Analyzing genderless fashion trends of consumers’ perceptions on social media: using unstructured big data analysis through Latent Dirichlet Allocation-based topic modeling

Abstract

AbstractAfter the development of Web 2.0 and social networks, analyzing consumers’ responses and opinions in real-time became profoundly important to gain business insights. This study aims to identify consumers’ preferences and perceptions of genderless fashion trends by text-mining, Latent Dirichlet Allocation-based topic modeling, and time-series linear regression analysis. Unstructured text data from consumer-posted sources, such as blogs and online communities, were collected from January 1, 2018 to December 31, 2020. We examined 9722 posts that included the keyword “genderless fashion” with Python 3.7 software. Results showed that consumers were interested in fragrances, fashion, and beauty brands and products. In particular, 18 topics were extracted: 13 were classified as fashion categories and 5 were derived from beauty and fragrance sectors. Examining the genderless fashion trend development among consumers from 2018 to 2020, “perfume and scent” was revealed as the hot topic, whereas “bags,” “all-in-one skin care,” and “set-up suit” were cold topics, declining in popularity among consumers. The findings contribute to contemporary fashion trends and provide in-depth knowledge about consumers’ perceptions using big data analysis methods and offer insights into product development strategies.