KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Discovering Hidden Emotional Heterogeneity of Customers in Textual Reviews and its Influencing Factors

  • Nasa Zata Dina (Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya) ;
  • Sri Devi Ravana (Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya) ;
  • Norisma Idris (Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, Universiti Malaya) ;
  • Tseng-Ping Chiu (Department of Industrial Design, National Cheng Kung University)
  • Received : 2024.06.18
  • Accepted : 2024.09.29
  • Published : 2024.10.31
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Abstract

E-commerce platforms are recognizing the value of customer experience and are dedicating sections for customers to share reviews of the product purchased. Therefore, this study aimed to analyze Online Customer Review (OCR) to identify hidden emotion expressed about the purchasing experience and further identify factors relating to the product. Text-based emotion classification is a prominent and growing field to better understand human emotions. An integrated Information Gain-Recursive Feature Elimination (IG-RFE) and stacking ensemble learning were implemented to develop a predictive emotion classification model to identify the hidden emotions of the customers. Additionally, the Latent Dirichlet Allocation (LDA) model was used to extract the influencing factors, providing further insight in OCR. The study extracted eight emotions from OCR and seven influencing factors from product's attributes. The emotions included anger, anticipation, disgust, fear, happiness, sadness, surprise, and trust while the identified factors were quality, brand credibility, product functionality, usability, appearance, price, and functional effect. The extracted emotions and factors from the OCR provided valuable knowledge on the study. The findings showed knowledge gaps in emotion classification and customer behavior fields, suggesting further investigation for future study.

Keywords

Acknowledgement

This research was supported by the Universiti Malaya International Collaboration Grant (grant number ST080-2022).

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