Domain Specific Opinion Retrieval

  • Guang Qiu
  • Feng Zhang
  • Jiajun Bu
  • Chun Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)


Opinion retrieval is a novel information retrieval task and has attracted a great deal of attention with the rapid increase of online opinionated information. Most previous work adopts the classical two stage framework, i.e., first retrieving topic relevant documents and then re-ranking them according to opinion relevance. However, none has considered the problem of domain coherence between queries and topic relevant documents. In this work, we propose to address this problem based on the similarity measure of the usage of opinion words (which users employ to express opinions). Our work is based on the observation that the opinion words are domain dependent. We reformulate this problem as measuring the opinion similarity between domain opinion models of queries and document opinion models. Opinion model is constructed to capture the distribution of opinion words. The basic idea is that if a document has high opinion similarity with a domain opinion model, it indicates that it is not only opinionated but also in the same domain with the query (i.e., domain coherence). Experimental results show that our approach performs comparatively with the state-of-the-art work.


Opinion Retrieval Domain Coherence Opinion Model Opinion Similarity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhang, M., Ye, X.: A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval. In: Proceedings of SIGIR 2008, pp. 411–418 (2008)Google Scholar
  2. 2.
    Eguchi, K., Lavrenko, V.: Sentiment Retrieval using Generative Models. In: Proceedings of EMNLP 2006, pp. 345–354 (2006)Google Scholar
  3. 3.
    Mishne, G.: Multiple Ranking Strategies for Opinion Retrieval in Blogs. In: Online Proceedings of TREC (2006)Google Scholar
  4. 4.
    Yang, K., Yu, N., Valerio, A., Zhang, H.: WIDIT in TREC 2006 Blog track. In: Online Proceedings of TREC (2006)Google Scholar
  5. 5.
    Liao, X., Cao, D., et al.: Combing Language Model with Sentiment Analysis for Opinoin Retreival of Blog-Post. In: Online Proceedings of TREC (2006)Google Scholar
  6. 6.
    Zhang, W., Yu, C.: UIC at TREC 2006 Blog Track. In: Online Proceedings of TREC (2006)Google Scholar
  7. 7.
    Mishne, G.: Using blog properties to improve retrieval. In: Proceedings of the International Conference on Weblogs and. Social Media, ICSWM (2007)Google Scholar
  8. 8.
    He, B., Macdonald, C., He, J., Ounis, I.: An effective statistical approach to blog post opinion retrieval. In: Proceedings of CIKM 2008 (2008)Google Scholar
  9. 9.
    He, B., Macdonald, C., Ounis, I.: Ranking opinionated blog posts using OpinionFinder. In: Proceedings of SIGIR 2008 (2008)Google Scholar
  10. 10.
    Zhang, W., Yu, C., Meng, W.: Opinion retrieval from blogs. In: Proceedings of CIKM 2007 (2007)Google Scholar
  11. 11.
    Zhang, W., Jia, L., Yu, C., Meng, W.: Improve the effectiveness of the opinion retrieval and opinion polarity classification. In: Proceeding of CIKM 2008 (2008)Google Scholar
  12. 12.
    Ounis, I., de Rijke, M., Macdonald, C., Mishne, G., Soboroff, I.: Overview of the TREC 2006 Blog Track. In: Online Proceedings of TREC (2006)Google Scholar
  13. 13.
    Macdonald, C., Ounis, I.: Overview of the TREC 2007 Blog Track. In: Online Proceedings of TREC (2007)Google Scholar
  14. 14.
    Turney, P.D.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of ACL 2002, pp. 417–424 (2002)Google Scholar
  15. 15.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)Google Scholar
  16. 16.
    Shen, D., Pan, R., et al.: Q2C@UST: our winning solution to query classification in KDDCUP 2005. SIGKDD Explorations 7(2), 100–110 (2005)CrossRefGoogle Scholar
  17. 17.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  18. 18.
    Hofmann, T.: Probabilistic Latent Semantic Analysis. In: Proceedings of UAI 1999 (1999)Google Scholar
  19. 19.
    Stone, P., Dunphy, D., Smith, M., Ogilvie, D.: The General Inquirer: A Computer Approach to Content Anaysis. MIT Press, Cambridge (1966)Google Scholar
  20. 20.
    Esuli, A., Sebastiani, F.: Determining the semantic orientation of terms through gloss classification. In: Proceedings of CIKM 2005, pp. 617–624 (2005)Google Scholar
  21. 21.
    Kullback, S., Leibler, R.A.: On Information and Sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: Proceedings of SIGKDD 2004 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Guang Qiu
    • 1
  • Feng Zhang
    • 1
  • Jiajun Bu
    • 1
  • Chun Chen
    • 1
  1. 1.Zhejiang Key Laboratory of Service Robot, College of Computer ScienceZhejiang UniversityHangzhouChina

Personalised recommendations