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

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dc.contributor.author Wei, Qiang
dc.contributor.author Shi, Xiaowei
dc.contributor.author Li, Quan
dc.contributor.author Chen, Guoqing
dc.date.accessioned 2020-01-04T07:26:44Z
dc.date.available 2020-01-04T07:26:44Z
dc.date.issued 2020-01-07
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.178
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63917
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Predictive Analytics in Accounting, Finance and Management
dc.subject machine learning
dc.subject ugc
dc.subject customer satisfaction
dc.subject matching
dc.subject nps
dc.title Enhancing Customer Satisfaction Analysis with a Machine Learning Approach: From a Perspective of Matching Customer Comment and Agent Note
dc.type Conference Paper
dc.type.dcmi Text
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