TY - JOUR
T1 - Categorizing affective response of customer with novel explainable clustering algorithm
T2 - The case study of Amazon reviews
AU - Kim, Wonjoon
AU - Nam, Keonwoo
AU - Son, Youngdoo
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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 multi-dimensional 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.
AB - 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 multi-dimensional 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.
KW - Attention mechanism
KW - Electronic word of mouth
KW - Explainable artificial intelligence
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85149616337&partnerID=8YFLogxK
U2 - 10.1016/j.elerap.2023.101250
DO - 10.1016/j.elerap.2023.101250
M3 - Article
AN - SCOPUS:85149616337
SN - 1567-4223
VL - 58
JO - Electronic Commerce Research and Applications
JF - Electronic Commerce Research and Applications
M1 - 101250
ER -