Skip to main content

Using an Information Quality Framework to Evaluate the Quality of Product Reviews

  • Conference paper
Information Retrieval Technology (AIRS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5839))

Included in the following conference series:

Abstract

The prevalence of Web2.0 makes the Web an invaluable source of information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable manufactures to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining is needed to identify important reviews and opinions for users. Most opinion mining approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the mining process often ignores the quality of each review and may retrieve useless or even noisy reviews. In this paper, we propose a method for evaluating the quality of information in product reviews. We treat review quality evaluation as a classification problem and employ an effective information quality framework to extract representative review features. Experiments based on an expert-composed data corpus demonstrate that the proposed method outperforms state-of-the-art approaches significantly.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Chevalier, J.A., Mayzlin, D.: The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research 43(3), 345–354 (2006)

    Article  Google Scholar 

  2. Dave, K., Lawrence, S., Pennock, D.M.: Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In: WWW, pp. 519–528 (2003)

    Google Scholar 

  3. Ding, X., Liu, B., Yu, P.S.: A Holistic Lexicon-Based Approach to Opinion Mining. In: WSDM, pp. 231–240 (2008)

    Google Scholar 

  4. Eppler, M.J., Wittig, D.: Conceptualizing Information Quality: A Review of Information Quality Frameworks from the Last Ten Years. In: ICIQ, pp. 83–96 (2000)

    Google Scholar 

  5. Fellbaum, C.: WordNet: an Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  6. Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)

    Article  Google Scholar 

  7. Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: SIGKDD, pp. 168–177 (2004)

    Google Scholar 

  8. Huang, K.T., Lee, Y.W., Wang, R.Y.: Quality Information and Knowledge. Prentice Hall PTR, Upper Saddle River (1998)

    Google Scholar 

  9. Jindal, N., Liu, B.: Opinion Spam and Analysis. In: WSDM, pp. 219–230 (2008)

    Google Scholar 

  10. Joachims, T.: Making Large-scale SVM Learning Practical. In: Schökopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT-Press, Cambridge (1999)

    Google Scholar 

  11. Kim, S.M., Hovy, E.: Determining the Sentiment of Opinions. In: ICCL, pp. 1367–1373 (2004)

    Google Scholar 

  12. Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically Assessing Review Helpfulness. In: EMNLP, pp. 423–430 (2006)

    Google Scholar 

  13. Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion Extraction, Summarization and Tracking in News and Blog Corpora. In: AAAI-CAAW, Technical Report SS-06-03, pp. 100–107 (2006)

    Google Scholar 

  14. Liu, J., Cao, Y., Lin, C.Y., Huang, Y., Zhou, M.: Low-Quality Product Review Detection in Opinion Summarization. In: EMNLP-CoNLL, pp. 334–342 (2007)

    Google Scholar 

  15. Manning, C., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  16. Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to WordNet: An On-line Lexical Database. International Journal of Lexicography 3(4), 235–244 (1990)

    Article  Google Scholar 

  17. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning Techniques. In: EMNLP, pp. 79–86 (2002)

    Google Scholar 

  18. Roed, J.: Language Learner Behavior in a Virtual Environment. Computer Assisted Language Learning 16(2–3), 155–172 (2003)

    Article  Google Scholar 

  19. Steinwart, I., Christmann, A.: Support Vector Machines. Springer, New York (2008)

    MATH  Google Scholar 

  20. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large Margin Methods for Structured and Interdependent Output Variables. Journal of Machine Learning Research 6, 1453–1484 (2005)

    MathSciNet  MATH  Google Scholar 

  21. Turney, P.D.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: ACL, pp. 129–159 (2002)

    Google Scholar 

  22. Wang, R.Y., Strong, D.M.: Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems 12(4), 5–33 (1996)

    Article  Google Scholar 

  23. Weston. J., Watkins. C.: Multi-class Support Vector Machines. Technical Report CSD-TR-98-04, Royal Holloway, University of London, Department of Computer Science (1998)

    Google Scholar 

  24. Zhang, Z., Varadarajan, B.: Utility Scoring of Product Reviews. In: CIKM, pp. 51–57 (2006)

    Google Scholar 

  25. Zhu, X., Gauch, S.: Incorporating Quality Metrics in Centralized/Distributed Information Retrieval on the World Wide Web. In: SIGIR, pp. 288–295 (2000)

    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

Tseng, YD., Chen, C.C. (2009). Using an Information Quality Framework to Evaluate the Quality of Product Reviews. In: Lee, G.G., et al. Information Retrieval Technology. AIRS 2009. Lecture Notes in Computer Science, vol 5839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04769-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04769-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04768-8

  • Online ISBN: 978-3-642-04769-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics