Categorizing affective response of customer with novel explainable clustering algorithm: The case study of Amazon reviews

Abstract

Electronic word of mouth (e-WOM) influences consumer decision-making. Since consumers' affective experiences for products are vast, research is needed to understand and categorize them accurately. In this paper, we developed a deep learning-based clustering algorithm for categorizing consumer sentiment in product reviews and explored the applicability of this algorithm. A Deep Attentive Self-Organizing Map (DASOM) was created by noting individualized sentimental characteristics of each review and interpreting why each review was included in a particular cluster. As a result of analyzing 4941 reviews of Amazon, one of online commerce platforms, it was confirmed that sentiment classification through DASOM could be effectively used to categorize implicit affective experiences of consumers. DASOM was effective in identifying the relationship between multidimensional affective elements that were difficult to derive from TF-IDF. Using the proposed methodology, it is expected to provide practical information for companies that design products considering consumer affection.