Using Topic Modeling for Extracting Customers’ Expectations: A Case of Women Apparel

Abstract

Increased internet usage has fueled significant growth in online retailing. The textile business has benefited in all countries thanks to the surge in online sales. Women’s fashion is having a huge impact on the world stage as women are the most concerned about what they wear and also new attractive clothes are released regularly. Electronic word of mouth (EWOM) is considered to be an important source of information and helps potential customers in purchasing decision-making. This study attempts to gather in-depth information about the women’s apparel sector by mining these EWOMs. Topic modeling through latent Dirichlet allocation (LDA) is utilized here for extracting the key aspects about which the customers chat in their reviews that are posted online. A total of 23,486 reviews were accessed on which the LDA technique was applied after preprocessing the data for potential cleaning. LDA technique resulted in six topics, namely, aesthetic, functionality, expressive, performance, extrinsic, and return policy. These are the aspects that the customers mention while commenting about their shopping experience. The findings of this study provide the key aspects that customers expect while purchasing women’s apparel online. The study discusses some implications for online marketers that may help them achieve a competitive edge.