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Proceedings Paper

Application of text mining for customer evaluations in commercial banking
Author(s): Jing Tan; Xiaojiang Du; Pengpeng Hao; Yanbo J. Wang
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Paper Abstract

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

Paper Details

Date Published: 6 July 2015
PDF: 6 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963120 (6 July 2015); doi: 10.1117/12.2197178
Show Author Affiliations
Jing Tan, Temple Univ. (United States)
Xiaojiang Du, Temple Univ. (United States)
Pengpeng Hao, China Minsheng Banking Corp., Ltd. (China)
Institute of Finance and Banking (China)
Yanbo J. Wang, China Minsheng Banking Corp., Ltd. (China)
Institute of Finance and Banking (China)


Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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