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, several aspect ratings, and review text. The rating is an abstract of review in terms of numerical points. The task of aspect-based opinion summarization is to extract aspect-specific opinions hidden in the reviews which do not have aspect ratings, so that users can quickly digest them without actually reading through them. The task consists of aspect identification and aspect rating inference. Most existing studies cannot utilize aspect ratings which become increasingly abundant on review hosts. In this paper, we propose two topic models which explicitly model aspect ratings as observed variables to improve the performance of aspect rating inference on unrated reviews. The experiment results show that our approaches outperform the existing methods on the data set crawled from TripAdvisor website.
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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 (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. Natl. Acad. Sci. U.S.A. 101(Suppl 1), 5228–5235 (2004)
Guo, Y., Xue, W.: Probabilistic multi-label classification with sparse feature learning. In: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence pp. 1373–1379
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, New York, USA (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: Proceedings of the 2013 SIAM international conference on data mining. 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, New York, USA (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, New York, USA (2009)
Luo, W., Zhuang, F., Cheng, X., He, Q., Shi, Z.: Ratable aspects over sentiments: Predicting ratings for unrated reviews. In: 2014 IEEE international conference on data mining, pp. 380–389 (2014)
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, June, pp. 115–124 (2005)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP ’02, vol. 10, pp. 79–86. Association for Computational Linguistics, Morristown, NJ, USA (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, April, pp. 300–307 (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, New York, USA (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, New York, USA (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, New York, USA (2011)
Xue, W., Li, T., Rishe, N.: Aspect and ratings inference with aspect ratings: Supervised generative models for mining hotel reviews. In: Web information systems engineering–WISE 2015, pp. 17–31. Springer (2015)
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, October, pp. 56–65 (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. Aspect identification and ratings inference for hotel reviews. World Wide Web 20, 23–37 (2017). https://doi.org/10.1007/s11280-016-0398-9
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DOI: https://doi.org/10.1007/s11280-016-0398-9