pieces of information. The results also enable classification of comments accurately. Experiments using Amazon review comments demonstrate the effectiveness of the proposed models.
Agarwal, R., S. Rajagopalan, R. Srikant, Y. Xu. 2003. Mining newsgroups using networks arising from social behavior. Proceedings of International Conference on World Wide Web 2003. Andrzejewski, D., X. Zhu, M. Craven. 2009. Incorporating domain knowledge into topic modeling via Dirichlet forest priors. Proceedings of International Conference on Machine Learning. Blei, D., A. Ng, and M. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research. Brody, S. and S. Elhadad. 2010. An Unsupervised Aspect-Sentiment Model for Online Reviews. Proceedings of the Annual Conference of the North American Chapter of the ACL. Burfoot, C., S. Bird, and T. Baldwin. 201 1. Collective Classification of Congressional Floor-Debate Transcripts. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Galley, M., K. McKeown, J. Hirschberg, E. Shriberg. 2004. Identifying agreement and disagreement in conversational speech: Use of Bayesian networks to model pragmatic dependencies. Proceedings of the 42th Annual Meeting of the Association of Computational Linguistics. Ghose, A. and P. Ipeirotis. 2007. Designing novel review ranking systems: predicting the usefulness and impact of reviews. Proceedings of International Conference on Electronic Commerce. Griffiths, T. and M. Steyvers. 2004. Finding scientific topics. Proceedings of National Academy of Sciences. Kim, S., P. Pantel, T. Chklovski, and M. Pennacchiotti. 2006. Automatically assessing review helpfulness. Proceedings of Empirical Methods in Natural Language Processing. Jindal, N. and B. Liu. 2008. Opinion spam and analysis. Proceedings of the ACM International Conference on Web Search and Web Data Mining. Jo, Y. and A. Oh. 2011. Aspect and sentiment unification model for online review analysis. Proceedings of the ACM International Conference on Web Search and Web Data Mining. Li, F., M. Huang, Y. Yang, and X. Zhu. 2011. Learning to Identify Review Spam. in Proceedings of the International Joint Conference on Artificial Intelligence. Lim, E., V. Nguyen, N. Jindal, B. Liu, and H. Lauw. 2010. Detecting Product Review Spammers using 328 Rating Behaviors. Proceedings of the ACM International Conference on Information and Knowledge Management. Lin, C. and Y. He. 2009. Joint sentiment/topic model for sentiment analysis. Proceedings of the ACM International Conference on Information and Knowledge Management. Liu, J., Y. Cao, C. Lin, Y. Huang, and M. Zhou. 2007. Low-quality product review detection in opinion summarization. Proceedings of Empirical Methods in Natural Language Processing. Liu, B. 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool publishers (to appear in June 2012). Liu, Y., X. Huang, A. An, and X. Yu. 2008. Modeling and predicting the helpfulness of online reviews. Proceedings of IEEE International Conference on Data Mining. Lu, Y. and C. Zhai. 2008. Opinion integration through semi-supervised topic modeling. Proceedings of International Conference on World Wide Web. Lu, Y., C. Zhai, and N. Sundaresan. 2009. Rated aspect summarization of short comments. Proceedings of International Conference on World Wide. Mei, Q. X. Ling, M. Wondra, H. Su and C. Zhai. 2007. Topic sentiment mixture: modeling facets and opinions in weblogs. Proceedings of International Conference on World Wide. Moghaddam, S. and M. Ester. 201 1. ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. Proceedings of Annual ACM SIGIR Conference on Research and Development in Information Retrieval. Mukherjee, A. and B. Liu. 2012a. Aspect Extraction through Semi-Supervised Modeling. Proceedings of 50th Annual Meeting of Association for Computational Linguistics (to appear in July 2012). Mukherjee, A. and B. Liu. 2012b. Mining Contentions from Discussions and Debates. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (to appear in August 2012). Mukherjee, A., B. Liu and N. Glance. 2012. Spotting Fake Reviewer Groups in Consumer Reviews. Proceedings of International World Wide Web Conference. Murakami A., and R. Raymond, 2010. Support or Oppose? Classifying Positions in Online Debates from Reply Activities and Opinion Expressions. Proceedings of International Conference on Computational Linguistics. O'Mahony, M. P. and B. Smyth. 2009. Learning to recommend helpful hotel reviews. Proceedings of the third ACM conference on Recommender systems. Ott, M., Y. Choi, C. Cardie, and J. T. Hancock. 201 1. Finding deceptive opinion spam by any stretch of the imagination. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Pang, B. and L. Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval. Ramage, D., D. Hall, R. Nallapati, and C. Manning. 2009. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of Empirical Methods in Natural Language Processing. Ramage, D., C. Manning, and S. Dumais. 201 1 Partially labeled topic models for interpretable text mining. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. Rosen-Zvi, M., T. Griffiths, M. Steyvers, and P. Smith. 2004. The author-topic model for authors and documents. Uncertainty in Artificial Intelligence. Sauper, C. A. Haghighi and R. Barzilay. 2011. Content models with attitude. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Somasundaran, S., J. Wiebe. 2009. Recognizing stances in online debates. Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP Teh, Y., M. Jordan, M. Beal and D. Blei. 2006. Hierarchical Dirichlet Processes. Journal of the American Statistical Association. Thomas, M., B. Pang and L. Lee. 2006. Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. Proceedings of Empirical Methods in Natural Language Processing. Titov, I. and R. McDonald. 2008a. Modeling online reviews with multi-grain topic models. Proceedings of International Conference on World Wide Web. Titov, I. and R. McDonald. 2008b. A joint model of text and aspect ratings for sentiment summarization. Proceedings of Annual Meeting of the Association for Computational Linguistics. Tsur, O. and A. Rappoport. 2009. Revrank: A fully unsupervised algorithm for selecting the most helpful 329 book reviews. Proceedings of the International AAAI Conference on Weblogs and Social Media. Wang, H., Y. Lu, and C. Zhai. 2010. Latent aspect rating analysis on review text data: a rating regression approach. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Yano, T and N. Smith. 2010. What’s Worthy of Comment? Content and Comment Volume in Political Blogs. Proceedings of the International AAAI Conference on Weblogs and Social Media. Zhang, Z. and B. Varadarajan. 2006. Utility scoring of product reviews. Proceedings of ACM International Conference on Information and Knowledge Management. Zhao, X., J. Jiang, H. Yan, and X. Li. 2010. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. Proceedings of Empirical Methods in Natural Language Processing.