Application of text mining for customer evaluations in commercial banking

Jing Tan, Xiaojiang Du, Pengpeng Hao, Yanbo J. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationSeventh International Conference on Digital Image Processing, ICDIP 2015
EditorsCharles M. Falco, Xudong Jiang
ISBN (Electronic)9781628418293
DOIs
StatePublished - 2015
Event7th International Conference on Digital Image Processing, ICDIP 2015 - Los Angeles, United States
Duration: 9 Apr 201510 Apr 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9631
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference7th International Conference on Digital Image Processing, ICDIP 2015
Country/TerritoryUnited States
CityLos Angeles
Period9/04/1510/04/15

Keywords

  • Association rule mining
  • Classification
  • Commercial banking
  • Customer evaluation
  • Decision tree

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