Published in Data Science and Engineering (DSE) Record 2024 Vol. 5 No. 1 pp. 243-260
Abstract
Online Consumer Reviews (OCR) significantly influence consumers' purchasing decisions for new products. Consequently, companies seek effective ways to analyze consumer opinions. The pet market has seen remarkable growth and is of particular interest, especially within the context of "Pet Humanization", where pets are cared for with love and attention akin to family members. This study aims to analyze customer reviews and discover consumer insights related to high-tech pet products, specifically automatic pet feeders. Using text mining techniques and Natural Language Processing (NLP), 15,558 customer reviews from the e-commerce market were collected to uncover consumer trends and preferences. This research utilizes Latent Dirichlet Allocation (LDA) for topic modeling to analyze customer opinions, with the results visualized using tools such as WordCloud and pyLDAvis. The findings reveal three main topics: Functionality, Performance, and Value and Quality Assessment. The study indicates that consumers prioritize product integration into daily life, reliability and ease of maintenance, and the overall quality and value of the product. Keywords: Online Customer Review, Text Mining, Topic Modeling, Pet Product, Consumer Insights