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Journal of Intelligent Information Systems

, Volume 38, Issue 2, pp 487–505 | Cite as

A new generative opinion retrieval model integrating multiple ranking factors

  • Seung-Wook Lee
  • Young-In Song
  • Jung-Tae Lee
  • Kyoung-Soo Han
  • Hae-Chang Rim
Article

Abstract

In this paper, we present clear and formal definitions of ranking factors that should be concerned in opinion retrieval and propose a new opinion retrieval model which simultaneously combines the factors from the generative modeling perspective. The proposed model formally unifies relevance-based ranking with subjectivity detection at the document level by taking multiple ranking factors into consideration: topical relevance, subjectivity strength, and opinion-topic relatedness. The topical relevance measures how strongly a document relates to a given topic, and the subjectivity strength indicates the likelihood that the document contains subjective information. The opinion-topic relatedness reflects whether the subjective information is expressed with respect to the topic of interest. We also present the universality of our model by introducing the model’s derivations that represent other existing opinion retrieval approaches. Experimental results on a large-scale blog retrieval test collection demonstrate that not only are the individual ranking factors necessary in opinion retrieval but they cooperate advantageously to produce a better document ranking when used together. The retrieval performance of the proposed model is comparable to that of previous systems in the literature.

Keywords

Opinion retrieval Opinion mining Sentiment analysis Subjectivity detection Generative model 

References

  1. Anil, R., & Sarkar, S. (2008). IIT Kharagpur at TREC 2008 blog track. In TREC 2008: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  2. Bermingham, A., Smeaton, A. F., Foster, J., & Hogan, D. (2008). DCU at the TREC 2008 blog track. In TREC 2008: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  3. Clark, M., Beresi, U. C., Watt, S., & Harper, D. (2006). RGU at the TREC blog track. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  4. Hannah, D., Macdonald, C., Peng, J., He, B., & Ounis, I. (2007). University of Glasgow at TREC 2007: Experiments in blog and enterprise tracks with terrier. In TREC 2007: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  5. Hoang, L., Lee, S. W., Hong, G. W., Lee, J. Y., & Rim, H. C. (2008). A hybrid method for opinion finding task (KUNLP at TREC 2008 blog track). In TREC 2008: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  6. Joshi, H., Bayrak, C., & Xu, X. (2006). UALR at TREC: Blog track. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  7. Kim, S. M., Hovy, E. H. (2005). Automatic detection of opinion bearing words and sentences. In IJCNLP-05: Companion volume to the proceedings of the second international joint conference on natural language processing.Google Scholar
  8. Kovacevic, M., & Huang, X. (2008). York University at TREC 2008: Blog track. In TREC 2008: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  9. Li, B., Liu, F., & Liu, Y. (2008). UTDallas at TREC 2008: Blog track. In TREC 2008: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  10. Liao, X., Cao, D., Tan, S., Liu, Y., Ding, G., & Cheng, X. (2006). Combining language model with sentiment analysis for opinion retrieval of blog-post. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  11. Lv, Y., & Zhai, C. (2009). Positional language models for information retrieval. In SIGIR ’09: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval (pp. 299–306). New York, NY, USA: ACM.CrossRefGoogle Scholar
  12. Macdonald, C., & Ounis, I. (2006). The TREC Blogs06 collection: creating and analysing a blog test collection. Tech. Rep. TR-2006-224, Department of Computing Science, University of Glasgow.Google Scholar
  13. Macdonald, C., Ounis, I., & Soboroff, I. (2007). Overview of the TREC 2007 blog track. In TREC 2007: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  14. Mackay, D. J. C., & Petoy, L. C. B. (1995). A hierarchical Dirichlet language model. Natural Language Engineering, 1, 1–19.CrossRefGoogle Scholar
  15. Mishne, G. (2006). Multiple ranking strategies for opinion retrieval in blogs the University of Amsterdam at the 2006 TREC blog track. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  16. Momtazi, S., Kazalski, S., & Klakow, D. (2009). A combined query expansion technique for retrieving opinions from blogs. In Intelligent systems design and applications (pp. 791–796).Google Scholar
  17. Na, S. H., & Ng, H. T. (2009). A 2-Poisson model for probabilistic coreference of named entities for improved text retrieval. In Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. SIGIR ’09 (pp. 275–282). New York, NY, USA: ACM.CrossRefGoogle Scholar
  18. Oard, D., Elsayed, T., Wang, J., Wu, Y., Zhang, P., Abels, E., et al. (2006). TREC-2006 at Maryland: Blog, enterprise, legal and QA tracks. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  19. Ounis, I., de Rijke, M., Macdonald, C., Mishne, G., & Soboroff, I. (2006). Overview of the TREC 2006 blog track. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA (pp. 17–31).Google Scholar
  20. Santos, R. L., He, B., Macdonald, C., & Ounis, I. (2009). Integrating proximity to subjective sentences for blog opinion retrieval. In ECIR ’09: Proceedings of the 31th European conference on IR research on advances in information retrieval (pp. 325–336). Springer: Berlin, Heidelberg.Google Scholar
  21. Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M. (1966). The general inquirer: A Computer approach to content analysis. MIT Press.Google Scholar
  22. Turney, P., & Littman, M. (2003). Measuring praise and criticism: inference of semantic orientation from association. In ACM transactions on information systems (Vol. 21, pp. 315–346).Google Scholar
  23. Vechtomova, O. (2007). Using subjective adjectives in opinion retrieval from blogs. In TREC 2007: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  24. Wiebe, J., Wilson, T., Bruce, R., Bell, M., & Martin, M. (2004). Learning subjective language. Computational Linguistics, 30(3), 277–308.CrossRefGoogle Scholar
  25. Yang, H., Si, L., & Callan, J. (2006a) Knowledge transfer and opinion detection in the TREC2006 blog track. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  26. Yang, K., Yu, N., Valerio, A., & Zhang, H. (2006b). WIDIT in TREC-2006 blog track. In TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  27. Zhang, E., & Zhang, Y. (2006). UCSC on TREC 2006 blog opinion mining. In: TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  28. Zhang, M., & Ye, X. (2008). A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval. In SIGIR ’08: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval (pp. 411–418). New York, NY, USA: ACM.CrossRefGoogle Scholar
  29. Zhang, W., & Clement, T. Y. (2006). UIC at TREC 2006 blog track. In: TREC 2006: Proceedings of the fifteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar
  30. Zhou, G., Joshi, H., & Bayrak, C. (2007). Topic categorization for relevancy and opinion detection. In TREC 2007: Proceedings of the sixteenth text retrieval conference, Gaithersburg, Maryland, USA.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Seung-Wook Lee
    • 1
  • Young-In Song
    • 2
  • Jung-Tae Lee
    • 1
  • Kyoung-Soo Han
    • 3
  • Hae-Chang Rim
    • 1
  1. 1.Department of Computer and Radio Communications EngineeringKorea UniversitySeoulSouth Korea
  2. 2.Microsoft Research AsiaBeijingPeople’s Republic of China
  3. 3.Division of Computer EngineeringSungkyul UniversityKyunggiSouth Korea

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