Topic analysis of online reviews for two competitive products using latent Dirichlet allocation

• Published in: Electronic commerce research and applications Vol. 29; pp. 142 - 156
• Main Authors: Wang, Wenxin, Feng, Yi, Dai, Wenqiang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2018
• Subjects: Competitive analysis; Latent Dirichlet allocation; Online product reviews; Product competition; Text mining; Topic analysis; Text mining; Latent Dirichlet allocation; Product competition; Online product reviews; Competitive analysis; Topic analysis
• ISSN: 1567-4223; 1873-7846
• DOI: 10.1016/j.elerap.2018.04.003

•Apply LDA to analyze competitive products using online reviews.•Analyze the competitive advantages and disadvantages of two competitive products.•Discover product feature complementarities in positive and negative reviews. The voice of the customer plays an important role in product competition. Traditional methods in the area have largely focused on market research and questionnaire surveys to obtain customer preferences. However, online product reviews have provided a good and reliable channel for not only understanding customers needs for one product or service but also analyzing products’ competition in the market. In this paper, we propose a new framework of applying online product reviews to analyze customer preferences for two competitive products. We extract the key topics of online reviews for two specific competitive products via a text mining approach of latent Dirichlet allocation (LDA). Topic difference analysis demonstrates the unique topics of the two products. The relative importance and topic heterogeneity analyses identify the competitive superiorities and weaknesses of both products. Two case studies that are presented demonstrate the efficacy of the proposed framework. The method also provides valuable managerial implications for product designers and e-commerce companies.