A Study on the Method for Extracting the Purpose-Specific Customized Information from Online Product Reviews based on Text Mining

텍스트 마이닝 기반의 온라인 상품 리뷰 추출을 통한 목적별 맞춤화 정보 도출 방법론 연구

  • Kim, Joo Young (Dept. of Industrial and Information Systems Engineering, Soongsil University) ;
  • Kim, Dong soo (Dept. of Industrial and Information Systems Engineering, Soongsil University)
  • Received : 2016.04.18
  • Accepted : 2016.05.23
  • Published : 2016.05.31


In the era of the Web 2.0, characterized by the openness, sharing and participation, it is easy for internet users to produce and share the data. The amount of the unstructured data which occupies most of the digital world's data has increased exponentially. One of the kinds of the unstructured data called personal online product reviews is necessary for both the company that produces those products and the potential customers who are interested in those products. In order to extract useful information from lots of scattered review data, the process of collecting data, storing, preprocessing, analyzing, and drawing a conclusion is needed. Therefore we introduce the text-mining methodology for applying the natural language process technology to the text format data like product review in order to carry out extracting structured data by using R programming. Also, we introduce the data-mining to derive the purpose-specific customized information from the structured review information drawn by the text-mining.


  1. Archak, N., Ghose, A., and Ipeirotis, P. G., "Deriving the pricing power of product features by mining consumer reviews," Management Science, Vol. 57, No. 8, pp. 1485-1509, 2011.
  2. Baars, H. and Kemper, H.-G., "Management support with structured and unstructured data-an integrated business intelligence framework," Information Systems Management, Vol. 25, No. 2, pp. 132-148, 2008.
  3. Blumberg, R. and Atre, S., "The problem with unstructured data," DM Review Magazine, 2003.
  4. Buneman, P., "Semistructured data," Proceedings of the sixteenth ACM SIGACTSIGMOD-SIGART symposium on Principles of database systems, ACM, 1997.
  5. Chevalier, J. A. and Mayzlin, D., "The effect of word of mouth on sales: Online book reviews," Journal of marketing research, Vol. 43, No. 3, pp. 345-354, 2006.
  6. Collins, M., Head-driven statistical models for natural language parsing, Computational linguistics, Vol. 29, No. 4, pp. 589-637, 2003.
  7. Decker, R. and Trusov, M., "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Vol. 27, No. 4, pp. 293-307, 2010.
  8. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P., "From data mining to knowledge discovery in databases," AI magazine, Vol. 17, No. 3, pp. 37-54, 1996.
  9. Holton, C., "Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem," Decision Support Systems, Vol. 46, No. 4, pp. 853-864, 2009.
  10. Kangale, A., Kumara, S. K., Naeema, M. A., Williamsb, M., and Tiwaria, M. K., "Mining consumer reviews to generate ratings of different product attributes while producing feature-based review-summary," International Journal of Systems Science, Vol. 47, No. 13, pp. 1-15, 2016.
  11. Kozinets, R. V., de Valck, K., Wojnicki, A. C., and Wilner, S. J. S., "Networked narratives: Understanding word-of-mouth marketing in online communities," Journal of marketing, Vol. 74, No. 2, pp. 71-89, 2010.
  12. Lee, J., "How eWOM Reduces Uncertainties in Decision-making Process: Using the Concept of Entropy in Information Theory," The Journal of Society for e-Business, Vol. 16, No. 4, pp. 241-256, 2011.
  13. Mangold, C., "A survey and classification of semantic search approaches," International Journal of Metadata, Semantics and Ontologies, Vol. 2, No. 1, pp. 23-34, 2007.
  14. Mayer-Schonberger, V. and Cukier, K., Big data: A revolution that will transform how we live, work, and think., Houghton Mifflin Harcourt, 2013.
  15. McAfee, A. and Brynjolfsson, E., "Big data," The management revolution, Harvard Bus Rev, Vol. 90, No. 10, pp. 61-67, 2012.
  16. Mei, Q. and Zhai, C. X., "Discovering evolutionary theme patterns from text: an exploration of temporal text mining," Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, ACM, 2005.
  17. O'reilly, T., "What is Web 2.0: Design patterns and business models for the next generation of software," Communications and strategies, No. 1, p. 17, 2007.
  18. Tan, A.-H., "Text mining: The state of the art and the challenges," Proceedings of the PAKDD 1999 Workshop on Knowledge Discovery from Advanced Databases, pp. 65-70, 1999.
  19. Washio, T. and H. Motoda., "State of the art of graph-based data mining," Acm Sigkdd Explorations Newsletter, Vol. 5, No. 1, pp. 59-68, 2003.
  20. Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., and Malone, T. W., "Evidence for a collective intelligence factor in the performance of human groups," science, Vol. 330, No. 6004, pp. 686-688, 2010.

Cited by

  1. Analyzing and visualizing comprehensive and personalized online product reviews pp.1573-7543, 2018,