Journal of Information Technology Services (한국IT서비스학회지)

Korea Society of IT Services (한국IT서비스학회)
권호 표지
DOI QR코드

DOI QR Code

A Multi-Dimensional Issue Clustering from the Perspective Consumers' Interests and R&D

소비자 선호 이슈 및 R&D 관점에서의 다차원 이슈 클러스터링

  • 현윤진 (국민대학교 비즈니스IT전문대학원) ;
  • 김남규 (국민대학교 경영정보학부) ;
  • 조윤호 (국민대학교 경영학부)
  • Received : 2014.07.25
  • Accepted : 2014.12.22
  • Published : 2015.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.

Keywords

References

  1. Albright, R., "Taming Text with the SVD", SAS Institute Inc, 2006.
  2. Bae, J., J. Son, and M. Song, "Analysis of Twitter for 2012 South Korea Presidential Election by Text Mining Techniques", Journal of Intelligence and Information Systems, Vol. 19, No.3, 2013, 141-156. https://doi.org/10.13088/jiis.2013.19.3.141
  3. Cho, I. and N. Kim, "Recommending Core and Connecting Keywords of Research Area Using Social Network and Data Mining Techniques", Journal of Intelligence and Information Systems, Vol.17, No.1, 2011, 127-138.
  4. Choi, C., "A Study on the Informal Networks in Organizations : An Application of Social Network Analysis", Korean Society and Public Administration, Vol.17, No.1, 2006, 1-23.
  5. Han, J., M. Kamber, and J. Pei, Data Mining : Concepts and Techniques, 3rd Edition, Morgan Kaufmann Publishers, 2011.
  6. Hearst, M.A., "Untangling Text Data Mining", in Proceedings of the 37th ACL, 1999.
  7. Hong, J., H. Choi, H. Han, J. Kim, E. Yu, S. Lim, W.X.S. Wong, and N. Kim, "A Methodology for Packaging R&D Information on Pending National Issues", Entrue Journal of Information Technology, Vol.13, No.1, 2014, 97-111.
  8. Hyun, Y., H. Han, H. Choi, J. Park, K. Lee, K. Kwahk, and N. Kim, "Methodology Using Text Analysis for Packaging R&D Information Services on Pending National Issues", Journal of Information Technology Applications and Management, Vol.20, No.3, 2013, 231-257.
  9. Kauffman, S., The Origins of Order, Oxford University Press, 1993.
  10. Kim, Y. Social Network Analysis, Parkyoungsa : Seoul, 2003.
  11. Kwahk, K., Social Network Analysis, Chungram : Seoul, 2013.
  12. McKinsey Global Institute, Big Data : The next Frontier for Innovation, Competition, and Productivity, McKinsey and Company, 2011.
  13. Mooney, R.J. and R. Bunescu, "Mining Knowledge from Text using Information Extraction", ACM SIGKDD Explorations, Vol.7, No.1, 2006, 3-10.
  14. O'Reilly Radar Team, Big Data Now : Current Perspectives from O'Reilly Radar, O'Reilly, 2011.
  15. Provost, F. and T. Fawcett, Data Science for Business, O'Reilly, 2013.
  16. Salton, G., A. Wong, and C.S. Yang, "A Vector Space Model for Automatic Indexing", Communications of the ACM, Vol.18, No.11, 1975, 613-620. https://doi.org/10.1145/361219.361220
  17. Sebastiani, F., Classification of Text, Automatic, The Encyclopedia of Language and Linguistics 14, 2nd Edition, Elsevier Science Pub, 2006.
  18. Sebastiani, F., "Machine Learning in Automated Text Categorization", ACM Computing Surveys, Vol.34, No.1, 2002, 1-47. https://doi.org/10.1145/505282.505283
  19. Stanvrianou, A., P. Andritsos, and N. Nicoloyannis, "Overview and Semantic Issues of Text Mining", ACM SIGMOD Record, Vol.36, No.3, 2007, 23-34. https://doi.org/10.1145/1324185.1324190
  20. Witten, I.H., Text Mining, Practical Handbook of Internet Computing, CRC Press, 2004.