@inproceedings{2d52ea89051a40509cb2e1b3d874d0e7,
title = "Application of text mining for customer evaluations in commercial banking",
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.",
keywords = "Association rule mining, Classification, Commercial banking, Customer evaluation, Decision tree",
author = "Jing Tan and Xiaojiang Du and Pengpeng Hao and Wang, \{Yanbo J.\}",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 7th International Conference on Digital Image Processing, ICDIP 2015 ; Conference date: 09-04-2015 Through 10-04-2015",
year = "2015",
doi = "10.1117/12.2197178",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
editor = "Falco, \{Charles M.\} and Xudong Jiang",
booktitle = "Seventh International Conference on Digital Image Processing, ICDIP 2015",
}