IJAT Vol.12 No.4 pp. 482-491
doi: 10.20965/ijat.2018.p0482


A Study on Support Method of Consulting Service Using Text Mining

Ruriko Watanabe*,†, Nobutada Fujii*, Daisuke Kokuryo*, Toshiya Kaihara*, Yoichi Abe**, and Ryoko Santo***

*Kobe University
1-1 Rokkodai, Nada-ku, Kobe-city, Hyogo 657-8501, Japan

Corresponding author

**F&M, Co., Ltd., Suita, Japan

***The New Industry Research Organization, Kobe, Japan

February 19, 2018
May 24, 2018
Online released:
July 3, 2018
July 5, 2018
text mining, DEA discriminant analysis, correspondence analysis

This study aims to build a support method for consulting service companies allowing them to respond to client demands regardless of the expertise of the consultants. With an emphasis on the revitalization of small and medium-sized enterprises, the importance of support systems for consulting services for small and medium-sized enterprises, which support solving problems that are difficult to deal with by an enterprise, is increasing. Consulting companies can respond to a wide range of management consultations; however, because the contents of a consultation are widely and highly specialized, a service proposal and the problem detection depend on the experience and intuition of the consultant, and thus a stable service may occasionally not be provided. Therefore, a support system for providing stable services independent of the ability of consultants is desired. In this research, as the first step in constructing a support system, an analysis of customer information describing the content of a consultation with the client companies is conducted to predict the occurrence of future problems. Text data such as the consultant’s visitation history, consultation content by e-mail, and call center content are used in the analysis because the contents explain not only the current problems but also possibly contain future problems. This paper describes a method for analyzing the text data by employing text mining. In the proposed method, by combining a correspondence analysis with a DEA (Data Envelopment Analysis) discriminant analysis, words that are strongly related to problem detection are extracted from a large number of words obtained from text data, and variables of the DEA discriminant analysis are reduced and analyzed. The proposed method focuses on a cancellation of contract problems. The cancellation problem does not include uncertainty; it is clearly known whether the contract of the consulting service is being updated or cancelled. In this study, computer experiments were conducted to verify the effectiveness of the proposed method through a comparison with an existing method. The results of the verification experiment are as follows. First, there is a possibility of discovering new factors that cannot be determined from the intuition and experience of the consultant regarding the target problem. Second, through a comparison with the existing method, the effectiveness of the proposed method was confirmed.

Cite this article as:
Ruriko Watanabe, Nobutada Fujii, Daisuke Kokuryo, Toshiya Kaihara, Yoichi Abe, and Ryoko Santo, “A Study on Support Method of Consulting Service Using Text Mining,” Int. J. Automation Technol., Vol.12, No.4, pp. 482-491, 2018.
Data files:
  1. [1] “Small and Medium Enterprise Charter,” 2010.
  2. [2] The Small and Medium Enterprise Agency, [Accessed June 25, 2018]
  3. [3] “White Paper on Small and Medium Enterprises in Japan,” 2017.
  4. [4] K. Hori, “What is consulting,” PHP Institute, 2011.
  5. [5] F. Duan, M. Morioka, J. T. C. Tan, and T. Arai, “Multi-Modal Assembly-Support System for Cell Production,” Int. J. Automation Technol., Vol.2, No.5, pp. 384-389, 2008.
  6. [6] T. Kamma, T. Saito, and S. Abe, “Analysis and Adaptation for Exaggeration Types of Animation Motion,” Graphic Science, Vol.47, pp. 13-23, 2013.
  7. [7] T. Nasukawa, “Technology using text mining / Technology to make – essence and application derived from basic technology and application examples,” Tokyo Denki University Press, 2006 (in Japanese).
  8. [8] H. Wakimori, “Text Mining Techniques for Analyzing Big Data,” UNISYS Technology Review, Vol.115, 2013.
  9. [9] H. T. P. Thanh and P. Meesad, “Stock Market Trend Prediction Based on Text Mining of Corporate Web and Time Series Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.1, pp. 22-31, 2014.
  10. [10] J. Yano and K. Araki, “Performance Evaluation for a Method of Generating Business Reports from Call Center Speech Dialogues Using Inductive Learning,” Information Processing Society of Japan, 2007 (in Japanese).
  11. [11] H. Takase, H. Kawanaka, and S. Tsuruoka, “Supporting System for Quiz in Large Class – Automatic Keyword Extraction and Browsing Interface –,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.1, pp. 152-157, 2015.
  12. [12] M. Nii, K. Takahama, and S. Miyake, “Rule Representation for Nursing-Care Process Evaluation Using Decision Tree Techniques,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.16, pp. 918-925, 2015.
  13. [13] H. Murai and A. Tokosumi, “Network Analysis of the Four Gospels and the Catechism of the Catholic Church,” Vol.11, No.7, pp. 772-779, 2007.
  14. [14] M. A. Clatworthy and M. J. Jones, “Financial reporting of good news and bad news: evidence from accounting narratives,” Accounting and Business Research, Vol.33, No.3, pp. 171-185, 2006.
  15. [15] K. Izumi, T. Goto, and F. Matsui, “Long-term Financial Market Analysis Using Economic Textual Information,” Information Processing Society J., Vol.52, No.12, pp. 3309-3315, 2011 (in Japanese).
  16. [16] MeCab: Yet Another Part-of-Speech and Morphological Analyzer (in Japanese). [Accessed June 25, 2018]
  17. [17] S. Shida, T. Maeda, and M. Yamazaki, “Statistical Input for Language Research Gate,” Kuroshio Publishing, 2010 (in Japanese).
  18. [18] T. Sueyoshi, “DEA-discriminant analysis in the view of goal programming,” European J. of Operational Research, Vol.115, No.3, pp. 564-582, 1999.
  19. [19] G. Salton and C. Buckley, “Term-weighing approaches in automatic text retrieval,” Information Processing & Management, Vol.24, No.5, pp. 513-523, 1988.

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