Merchandising/Marketing/Retailing: Management

Online Adaptive Clothing Shopping Experience: Text-mining of Product Titles and Consumer Reviews

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Abstract

This paper aims to discover the current needs of adaptive clothing consumers and how consumers assess the adaptive clothing product in the context of online shopping, through a text-mining approach. 253 adaptive clothing product titles and 1060 customer reviews posted on Silvert’s, an adaptive clothing website were collected using Python. The terms associated with needs and target consumers were selected and coded guided by the FEA theory. Then the Latent Dirichlet Allocation (LDA) was implemented to extract the topics from consumer reviews. The finding suggests that the functional needs are most important from both marketers’ and consumers’ perspective in adaptive clothing market. Online marketers mainly use functional words to introduce the products, while only few words are related to aesthetic. For future research, data from other resources, such as social media, will be collected to further explore the online searching habits of adaptive clothing consumers.

Keywords: LDA algorithm, FEA theory, text analysis, Adaptive clothing

How to Cite:

Li, M. & Zhao, L., (2020) “Online Adaptive Clothing Shopping Experience: Text-mining of Product Titles and Consumer Reviews”, International Textile and Apparel Association Annual Conference Proceedings 77(1). doi: https://doi.org/10.31274/itaa.12061