Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63917

Enhancing Customer Satisfaction Analysis with a Machine Learning Approach: From a Perspective of Matching Customer Comment and Agent Note

File Size Format  
0144.pdf 512.57 kB Adobe PDF View/Open

Item Summary

Title:Enhancing Customer Satisfaction Analysis with a Machine Learning Approach: From a Perspective of Matching Customer Comment and Agent Note
Authors:Wei, Qiang
Shi, Xiaowei
Li, Quan
Chen, Guoqing
Keywords:Machine Learning and Predictive Analytics in Accounting, Finance and Management
machine learning
ugc
customer satisfaction
matching
show 1 morenps
show less
Date Issued:07 Jan 2020
Abstract:With the booming of UGCs, customer comments are widely utilized in analyzing customer satisfaction. However, due to the characteristics of emotional expression, ambiguous semantics and short text, sentiment analysis with customer comments is easily biased and risky. This paper introduces another important UGC, i.e., agent notes, which not only effectively complements customer comment, but delivers professional details, which may enhance customer satisfaction analysis. Moreover, detecting the mismatch on aspects between these two UGCs may further help gain in-depth customer insights. This paper proposes a machine learning based matching analysis approach, namely CAMP, by which not only the semantics and sentiment in customer comments and agent notes can be sufficiently and comprehensively investigated, but the granular and fine-grained aspects could be detected. The CAMP approach can provide practical guidance for following-up service, and the automation can help speed-up service response, which essentially improves customer satisfaction and retains customer loyalty.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63917
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.178
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance and Management


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons