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IJAT Vol.14 No.5 pp. 779-790
doi: 10.20965/ijat.2020.p0779
(2020)

Paper:

Text Mining to Support Consulting Services for Client Company State Recognition

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

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

Corresponding author

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

Received:
April 3, 2020
Accepted:
July 20, 2020
Published:
September 5, 2020
Keywords:
correspondence analysis, DEA discriminant analysis, text mining
Abstract

This study was conducted to devise a method for supporting consulting service companies in their response to client demands irrespective of the expertise of consultants. With emphasis on revitalization of small and medium-sized enterprises, the importance of support systems for consulting services to serve them is increasing. Those systems must support solutions to difficulties that must be addressed by enterprises. Consulting companies can respond to widely various management consultations. Nevertheless, because the consultation contents are highly specialized, service proposals and problem detection depend on the experience and intuition of the consultant. Often, stable service cannot be provided. A support system must provide stable services independent of the ability of consultants. In this study, analyzing customer information describing the contents of consultation with client companies is the first step in constructing a support system that can predict future problems. Text data such as a consultant’s visit history, consultation contents by e-mail, and contents of call centers are used for analyses because the contents can explain current problems. They might also indicate future problems. This report describes a method to analyze text data using text mining. The target problem is fraud, which includes uncertainty: cases in which it is not clear whether a fraud problem has occurred with the company. To address uncertainty, a method of using logistic regression models is proposed to represent inferred values as probabilities, rather than as binary discriminated data, because the possibility exists that some misidentified companies might have some difficulty. As described herein, computer experiments are conducted to verify the effectiveness of the proposed method and to compare consultants’ forecasted and achieved results. Results of a verification experiment are presented in the following. First, the proposed method is applicable to problems including uncertainties. Secondly, the possibility exists of discovering companies with a fraud problem of which they are unaware.

Cite this article as:
Ruriko Watanabe, Nobutada Fujii, Daisuke Kokuryo, Toshiya Kaihara, and Yoichi Abe, “Text Mining to Support Consulting Services for Client Company State Recognition,” Int. J. Automation Technol., Vol.14, No.5, pp. 779-790, 2020.
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