Extraction of Minority Opinion Based on Peculiarity in a Semantic Space Constructed of Free Writing

Analysis of Online Customer Reviews as an Example
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 271)

Abstract

Recently, the “voice of the customer (VOC)” such as exhibited by a Web user review has become easily collectable as text data. Based on large quantity of collected review data, we take the stance that minority review sentences buried in a majority review have high value. It is conceivable that hints of solution and discovery of new subjects are hidden in such minority opinions. The purpose of this research is to extract minority opinion from a huge quantity of text data taken from free writing in user reviews of products and services. In this study, we propose a method for extracting minority opinions that become outliers in a low-dimensional semantic space. Here, a low-dimensional semantic space of Web user reviews is constructed by latent semantic indexing (LSI). We were able to extract minority opinions using the Peculiarity Factor (PF) for outlier detection. We confirmed the validity of our proposal through an analysis using the user reviews of the EC site.

Keywords

Text Mining Online Reviews Peculiarity Factor (PF) Dimensionality Reduction Voice of the Customer (VOC) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jain, S.S., Meshram, B.B., Singh, M.: Voice of customer analysis using parallel association rule mining. In: IEEE Students’ Conference on Electrical Electronics and Computer Science (SCEECS), pp. 1–5 (2012)Google Scholar
  2. 2.
    Peng, W., Sun, T., Revankar, S.: Mining the “Voice of the Customer” for Business Prioritization. ACM Transactions on Intelligent Systems and Technology 3(2), Article 38 (2012)Google Scholar
  3. 3.
    Yamashita, T., Matsumoto, Y.: Language independent morphological analysis. In: Proceedings of the Sixth Conference on Applied Natural Language Processing (ANLC 2000), pp. 232–238 (2000)Google Scholar
  4. 4.
    Kudo, T., Yamamoto, K., Matsumoto, Y.: Applying Conditional Random Fields to Japanese Morphological Analysis. In: Proceedings of Conference on Empirical Methods in Natural Language Processing 2004, pp. 230–237 (2004)Google Scholar
  5. 5.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the Society for Information Science 41, 391–407 (1990)CrossRefGoogle Scholar
  6. 6.
    Niu, Z., Shi, S., Sun, J., He, X.: A Survey of Outlier Detection Methodologies and Their Applications. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds.) AICI 2011, Part I. LNCS, vol. 7002, pp. 380–387. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Yang, J., Zhong, N., Yao, Y., Wang, J.: Peculiarity Analysis for Classifications. In: Proceeding of IEEE International Conference on Data Mining, pp. 608–616 (2009)Google Scholar
  8. 8.
    Buchanan, M.: Ubiquity: Why Catastrophes Happen. Broadway (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Industrial and Systems Engineering, College of Science and EngineeringAoyama Gakuin UniversitySagamihara CityJapan

Personalised recommendations