Dependency Driven Semantic Approach to Product Features Extraction and Summarization Using Customer Reviews

  • V. Ravi Kumar
  • K. Raghuveer
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 178)


Customer reviews include opinions of the customer who purchased the products and expressed opinions may be regarding their satisfaction and criticism about different features of the product. In this paper we aim to mine different product features based on customer opinion expressed in the review and also to identify the opinion sentences associates with the extracted product features to find opinion summarization. We propose a semantic based approach using typed dependency relations to identify the product features based on the opinion word associated with it using different dependencies.


Product Feature extraction Opinion Identification Opinion Sentences Dependency Relations Pronoun resolution 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proc. of the 10th ACM SIGKDD-2004 International Conference on Knowledge Discovery and Data Mining, Seattle, pp. 168–177 (2004)Google Scholar
  2. 2.
    Kobayashi, N., Iida, R., Inui, K., Matsumotto, Y.: Opinion extraction using a learning-based anaphora resolution technique. In: Proc. of the Second International Joint Conference on Natural Language Processing (IJCNLP 2004), Jeju Island, pp. 173–178 (2004)Google Scholar
  3. 3.
    Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Proc. of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, pp. 339–346 (2005)Google Scholar
  4. 4.
    Wong, T.L., Lam, W.: Learning to extract and summarize hot item features from multiple auction Web sites. Knowl. Inf. Syst. 14(2), 143–160 (2008)CrossRefGoogle Scholar
  5. 5.
    Stanford typed dependencies manualGoogle Scholar
  6. 6.
    Riloff, E., Janyce, W., Theresa, W.: Learning Subjective Nouns Using Extraction Pattern Bootstrapping. In: Proc. 7th Conf. Natural Language Learning, pp. 25–32 (2003)Google Scholar
  7. 7.
    Wiebe, J.: Learning Subjective Adjectives from Corpora. In: Proc. of 12th Conference on Innovative Applications of Artificial Intelligence (2000)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Carenini, G., Ng, R., Pauls, A.: MultiDocument Summarization of Evaluative Text. In: Proc. of the 11th European Chapter of the Association for Computational Linguistics (EACL 2006), Trento, Italy (April 2006)Google Scholar
  10. 10.
    Somprasertsri, G., Lalitrojwong, P.: Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization. Journal of Universal Computer Science 16(6), 938–955 (2010)Google Scholar
  11. 11.
    Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase Dependency Parsing for Opinion Mining. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, August 6-7, pp. 1533–1541 (2009)Google Scholar
  12. 12.
    Zhang, L., Liu, B., Lim, S.H., O’Brien-Strain, E.: Extracting and Ranking Product Features in Opinion Documents. In: Colling 2010: Poster Volume, Beejing, pp. 1462–1470 (August 2010)Google Scholar
  13. 13.
    Wei, C.-P., Chen, Y.-M., Yang, C.-S., Yang, C.C.: Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews. Springer (2009) (online)Google Scholar
  14. 14.
    Jung-Yeon, Kim, H.-J., Lee, S.-G.: Feature-based Product Review Summarization Utilizing User Score. Journal of Information Science and Engineering, 1973–1990 (2010)Google Scholar
  15. 15.
    Qadir, A.: Detecting Opinion Sentences Specific to Product Features in Customer Reviews using Typed Dependency Relations. In: Events in Emerging Text Types (eETTs), Borovets, Bulgaria, pp. 38–43 (2009)Google Scholar
  16. 16.
    Hatzivassiloglou, V., Wiebe, J.: Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In: Proc. of the 18th Conference on Computational Linguistics, Germany (2000)Google Scholar
  17. 17.
  18. 18.
    Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: VLDB 1994 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringThe National Institute of EngineeringMysoreIndia

Personalised recommendations