A study on the user perception in fashion design through social media text-mining

Abstract

This study seeks methods to analyze users' perception in fashion designs shown in social media using textmining analysis methods. The research methods selected 'men's stripe shirts' as subjects and collected texts related to the subject mainly from blogs. Texts from 13,648 posts from November 1st, 2015 to October 31st, 2016 were analyzed by applying the LDA algorithm and content analysis. As a result, the wearing status per season and subjects of men's stripe shirts were derived. Across the entire period, the main topics discussed by users to be pattern, customized suits, brands, coordination and purchase information. In terms of seasons, spring time showed the sharing of information on coordinating daily looks or boyfriend looks, and during the winter season the information shared were about shirts suitable for special occasions such as job interviews and stripe shirts that match suits. The study results showed that text-mining analysis is capable of analyzing the context and provide a user-centered index responding to demands newly mentioned by users along with the rapid changes in fashion design trends. © 2017, The Korean Society of Clothing and Textiles.