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Mining from open answers in questionnaire data

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Published:26 August 2001Publication History

ABSTRACT

Surveys are an important part of marketing and customer relationship management, and open answers (i.e., answers to open questions) in particular may contain valuable information and provide an important basis for making business decisions. We have developed a text mining system that provides a new way for analyzing open answers in questionnaire data. The product is able to perform the following two functions: (A) accurate extraction of characteristics for individual analysis targets, (B) accurate extraction of the relationships among characteristics of analysis targets. In this paper, we describe the working of our text mining system. It employs two statistical learning techniques: rule analysis and Correspondence Analysis for performing the two functions. Our text mining system has already been put into use by a number of large corporations in Japan in the performance of text mining on various types of survey data, including open answers about brand images, open answers about company images, complaints about products, comments written on home pages, business reports, and help desk records. In this it has been found to be useful in forming a basis for effective business decisions.

References

  1. 1.M.R. Anderberg, Cluster Analysis for Applications, Academic Press, 1973.Google ScholarGoogle Scholar
  2. 2.J.P. Benzecri, Correspondence Analysis Handbook. Mercel Dekker, i 992.Google ScholarGoogle Scholar
  3. 3.Jochen Dorre and Peter Gerstl and Roland Seiffert, Text mining: finding nuggets in mountains of textual data, Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining, 398-401, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 4.Ronen Feldman and Ido Dagan, Knowledge discovery in textual databases (KDT), Proceedings of First International Conference on Knowledge Discovery and Data Mining, 1995.Google ScholarGoogle Scholar
  5. 5.Fujitsu, Symfoware World http://www.fuiitsu.co.ip/ip/soft/symfoware/index.html, 2001.Google ScholarGoogle Scholar
  6. 6.Marko Grobelnik, Dunja Mladenic, and Natasa Milic-Fraling (Ed.) Proceedings of KDD-2000 Workshop on Text Mining, 2000.Google ScholarGoogle Scholar
  7. 7.Marti Hearst, Untangling text data mining, Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, 3-10, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8.Komatsu Soft, Information Mining Tool VextSearch (in Japanese) http://www.komatsusoft.co.jp/develp/vxtsc/index.html, 2001.Google ScholarGoogle Scholar
  9. 9.Brian Lent and Rakesh Agrawal and Ramakrishnan Srikant, Discovering trends in text databases, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, 227-230, 1997.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 10.Hang Li and Kenji Yamanishi, Text classification using ESC-based stochastic decision lists, Proceedings of the 8th International Conference on Information and Knowledge Management, 122-130, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 11.Hang Li and Kenji Yamanishi, Topic analysis using a finite mixture model, Proceedings of 2000 Joint ACL-SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 35-44, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.Jorma Rissanen, Fisher information and stochastic complexity, IEEE Transaction on Information Theory, 42(1):40- 47, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. 13.Russell Swan and James Allan, Extracting significant time varying features from text, Proceedings of the 8th International Conference on Information and Knowledge Management, 45, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.Mark Shewhart and Mark Wasson, Monitoring a newsfeed for hot topics, Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining, 402-404, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15.Kenji Yamanishi, A learning criterion for stochastic rules, Machine Learning, 9:165-203, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16.Kenji Yamanishi, A decision-theoretic extension of stochastic complexity and its applications to learning, IEEE Transaction on Infortmation Theory.,44(4): 1424-1439, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Conferences
        KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
        August 2001
        493 pages
        ISBN:158113391X
        DOI:10.1145/502512

        Copyright © 2001 ACM

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        New York, NY, United States

        Publication History

        • Published: 26 August 2001

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        KDD '01 Paper Acceptance Rate31of237submissions,13%Overall Acceptance Rate1,133of8,635submissions,13%

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