Abstract
Today, a large volume of hotel reviews is available on many websites, such as TripAdvisor (http://www.tripadvisor.com) and Orbitz (http://www.orbitz.com). A typical review contains an overall rating and several aspect ratings along with text. The rating is perceived as an abstraction of reviewers’ satisfaction in terms of points. Although the amount of reviews having aspect ratings is growing, there are plenty of reviews including only an overall rating. Extracting aspect-specific opinions hidden in these reviews can help users quickly digest them without actually reading through them. The task mainly consists of two parts: aspect identification and rating inference. Most existing studies cannot utilize aspect ratings which are becoming abundant in the last few years. In this paper, we propose two topic models which explicitly model aspect ratings as observed variables to improve the performance of aspect rating inference over unrated reviews. Specifically, we consider sentiment distributions in the aspect level, which generate sentiment words and aspect ratings. The experiment results show our approaches outperform other existing methods on the data set crawled from TripAdvisor.
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References
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media (2009)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer-Verlag New York Inc., Secaucus (2006)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. U.S.A. 101(Suppl. 1), 5228–5235 (2004)
Guo, Y., Xue, W.: Probabilistic multi-label classification with sparse feature learning, pp. 1373–1379, August 2013
Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Forth International Conference on Web Search and Web Data Mining, p. 815. ACM Press, New York (2011)
Lakkaraju, H., Bhattacharyya, C.: Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 498–509 (2011)
Li, C., Zhang, J., Sun, J.T., Chen, Z.: Sentiment topic model with decomposed prior. In: SIAM International Conference on Data Mining (SDM 2013). Society for Industrial and Applied Mathematics (2013)
Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, p. 375. ACM Press, New York, November 2009
Lin, C., He, Y., Everson, R., Ruger, S.M.: Weakly supervised joint sentiment-topic detection from text. IEEE Trans. Knowl. Data Eng. 24(6), 1134–1145 (2012)
Lu, Y., Zhai, C., Sundaresan, N.: Rated aspect summarization of short comments. In: Proceedings of the 18th International Conference on World Wide Web, p. 131. ACM Press, New York (2009)
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)
Moghaddam, S.: ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews categories and subject descriptors. In: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 665–674 (2011)
Moghaddam, S., Ester, M.: On the design of LDA models for aspect-based opinion mining. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 803–812 (2012)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics. pp. 115–124, June 2005
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing - EMNLP 2002, vol. 10, pp. 79–86. Association for Computational Linguistics, Morristown, July 2002
Snyder, B., Barzilay, R.: Multiple aspect ranking using the good grief algorithm. In: Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pp. 300–307, April 2007
Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp. 308–316. ACL (2008)
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, p. 111. ACM Press, New York (2008)
Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 783. ACM Press, New York (2010)
Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 618. ACM Press, New York (2011)
Zeng, C., Li, T., Shwartz, L., Grabarnik, G.Y.: Hierarchical multi-label classification over ticket data using contextual loss. In: 2014 IEEE Network Operations and Management Symposium (NOMS), pp. 1–8. IEEE, May 2014
Zhao, W., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 56–65, October 2010
Acknowledgment
The work is partially supported by National Science Foundation under grants CNS-1126619, IIS-121302, and CNS-1461926 and the U.S. Department of Homeland Security under grant Award Number 2010-ST-062-000039, the U.S. Department of Homeland Security’s VACCINE Center under Award Number 2009-ST-061-CI0001.
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Xue, W., Li, T., Rishe, N. (2015). Aspect and Ratings Inference with Aspect Ratings: Supervised Generative Models for Mining Hotel Reviews. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_2
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