Discovering Hidden Emotional Heterogeneity of Customers in Textual Reviews and its Influencing Factors
Document Type
Article
Publication Date
10-1-2024
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
customer behavior, emotion classification, ensemble learning, feature selection, online customer review, topic modelling
Divisions
ai,infosystem
Funders
Universiti Malaya International Collaboration Grant (ST080-2022)
Publication Title
KSII Transactions on Internet and Information Systems
Volume
18
Issue
10
Publisher
Korean Society for Internet Information
Publisher Location
KOR SCI & TECHNOL CTR, 409 ON 4TH FLR, MAIN BLDG, 635-4 YEOKSAM 1-DONG, GANGNAM-GU, SEOUL 00000, SOUTH KOREA