Skip to main content

Ontology Based Opinion Mining for Movie Reviews

  • Conference paper
Knowledge Science, Engineering and Management (KSEM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5914))

Abstract

Ontology itself is an explicitly defined reference model of application domains with the purpose of improving information consistency and knowledge sharing. It describes the semantics of a domain in both human-understandable and computer-processable way. Motivated by its success in the area of Information Extraction (IE), we propose an ontology-based approach for opinion mining. In general, opinion mining is quite context-sensitive, and, at a coarser granularity, quite domain dependent. This paper introduces a fine-grain approach for opinion mining, which uses the ontology structure as an essential part of the feature extraction process, by taking account the relations between concepts. The experiment result shows the benefits of exploiting ontology structure to opinion mining.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2002 (2002)

    Google Scholar 

  2. Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2005 (2005)

    Google Scholar 

  3. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD conference, KDD 2004 (2004)

    Google Scholar 

  4. Kaji, N., Kitsuregawa, M.: Automatic construction of polarity-tagged corpus from html documents. In: Proceedings of the COLING/ACL on Main conference poster sessions, Association for Computational Linguistics Morristown, NJ, USA, pp. 452–459 (2006)

    Google Scholar 

  5. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of AAAI, pp. 755–760 (2004)

    Google Scholar 

  6. Carenini, G., Ng, R., Pauls, A.: Interactive multimedia summaries of evaluative text. In: Proceedings of the 11th international conference on Intelligent user interfaces, pp. 124–131. ACM, New York (2006)

    Chapter  Google Scholar 

  7. Ding, X., Liu, B.: The utility of linguistic rules in opinion mining. In: Proceedings of SIGIR 2007 (2007)

    Google Scholar 

  8. Gruber, T.R.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1993)

    Article  Google Scholar 

  9. Pang, B.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Ann. Arbor. 100 (2005)

    Google Scholar 

  10. Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2003), pp. 105–112 (2003)

    Google Scholar 

  11. Turney, P., et al.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 417–424 (2002)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. Dave, K., Lawrence, S., Pennock, D.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web, pp. 519–528. ACM, New York (2003)

    Google Scholar 

  14. Hearst, M.A.: Direction-based text interpretation as an information access refinement, pp. 257–274 (1992)

    Google Scholar 

  15. Jacquemin, C.: Spotting and Discovering Terms through Natural Language Processing. MIT Press, Cambridge (2001)

    Google Scholar 

  16. Kobayashi, N., Inui, K., Matsumoto, Y.: Collecting evaluative express for opinion extraction. In: Proceedings of the International Joint Conference on Natural Language Processing, IJCNLP (2004)

    Google Scholar 

  17. Yi, J., Bunescu, T.N., Niblack, R.W.: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of IEEE International Conference on Data Mining, ICDM 2003 (2003)

    Google Scholar 

  18. Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: Proceedings of ACL-EACL 1997 (1997)

    Google Scholar 

  19. Kanayama, H., Nasukawa, T.: Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2006 (2006)

    Google Scholar 

  20. Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of 5th Conference on Language Resources and Evaluation, LREC 2006 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhao, L., Li, C. (2009). Ontology Based Opinion Mining for Movie Reviews. In: Karagiannis, D., Jin, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2009. Lecture Notes in Computer Science(), vol 5914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10488-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10488-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10487-9

  • Online ISBN: 978-3-642-10488-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics