쾌락적․실용적 성향의 온라인 소비자 불만과 기업 대응 패턴

초록

In the evolving landscape of e-commerce, negative online reviews play a crucial role in shaping consumer perceptions and influencing future purchasing decisions. This study applies text mining techniques to analyze online customer reviews, focusing on two product categories—hedonic and utilitarian—and their corresponding seller responses. By employing sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling, the research identifies key themes in negative reviews, highlighting the specific aspects of the purchasing experience that consumers emphasize when expressing dissatisfaction. Furthermore, the study investigates seller response patterns to evaluate the effectiveness of different response strategies in mitigating the impact of negative feedback. The sentiment analysis results reveal that consumer concerns differ by product type: buyers of utilitarian products primarily underscore functional failures, whereas buyers of hedonic products tend to emphasize emotional dissatisfaction. These findings provide actionable insights for businesses, contributing to a deeper understanding of consumer behavior in the context of negative reviews. The results can guide the development of more effective crisis management strategies and optimized response mechanisms, ultimately fostering consumer trust and encouraging repurchase intentions.

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