Abstract
Extracting the latent structure of the aspects and the sentiment polarities is important as it helps customers to understand people’ preference to a certain product and show the reasons why they prefer this product. However, insufficient studies have been done to effectively reveal the structure sentiment of the aspects from short texts due to the shortness and sparsity. In this paper, we propose a structured sentiment analysis (SSA) approach to understand the sentiments and opinions expressed by people in short texts. The proposed SSA approach has three advantages: (1) automatically extracts a hierarchical tree of a product’s hot aspects from short texts; (2) hierarchically analyses people’s opinions on those aspects; and (3) generates a summary and evidences of the results. We evaluate our approach on popular products. The experimental results show that the proposed approach can effectively extract a sentiment tree from short texts.
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References
Blei, D.M., Griffiths, T.L., Jordan, M.I.: The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. arXiv preprint arXiv:0710.0845 (2007)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
Kim, J.H., Kim, D., Kim, S., Oh, A.: Modeling topic hierarchies with the recursive Chinese restaurant process. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 783–792. ACM (2012)
Kim, S., Zhang, J., Chen, Z., Oh, A.H., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: AAAI (2013)
Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 375–384. ACM (2009)
Paisley, J., Wang, C., Blei, D.M., Jordan, M.I.: Nested hierarchical dirichlet processes. IEEE Trans. Pattern Anal. Mach. Intell. 37(2), 256–270 (2015)
Phan, X.H., Nguyen, C.T., Le, D.T., Nguyen, L.M., Horiguchi, S., Ha, Q.T.: A hidden topic-based framework toward building applications with short web documents. IEEE Trans. Knowl. Data Eng. 23(7), 961–976 (2011)
Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Kao, A., Poteet, S.R. (eds.) Natural Language Processing and Text Mining, pp. 9–28. Springer, London (2007)
Ramage, D., Dumais, S.T., Liebling, D.J.: Characterizing microblogs with topic models. In: ICWSM, vol. 10, pp. 1–1 (2010)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a large ontology from Wikipedia and WordNet. Web Semant. Sci. Serv. Agents World Wide Web 6(3), 203–217 (2008)
Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)
Wang, C., Danilevsky, M., Desai, N., Zhang, Y., Nguyen, P., Taula, T., Han, J.: A phrase mining framework for recursive construction of a topical hierarchy. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 437–445. ACM (2013)
Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential Twitterers. In: Proceedings of the third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
Zhao, P., Li, X., Wang, K.: Feature extraction from micro-blogs for comparison of products and services. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds.) WISE 2013. LNCS, vol. 8180, pp. 82–91. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41230-1_7
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Almars, A., Li, X., Zhao, X., Ibrahim, I.A., Yuan, W., Li, B. (2017). Structured Sentiment Analysis. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_49
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DOI: https://doi.org/10.1007/978-3-319-69179-4_49
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