Opinion Mining as Extraction of Attribute-Value Relations

  • Nozomi Kobayashi
  • Ryu Iida
  • Kentaro Inui
  • Yuji Matsumoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4012)


This paper addresses the task of extracting opinions from a given document collection. Assuming that an opinion can be represented as a tuple 〈Subject, Attribute, Value〉, we propose a computational method to extract such tuples from texts. In this method, the main task is decomposed into (a) the process of extracting Attribute-Value pairs from a given text and (b) the process of judging whether an extracted pair expresses an opinion of the author. We apply machine-learning techniques to both subtasks. We also report on the results of our experiments and discuss future directions.


Noun Phrase Machine Translation Opinion Mining Sentiment Analysis Opinion Extraction 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proc. of the 12th International World Wide Web Conference, pp. 519–528 (2003)Google Scholar
  2. 2.
    Pang, B., Lee, L.: A sentiment education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proc. of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 271–278 (2004)Google Scholar
  3. 3.
    Turney, P.D.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proc. of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  4. 4.
    Kanayama, H., Nasukawa, T.: Deeper sentiment analysis using machine translation technology. In: Proceedings of the 20th International Conference on Computational Linguistics, pp. 494–500 (2004)Google Scholar
  5. 5.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proc. of the Tenth International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)Google Scholar
  6. 6.
    Tateishi, K., Ishiguro, Y., Fukushima, T.: Opinion information retrieval from the internet. IPSJ SIGNL Note 144-11, 75–82 (2001) (in Japanese)Google Scholar
  7. 7.
    Tateishi, K., Fukushima, T., Kobayashi, N., Takahashi, T., Fujita, A., Inui, K., Matsumoto, Y.: Web opinion extraction and summarization based on viewpoints of products. IPSJ SIGNL Note 163, 1–8 (2004) (in Japanese)Google Scholar
  8. 8.
    Murano, S., Sato, S.: Automatic extraction of subjective sentences using syntactic patterns. In: Proc. of the Ninth Annual Meeting of the Association for Natural Language Processing, pp. 67–70 (2003) (in Japanese)Google Scholar
  9. 9.
    Mitkov, R.: Factors in anaphora resolution: they are not the only things that matter. a case study ba sed on two different approaches. In: Proc. of the ACL 1997/EACL 1997 Workshop on Operational Factors in Practical, Robust Anaph ora Resolution. (1997)Google Scholar
  10. 10.
    Baldwin, B.: CogNIAC: A Discourse Processing Engine. PhD thesis, Department of Computer and Information Sciences, University of Pennsylvania (1995)Google Scholar
  11. 11.
    Nakaiwa, H., Shirai, S.: Anaphora resolution of japanese zero pronouns with deictic reference. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING), pp. 812–817 (1996)Google Scholar
  12. 12.
    Grosz, B.J., Joshi, A.K., Weinstein, S.: Centering: A framework for modeling the local coherence of discourse. Computational Linguistics 21, 203–226 (1995)Google Scholar
  13. 13.
    Kameyama, M.: A property-sharing constraint in centering. In: Proceedings of the 24th Annual Meeting of the Association for Computational Linguistics, pp. 200–206 (1986)Google Scholar
  14. 14.
    Halliday, M.A.K., Hasan, R.: Cohesion in English. English Language Series,Title No.9. Longman (1976)Google Scholar
  15. 15.
    Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to coreference resolution of noun phrases. Computational Linguistics 27, 521–544 (2001)CrossRefGoogle Scholar
  16. 16.
    Ng, V., Cardie, C.: Improving machine learning approaches to coreference resolution. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 104–111 (2002a)Google Scholar
  17. 17.
    Iida, R., Inui, K., Takamura, H., Matsumoto, Y.: Incorporating contextual cues in trainable models for coreference resolution. In: Proc. of the EACL Workshop on the Computational Treatment of Anaphora, pp. 23–30 (2003)Google Scholar
  18. 18.
    Ng, V.: Learning noun phrase anaphoricity to improve coreference resolution: Issues in representation and optimization. In: Proc. of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 152–159 (2004)Google Scholar
  19. 19.
    Iida, R., Inui, K., Matsumoto, Y., Sekine, S.: Noun phrase coreference resolution in japanese base on most likely antecedant candidates. Journal of Information Processing Society of Japan 46 (2005) (in Japanese)Google Scholar
  20. 20.
    Kobayashi, N., Inui, K., Matsumoto, Y., Tateishi, K., Fukushima, T.: Collecting evaluative expressions for opinion extraction. In: Su, K.-Y., Tsujii, J., Lee, J.-H., Kwong, O.Y. (eds.) IJCNLP 2004. LNCS, vol. 3248, pp. 596–605. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nozomi Kobayashi
    • 1
  • Ryu Iida
    • 1
  • Kentaro Inui
    • 1
  • Yuji Matsumoto
    • 1
  1. 1.Nara Institute of Science and TechnologyNaraJapan

Personalised recommendations