Rated aspect summarization of short comments

WWW'09 - Proceedings of the 18th International World Wide Web Conference

Volumn , Issue , 2009, Pages 131-140

  Lu, Yue ^a   Zhai, Cheng Xiang ^a   Sundaresan, Neel ^b  

^a University of Illinois at Urbana Champaign   (United States)

^b EBAY INC   (United States)

Author keywords

Rated aspect summarization; Rating prediction; Short comments

Indexed keywords

AUTOMATIC SUMMARIZATION; RATED ASPECT SUMMARIZATION; SHORT COMMENTS; WEB 2.0;

WORLD WIDE WEB;

RATING;

EID: 79959584952     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1526709.1526728     Document Type: Conference Paper

References (20)

1. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, 2003.
2. 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.
3. 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.
4. T. Hofmann. Probabilistic latent semantic analysis. In Proc. of Uncertainty in Artificial Intelligence, Stockholm, 1999.
5. T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of SIGIR '99, pages 50-57, 1999.
6. 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.
7. S.-M. Kim and E. Hovy. Determining the sentiment of opinions. In Proceedings of the 20th international conference on Computational Linguistics, page 1367, 2004.
8. 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.
9. L. Lovasz and M. Plummer. Matching theory. In Annals of Discrete Mathematics, North Holland, Amsterdam, 1986.
10. 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.
11. 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.
12. Q. Mei and C. Zhai. A mixture model for contextual text mining. In KDD '06, pages 649-655, 2006.
13. 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.
14. 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.
15. 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.
16. B. Snyder and R. Barzilay. Multiple aspect ranking using the good grief algorithm. In HLT-NAACL, 2007.
17. 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.
18. 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.
19. 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.
20. 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.