ABSTRACT
Web 2.0 technologies have enabled more and more people to freely comment on different kinds of entities (e.g. sellers, products, services). The large scale of information poses the need and challenge of automatic summarization. In many cases, each of the user-generated short comments comes with an overall rating. In this paper, we study the problem of generating a ``rated aspect summary'' of short comments, which is a decomposed view of the overall ratings for the major aspects so that a user could gain different perspectives towards the target entity. We formally define the problem and decompose the solution into three steps. We demonstrate the effectiveness of our methods by using eBay sellers' feedback comments. We also quantitatively evaluate each step of our methods and study how well human agree on such a summarization task. The proposed methods are quite general and can be used to generate rated aspect summary automatically given any collection of short comments each associated with an overall rating.
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003. Google ScholarCross Ref
- H. Cui, V. Mittal, and M. Datar. Comparative experiments on sentiment classification for online product reviews. In Twenty-First National Conference on Artificial Intelligence, 2006. Google ScholarDigital Library
- A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statist. Soc. B, 39:1--38, 1977.Google ScholarCross Ref
- T. Hofmann. Probabilistic latent semantic analysis. In Proc. of Uncertainty in Artificial Intelligence, Stockholm, 1999. Google ScholarDigital Library
- T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of SIGIR '99, pages 50--57, 1999. Google ScholarDigital Library
- M. Hu and B. Liu. Mining and summarizing customer reviews. In W. Kim, R. Kohavi, J. Gehrke, and W. DuMouchel, editors, KDD, pages 168--177. ACM, 2004. Google ScholarDigital Library
- S.-M. Kim and E. Hovy. Determining the sentiment of opinions. In Proceedings of the 20th international conference on Computational Linguistics, page 1367, 2004. Google ScholarDigital Library
- W. Li and A. McCallum. Pachinko allocation: Dag-structured mixture models of topic correlations. In ICML '06: Proceedings of the 23rd international conference on Machine learning, pages 577--584, 2006. Google ScholarDigital Library
- L. Lovasz and M. Plummer. Matching theory. In Annals of Discrete Mathematics, North Holland, Amsterdam, 1986.Google Scholar
- J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281--297. University of California Press, 1967.Google Scholar
- Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai. Topic sentiment mixture: modeling facets and opinions in weblogs. In WWW, pages 171--180. ACM, 2007. Google ScholarDigital Library
- Q. Mei and C. Zhai. A mixture model for contextual text mining. In KDD '06, pages 649--655, 2006. Google ScholarDigital Library
- B. Pang and L. Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the ACL, pages 115--124, 2005. Google ScholarDigital Library
- B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 79--86, 2002. Google ScholarDigital Library
- A.-M. Popescu and O. Etzioni. Extracting product features and opinions from reviews. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 339--346, 2005. Google ScholarDigital Library
- B. Snyder and R. Barzilay. Multiple aspect ranking using the good grief algorithm. In HLT-NAACL, 2007.Google Scholar
- I. Titov and R. McDonald. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of ACL-08: HLT, pages 308--316, June 2008.Google Scholar
- P. D. Turney. 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, pages 417--424, 2002. Google ScholarDigital Library
- C. Zhai, A. Velivelli, and B. Yu. A cross-collection mixture model for comparative text mining. In Proceedings of KDD '04, pages 743--748, 2004. Google ScholarDigital Library
- L. Zhuang, F. Jing, and X.-Y. Zhu. Movie review mining and summarization. In Proceedings of the 15th ACM international conference on Information and knowledge management, pages 43--50. ACM, 2006. Google ScholarDigital Library
Index Terms
- Rated aspect summarization of short comments
Recommendations
Topic-driven reader comments summarization
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementReaders of a news article often read its comments contributed by other readers. By reading comments, readers obtain not only complementary information about this news article but also the opinions from other readers. However, the existing ranking ...
Sentiment diversification for short review summarization
WI '17: Proceedings of the International Conference on Web IntelligenceWith the abundance of reviews published on the Web about a given product, consumers are looking for ways to view major opinions that can be presented in a quick and succinct way. Reviews contain many different opinions, making the ability to show a ...
Aspect-based sentence segmentation for sentiment summarization
TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinionAspect-based sentiment summarization systems generally use sentences associated with relevant aspects extracted from the reviews as the basis for summarization. However, in real reviews, a single sentence often exhibits several aspects for opinions. ...
Comments