Advertisement

A Comparison Study of Clustering Models for Online Review Sentiment Analysis

  • Baojun Ma
  • Hua Yuan
  • Qiang Wei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7923)

Abstract

In this work, we conduct a comparison study of the online review sentiment clustering problem from a combined perspective of data preprocessing, VSM modeling and clustering algorithm. To that end, we first introduce some methods for data preprocessing. Then, we explore the impacts of the term weighting models for review representation. Finally, we present detailed experiment results of some review clustering techniques. The conclusions would be valuable for both the study and usage of clustering methods in online review sentiment analysis.

Keywords

Online review sentiment analysis term weighting model clustering algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhu, F., (Michael) Zhang, X.: Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing 74(2), 133–148 (2010)Google Scholar
  2. 2.
    Yi, J., Nasukawa, T., Niblack, W., Bunescu, R.: Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of the ICDM 2003, Florida, USA, pp. 427–434 (2003)Google Scholar
  3. 3.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  4. 4.
    Prabowo, R., Thelwall, M.: Sentiment analysis: A combined approach. Journal of Informetrics 3, 143–157 (2009)CrossRefGoogle Scholar
  5. 5.
    Anick, P., Vaithyanathan, S.: Exploiting Clustering and Phrases for Context-Based Information Retrieval. In: Proceedings of the 20th ACM SIGIR, pp. 314–323 (1997)Google Scholar
  6. 6.
    Salton, G., Wong, A., et al.: A vector space model for automatic indexing. Communication of the ACM 18(11), 613–620 (1975)zbMATHCrossRefGoogle Scholar
  7. 7.
    Liu, B.: Sentiment Analysis and Opinion Mining. Morgan and Claypool Publishers (May 2012)Google Scholar
  8. 8.
    Li, G., Liu, F.: Application of a clustering method on sentiment analysis. Journal of Information Science 38(2), 127–139 (2012)CrossRefGoogle Scholar
  9. 9.
    Zhai, Z., Liu, B., Xu, H., Jia, P.: Clustering product features for opinion mining. In: Proceedings of the WSDM 2011, New York, USA, pp. 347–354 (2011)Google Scholar
  10. 10.
    Wang, D., Feng, S., Yan, C., Yu, G.: An approach of semi-automatic public sentiment analysis for opinion and district. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds.) WAIM 2011. LNCS, vol. 7142, pp. 210–222. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Whissell, J.S., Clarke, C.L.: Improving document clustering using Okapi BM25 feature weighting. Information Retrieval 14(5), 466–487 (2011)CrossRefGoogle Scholar
  12. 12.
    Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining (2000)Google Scholar
  13. 13.
    Toutanova, K., Manning, C.D.: Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In: Proceedings of the EMNLP/VLC 2000, Hong Kong, pp. 63–70 (2000)Google Scholar
  14. 14.
    Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Baojun Ma
    • 1
    • 3
  • Hua Yuan
    • 2
  • Qiang Wei
    • 1
  1. 1.School of Economics and ManagementTsinghua UniversityBeijingChina
  2. 2.School of Management and EconomicsUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.School of Economics and ManagementBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations