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Exploring customer sentiment regarding online retail services: A topic-based approach

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  • Wu, Jia-Jhou
  • Chang, Sue-Ting

Abstract

User-generated content is a valuable source for understanding online shoppers' emotions. Using text-mining techniques, this study identifies seven topics regarding online retail services in online posts: product, retailer promotion, delivery, payment, communication, return/refund, and price. The topics are associated with the sentiment polarity of online shoppers' posts. This study further explores whether the emotional responses from domestic and cross-border online shoppers differ with regard to these topics. The results show that differences exist in these two groups' sentiments concerning product and payment. Furthermore, there are differences in the two groups’ respective negative emotions (i.e., anger, sadness, and fear) concerning delivery, communication, and return/refund. The findings of this study provide online retailers with important managerial implications.

Suggested Citation

  • Wu, Jia-Jhou & Chang, Sue-Ting, 2020. "Exploring customer sentiment regarding online retail services: A topic-based approach," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    • Handle: RePEc:eee:joreco:v:55:y:2020:i:c:s096969891930997x
    DOI: 10.1016/j.jretconser.2020.102145
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    9. Md Shamim Hossain & Mst Farjana Rahman, 2023. "Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches," FIIB Business Review, , vol. 12(4), pages 386-402, December.
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