Skip to main content

Review Summarization Based on Linguistic Knowledge

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7240)

Abstract

In this paper, domain-knowledge extraction and aspect- opinion extraction are proposed in order to generate a summary from the relevant product and service review. In order to extract the word corresponding to aspect and opinion, we extract the domain-salient word and collocation information by applying statistical techniques from the bulk of the text, and construct the clue words through manual filtering. In domain knowledge extraction, in order to extract useful information, domain-salient words which occur more significantly in a given domain rather than in a public domain article are automatically extracted by using the statistical techniques. As well, collocation information has the association with high frequency words. In recognition of aspect-opinion association, words corresponding to aspects and opinions in a sentence are checked by using information of clue words, and the polarity of the sentence is determined by performing pattern-based modality analysis. Through checking the binary association based on the frequency of co-occurrence, a pair of aspect and opinion is extracted, our system can automatically acquire the scores for a review target based of the degree of positive/negative.

Keywords

  • Domain-knowledge extraction
  • aspect-opinion extraction
  • linguistic knowledge
  • review summarization

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Song, J.S., Lee, S.W.: Automatic Construction of Positive/Negative Feature-Predicate Dictionary for Polarity Classification of Product Reviews. Korean Institute of Information Scientists and Engineers: Software and Applications 38(3), 157–168 (2011)

    Google Scholar 

  2. Zhuang, L., Jing, F., Zhu, X.Y.: Movie Review Mining and Summarization. In: Proceedings of the International Conference on Information and Knowledge Management (2006)

    Google Scholar 

  3. Chen, H., Zimbra, D.: AI and Opinion Mining. IEEE Intelligent Systems 25(3), 74–80 (2010)

    CrossRef  Google Scholar 

  4. Titov, I., McDonald, R.: Modeling Online Reviews with Multi-Grain Topic Models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120 (2008)

    Google Scholar 

  5. Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reynar, J.: Building a Sentiment Summarizer for Local Service Reviews. In: WWW Workshop on NLP in the Information Explosion Era (2008)

    Google Scholar 

  6. Ming, Z.Y., Chua, T.S., Cong, G.: Exploring Domain-Specific Term Weight in Archived Question Search. In: Proceedings of the ACM International Conference on Information and Knowledge Management (2010)

    Google Scholar 

  7. Esuli, A., Sebastiani, F.: Determining Term Subjectivity and Term Orientation for Opinion Mining. In: Proceedings of the European Chapter of the Association for Computational Linguistics (2006)

    Google Scholar 

  8. Nishikawa, H., Hasegawa, T., Matsuo, Y., Kikui, G.: Opinion Summarization with Integer Linear Programming Formulation for Sentence Extraction and Ordering. In: Proceedings of the International Conference on Computational Linguistics (2010)

    Google Scholar 

  9. Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.X.: Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180 (2007)

    Google Scholar 

  10. Lerman, K., Blair-Goldensohn, S., McDonald, R.: Sentiment Summarization: Evaluating and Learning User Preferences. In: Proceedings of the European Chapter of the Association for Computational Linguistics (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Park, KM., Park, H., Kim, HG., Ko, H. (2012). Review Summarization Based on Linguistic Knowledge. In: Yu, H., Yu, G., Hsu, W., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29023-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29023-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29022-0

  • Online ISBN: 978-3-642-29023-7

  • eBook Packages: Computer ScienceComputer Science (R0)