APPLICATION OF SUPPORT SYSTEMS FOR CONSULTING SERVICE TO REAL PROBLEM BY USING A SYNONYM DICTIONARY

• Main Authors: Ruriko WATANABE, Nobutada FUJII, Daisuke KOKURYO, Toshiya KAIHARA, Yoichi ABE
• Format: Article
• Language: English
• Published: Technical University of Kosice 2020-08-01
• Series: Acta Electrotechnica et Informatica
• Subjects: text mining; correspondence analysis; dea discriminant analysis
• Online Access: http://www.aei.tuke.sk/papers/2020/2/01_Watanabe.pdf
• ISSN: 1335-8243; 1338-3957

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, are 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 research proposed 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. This paper describes improved method for the application in the real problem. The method is improved to eliminate the following two problems. First, IDF values are used to extract more general phrases. Second, in order to reduce the number of companies that cannot be identified, it is used standardization and data are expanded with synonym dictionaries. In this study, computer experiments were conducted to verify the effectiveness of the improved 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.