Discovering K Web User Groups with Specific Aspect Interests

  • Jianfeng Si
  • Qing Li
  • Tieyun Qian
  • Xiaotie Deng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7376)

Abstract

Online review analysis becomes a hot research topic recently. Most of the existing works focus on the problems of review summarization, aspect identification or opinion mining from an item’s point of view such as the quality and popularity of products. Considering the fact that authors of these review texts may pay different attentions to different domain-based product aspects with respect to their own interests, in this paper, we aim to learn K user groups with specific aspect interests indicated by their review writings. Such K user groups’ identification can facilitate better understanding of customers’ interests which are crucial for application like product improvement on customer-oriented design or diverse marketing strategies. Instead of using a traditional text clustering approach, we treat the clusterId as a hidden variable and use a permutation-based structural topic model called KMM. Through this model, we infer K groups’ distribution by discovering not only the frequency of reviewers’ product aspects, but also the occurrence priority of respective aspects. Our experiment on several real-world review datasets demonstrates a competitive solution.

Keywords

User Group Topic Model Sentiment Analysis Online Review Topic Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abdul-Mageed, M., Diab, M.T., Korayem, M.: Subjectivity and sentiment analysis of modern standard arabic. In: ACL (Short Papers) 2011, pp. 587–591 (2011)Google Scholar
  2. 2.
    Beineke, P., Hastie, T., Manning, C., Vaithyanathan, S.: An Exploration of Sentiment Summarization. In: Proceeding of AAAI, pp. 12–15 (2003)Google Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)Google Scholar
  4. 4.
    Blei, D.M., Griffiths, T.L., Jordan, M.I.: The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM 57, 7:1–7:30 (2010)Google Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  6. 6.
    Chen, H., Branavan, S.R.K., Barzilay, R., Karger, D.R.: Content modeling using latent permutations. J. Artif. Intell. Res. (JAIR) 36, 129–163 (2009)Google Scholar
  7. 7.
    Fligner, M.A., Verducci, J.S.: Distance based ranking models. Journal of the Royal Statistical Society. Series B (Methodological) 48(3), 359–369 (1986)MathSciNetMATHGoogle Scholar
  8. 8.
    Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse: Mining Customer Opinions from Free Text. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 121–132. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Ganesan, K., Zhai, C.: Opinion-based entity ranking. Information Retrieval (2011)Google Scholar
  10. 10.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. PNAS 101(suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar
  11. 11.
    Gruber, A., Rosen-Zvi, M., Weiss, Y.: Hidden Topic Markov Models. In: Artificial Intelligence and Statistics (AISTATS), San Juan, Puerto Rico (2007)Google Scholar
  12. 12.
    Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the International Conference on Web Search and Web Data Mining, WSDM 2008, pp. 219–230 (2008)Google Scholar
  13. 13.
    Jindal, N., Liu, B., Lim, E.-P.: Finding unusual review patterns using unexpected rules. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1549–1552 (2010)Google Scholar
  14. 14.
    Jordan, M. (ed.): Learning in Graphical Models. MIT Press (1999)Google Scholar
  15. 15.
    Li, W., McCallum, A.: Pachinko allocation: Dag-structured mixture models of topic correlations. In: ICML (2006)Google Scholar
  16. 16.
    Liu, B.: Opinion observer: Analyzing and comparing opinions on the web. In: Proceedings of the 14th International Conference on World Wide Web, WWW 2005, pp. 342–351 (2005)Google Scholar
  17. 17.
    Mei, Q., Liu, C., Su, H., Zhai, C.: A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 533–542 (2006)Google Scholar
  18. 18.
    Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N.: Detecting group review spam. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, pp. 93–94 (2011)Google Scholar
  19. 19.
    Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 339–346 (2005)Google Scholar
  20. 20.
    Purver, M., Griffiths, T.L., Körding, K.P., Tenenbaum, J.B.: Unsupervised topic modelling for multi-party spoken discourse. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, ACL-44, pp. 17–24 (2006)Google Scholar
  21. 21.
    Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceeding of the 17th International Conference on World Wide Web, WWW 2008, pp. 111–120 (2008)Google Scholar
  22. 22.
    Zhao, Y., Karypis, G.: Criterion functions for document clustering: Experiments and analysis. Tech. rep., University of Minnesota (2002)Google Scholar
  23. 23.
    Zhou, X., Zhang, X., Hu, X.: Semantic smoothing of document models for agglomerative clustering. In: Proceeding 20th International Joint Conf. Artificial Intelligence, IJCAI 2007, pp. 2928–2933 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jianfeng Si
    • 1
  • Qing Li
    • 1
  • Tieyun Qian
    • 2
  • Xiaotie Deng
    • 1
  1. 1.Department of Computer ScienceCity University of Hong KongHong Kong, China
  2. 2.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina

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