Coarse-to-fine review selection via supervised joint aspect and sentiment model

SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2014, Pages 617-626

  Hai, Zhen ^a   Cong, Gao ^a   Chang, Kuiyu ^a   Liu, Wenting ^a   Cheng, Peng ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Review helpfulness; Review selection; Sentiment analysis; Supervised joint topic model

Indexed keywords

COMPETING MODELS; CUSTOMER REVIEW; PROBABILISTIC TOPIC MODELS; PRODUCT ASPECTS; PRODUCTS AND SERVICES; PURCHASE DECISION; SENTIMENT ANALYSIS; TOPIC MODELING;

INFORMATION RETRIEVAL;

EID: 84904556878     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2600428.2609570     Document Type: Conference Paper

References (20)

1. D. M. Blei and J. D. McAuliffe. Supervised topic models. In Proceedings of the 21st Annual Conference on Neural Information Processing Systems - Volume 7, pages 121-128, Vancouver, Canada, 2007.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, March 2003.
3. A. Ghose and P. Ipeirotis. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Trans. on Knowl. and Data Eng., 23(10):1498-1512, 2011.
4. T. L. Griffiths and M. Steyvers. Finding scientific topics. In Proceedings of the National Academy of Science, volume 101, pages 5228-5235, Jan 2004.
5. T. Hofmann. Probabilistic latent semantic analysis. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, pages 289-296, Stockholm, Sweden, 1999.
6. Y. Jo and A. H. Oh. Aspect and sentiment unification model for online review analysis. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pages 815-824, Hong Kong, China, 2011.
7. S.-M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti. Automatically assessing review helpfulness. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 423-430, Sydney, Australia, 2006.
8. D. Klein and C. D. Manning. Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, pages 423-430, Sapporo, Japan, 2003.
9. C. Lin and Y. He. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, pages 375-384, Hong Kong, China, 2009.
10. C. Lin, Y. He, R. Everson, and S. Ruger. Weakly supervised joint sentiment-topic detection from text. IEEE Trans. on Knowl. and Data Eng., 24(6):1134-1145, June 2012.
11. J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou. Low-quality product review detection in opinion summarization. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 334-342, Prague, Czech Republic, 2007.
12. Y. Liu, X. Huang, A. An, and X. Yu. Modeling and predicting the helpfulness of online reviews. In Proceedings of the 8th IEEE International Conference on Data Mining, pages 443-452, Pisa, Italy, 2008.
13. Y. Lu, P. Tsaparas, A. Ntoulas, and L. Polanyi. Exploiting social context for review quality prediction. In Proceedings of the 19th International Conference on World Wide Web, pages 691-700, Raleigh, North Carolina, USA, 2010.
14. T. Minka. Estimating a dirichlet distribution. In Technical report, MIT, 2000.
15. S. Moghaddam and M. Ester. Ilda: Interdependent lda model for learning latent aspects and their ratings from online product reviews. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 665-674, Beijing, China, 2011.
16. M. P. OMahony and B. Smyth. Learning to recommend helpful hotel reviews. In Proceedings of the 3rd ACM Conference on Recommender Systems, pages 305-308, New York, USA, 2009.
17. I. Titov and R. T. McDonald. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pages 308-316, Columbus, Ohio, USA, 2008.
18. H. M. Wallach, D. M. Mimno, and A. McCallum. Rethinking lda: Why priors matter. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, pages 1973-1981, Vancouver, Canada, 2009.
19. H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis without aspect keyword supervision. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 618-626, San Diego, California, USA, 2011.
20. Z. Zhang and B. Varadarajan. Utility scoring of product reviews. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pages 51-57, Arlington, Virginia, USA, 2006.