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.

中文翻译：

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使用文本挖掘和语义网络分析处理服装设计趋势应用程序

这项研究旨在通过使用文本挖掘和语义网络分析来识别具有设计特征的时尚趋势，并在数字动力学中提供一种由消费者驱动的时装设计应用程序。我们检查了时尚预测的当前作用和方法，并使用消费者文本数据开发了趋势分析过程。这项研究着重于分析有关时装系列的博客文章。具体来说，我们选择夹克作为我们的时尚产品，以便为我们的趋势报告提供实用的结果，因为它是多个季节使用的产品，并且可以代表整个时尚。在过去十年中，我们收集了29,436篇博客文章，其中包括关键字“夹克”和“时装系列”。收集数据后，我们通过对样式（例如复古），颜色（例如，黑色），织物（例如皮革）和图案（例如格子）。进行了时间序列聚类分析，将时尚趋势分为四个类-增长，减少，常绿和季节性趋势。语义网络分析显示了最新季节的主导趋势及其相应的设计特征。我们得出的结论是，这些结果很有用，因为它们可以减少耗时的时尚趋势分析过程，并提供以消费者为导向的服装设计准则。