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Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing

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Abstract

A growing body of research suggests that affective computing has many valuable applications in enterprise systems research and e-businesses. This paper explores affective computing techniques for a vital sub-area in enterprise systems—consumer satisfaction measurement. We propose a linguistic-based emotion analysis and recognition method for measuring consumer satisfaction. Using an annotated emotion corpus (Ren-CECps), we first present a general evaluation of customer satisfaction by comparing the linguistic characteristics of emotional expressions of positive and negative attitudes. The associations in four negative emotions are further investigated. After that, we build a fine-grained emotion recognition system based on machine learning algorithms for measuring customer satisfaction; it can detect and recognize multiple emotions using customers’ words or comments. The results indicate that blended emotion recognition is able to gain rich feedback data from customers, which can provide more appropriate follow-up for customer relationship management.

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Acknowledgments

This research has been partially supported by the National High-Tech Research & Development Program of China 863 Program under Grant No.2012AA011103, and the Ministry of Education, Science, Sports and Culture of Japan under Grant-in-Aid for Scientific Research (A) No. 22240021.

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Authors and Affiliations

  1. Faculty of Engineering, University of Tokushima, 2-1 Minami-Josanjima, Tokushima, 770-8506, Japan

    Fuji Ren

  2. AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, HeFei University of Technology, Tunxi Road 193, Hefei, 230009, China

    Changqin Quan

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  1. Fuji Ren
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Correspondence to Changqin Quan.

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Ren, F., Quan, C. Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing. Inf Technol Manag 13, 321–332 (2012). https://doi.org/10.1007/s10799-012-0138-5

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