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 language | English |
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Title of host publication | Seventh International Conference on Digital Image Processing, ICDIP 2015 |
Editors | Charles M. Falco, Xudong Jiang |
ISBN (Electronic) | 9781628418293 |
DOIs | |
State | Published - 2015 |
Event | 7th International Conference on Digital Image Processing, ICDIP 2015 - Los Angeles, United States Duration: 9 Apr 2015 → 10 Apr 2015 |
Publication series
Name | Proceedings of SPIE - The International Society for Optical Engineering |
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Volume | 9631 |
ISSN (Print) | 0277-786X |
ISSN (Electronic) | 1996-756X |
Conference
Conference | 7th International Conference on Digital Image Processing, ICDIP 2015 |
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Country/Territory | United States |
City | Los Angeles |
Period | 9/04/15 → 10/04/15 |
Keywords
- Association rule mining
- Classification
- Commercial banking
- Customer evaluation
- Decision tree