Can Online Consumer Reviews Identify Key Evidence Regarding Common Consumer Choices for High-Tech Pet Products?

Abstract

Online consumer reviews (OCRs) significantly influence consumer purchase decisions for new products. Therefore, today’s companies actively seek practical approaches for analyzing these OCRs. This study proposes a comprehensive method for OCR analysis using topic modeling and association rule analysis to overcome the current limitations in topic interpretation and topic overlap in text mining. Meanwhile, to examine the development of the high-tech industry and customer interest in the pet care field, this study synthesizes and analyzes reviews from consumers who are using healthcare products in the pet industry. To this end, we first collected 20,820 customer reviews from Amazon.com (accessed on 2 August 2023) for high-tech pet products and categorized them into three distinct product categories. Topic modeling was then conducted on each category, revealing five key topics per category. Subsequently, association rules analysis was performed on the customer reviews associated with the most representative topic. As a result, we were able to demonstrate that ‘satisfaction’ emerged as the most crucial topic across all three categories of high-tech pet products. Satisfaction is a topic expressing consumers’ attitudes after experiencing the product, and they used words to describe their feelings in the product reviews. A diverse range of associated terms was also identified that represented the essence of each product’s corresponding representative explanations. By leveraging these approaches, we are confident that pet product companies and market players will gain valuable insights into consumer preferences and behavior. © 2023 by the authors.