Abstract
Opinion rating has been studied for a long time and recent work started to pay attention to topical aspects opinion rating, for example, the food quality, service, location and price of a restaurant. In this paper, we focus on predicting the overall and aspect rating of entities based on widely available on-line reviews. A novel hierarchical Bayesian generative method is developed for this task. It enables us to mine the overall and aspect ratings of both entity and its reviews at the same time. We conduct experiments on TripAdvisor and results show that we can predict entity-level and review-level overall ratings and aspect ratings well.
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References
Blei, D., Andrew, N., Ordan, M.J.: Latent dirichlet allocation. In: JMLR (2003)
Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: NAACL 2010 (2010)
Burges, C.: A tutorial on support vector machines for pattern recognition. In: Data Mining and Knowledge Discovery (1998)
Ding, X., Liu, B., Zhang, L.: Entity discovery and assignment for opinion mining applications. In: KDD 2009 (2009)
Gruber, A., Weiss, Y., Rosen-Zvi, M.: Hidden topic Markov models. In: AISTATS 2007 (2007)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: CIKM 2004 (2004)
Hu, M., Liu, B.: Opinion integration through semi-supervised topic modeling. In: WWW 2008 (2008)
Jindal, N., Liu, B.: Identifying comparative sentences in text documents. In: SIGIR 2006 (2006)
Jordan, M., Ghahramani, Z., Jaakkola, T., Saul, L.: An introduction to variational methods for graphical models. In: Machine Learning (2008)
Lu, B., Ott, M., Cardie, C., Tsou, B.: Multi-aspect sentiment analysis with topic models. In: ICDM 2011 (2011)
Lu, Y., Zhai, C.X., Sundaresan, N.: Rated aspect summarization of short comments. In: WWW 2009 (2009)
Blei, D., McAuliffe, J.: Supervised topic models. In: NIPS 2007 (2007)
Kim, S.-M., Hovy, E.: Determining the sentiment of opinions. In: COLING 2004 (2004)
Lerman, K., Blair-Goldensohn, S., McDonald, R.: Sentiment summarization: evaluating and learning user preferences. In: EACL 2009 (2009)
Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: CIKM 2009 (2009)
Lu, Y., Zhai, C.: Opinion integration through semi-supervised topic modeling. In: WWW 2004 (2004)
Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining product reputations on the web. In: KDD 2002 (2002)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: ACl 2005 (2005)
Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Natural Language Processing and Text Mining (2007)
Sauper, C., Haghighi, A., Barzilay, R.: Incorporating content structure into text analysis applications. In: EMNLP 2010 (2010)
Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: ACL 2008 (2008)
Wang, H., Lu, Y., Zhai, C.X.: Latent aspect rating analysis on review text data: a rating regression approach. In: KDD 2010 (2010)
Wang, H., Lu, Y., Zhai, C.X.: Latent aspect rating analysis without aspect keyword supervision. In: KDD 2011 (2011)
Snyder, B., Barzilay, R.: Multiple aspect ranking using the good grief algorithm. In: HLT-NAACL (2007)
Zhuang, L., Jing, F., Zhu, X.: Movie review mining and summarization. In: CIKM 2006 (2006)
Li, J., Cardie, C.: TopicSpam: a topic-model based approach for spam detection. In: ACL 2013 (2013)
Jindal, N., Liu, B.: Opinion spam and analysis. In: WSDM 2008 (2008)
Li, J., Ott, M., Cardie, C.: Identifying manipulated offerings on review portals. In: EMNLP 2013 (2013)
Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam. In: ACL 2014 (2014)
Zhao, W., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: EMNLP 2010 (2010)
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Wang, X., Sudoh, K., Nagata, M. (2015). Rating Entities and Aspects Using a Hierarchical Model. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_4
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DOI: https://doi.org/10.1007/978-3-319-18032-8_4
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