Abstract
This study aims to identify fashion trends with design features and provide a consumer-driven fashion design application in digital dynamics, by using text mining and semantic network analysis. We examined the current role and approach of fashion forecasting and developed a trend analysis process using consumer text data. This study focuses on analyzing blog posts regarding fashion collections. Specifically, we chose the jacket as our fashion item to produce practical results for our trend report, as it is an item used in multiple seasons and can be representative of fashion as a whole. We collected 29,436 blog posts from the past decade that included the keywords “jacket” and “fashion collection.” After the data collection, we established a list of fashion trend words for each design feature by classifying styles (e.g., retro), colors (e.g., black), fabrics (e.g., leather), and patterns (e.g., checkered). A time-series cluster analysis was used to categorize fashion trends into four clusters—increasing, decreasing, evergreen, and seasonal trends—and a semantic network analysis visualized the latest season’s dominant trends along with their corresponding design features. We concluded that these results are useful as they can reduce the time-consuming process of fashion trend analysis and offer consumer-driven fashion design guidelines.
Highlights
Consumers use smartphones and other devices to search, share, and even create fashion trends in real time (Jennings 2019)
Time‐series clustering Time-series clustering was applied to the 20 fashion seasons that occurred during the analysis period of 2009 spring/summer to 2018 fall/winter seasons to classify fashion trends into four types: increased, decreased, evergreen, and seasonal (Fig. 2)
The results indicate that consumerdriven color planning for the jacket is necessary as well as analyzing the color trends of official organizations
Summary
Consumers use smartphones and other devices to search, share, and even create fashion trends in real time (Jennings 2019). The fashion industry uses this flood of data to explore new ways of forecasting trends (Chaudhuri 2018). Several studies have used consumer-driven data approaches through text mining methodology to analyze fashion consumers’ preferences (Camiciottoli et al 2012; Kulmala et al 2013). Despite recent methodological advances in processing online text data, applying these advances to determine fashion design developments has not yet been discussed. Because of its complexity and various design features, fashion design applications can be difficult to discuss. Fashion collections would be released 6 months before the product was ready for the market. For fashion traders and designers, fashion shows constituted
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