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http://dx.doi.org/10.15207/JKCS.2020.11.12.113

The Analysis of Fashion Trend Cycle using Big Data  

Kim, Ki-Hyun (Division of Information System Engineering, Sungshin Women's University)
Byun, Hae-Won (Division of Information System Engineering, Sungshin Women's University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.12, 2020 , pp. 113-123 More about this Journal
Abstract
In this paper, big data analysis was conducted for past and present fashion trends and fashion cycle. We focused on daily look for ordinary people instead of the fashion professionals and fashion show. Using the social matrix tool, Textom, we performed frequency analysis, N-gram analysis, network analysis and structural equivalence analysis on the big data containing fashion trends and cycles. The results are as follows. First, this study extracted the major key words related to fashion trends for the daily look from the past(1980s, 1990s) and the present(2019 and 2020). Second, the frequence analysis and N-gram analysis showed that the fashion cycle has shorten to 30-40 years. Third, the structural equivalence analysis found the four representative clusters. The past four clusters are jean, retro codi, athleisure look, celebrity retro and the present clusters are retro, newtro, lady chic, retro futurism. Fourth, through the network analysis and N-gram analysis, it turned out that the past fashion is reproduced and evolves to the current fashion with certain reasoning.
Keywords
Convergence; Fashion Trend; Fashion Trend Cycle; Retro; Newtro; Big Data; Text Mining; TF-IDF; N-gram Analysis; Network Analysis; Concor Analysis;
Citations & Related Records
Times Cited By KSCI : 14  (Citation Analysis)
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