디자인 및 패션 분야 창업 연구 동향 분석 - 텍스트 마이닝 기반 접근 -

This study examines trends in entrepreneurship research in the design and fashion fields in Korea over the past decade (2015–2025) using a text mining approach. Unlike earlier studies relying on case studies or qualitative reviews, this research applies quantitative methods to systematically analyze thematic structures and conceptual flows of academic discourse. A total of 92 articles (49 design, 43 fashion) were collected from domestic journals, and their titles, abstracts, and keywords analyzed. After preprocessing with lemmatization, stopword removal, and N-gram integration, techniques included frequency and term frequency-inverse document frequency (TF-IDF) analysis, N-gram analysis, latent latent Dir- ichlet allocation (LDA) topic modeling, and hierarchical clustering. The results show that design and fashion entre- preneurship research is dominated by education and policy support, while brand identity, creativity, and storytelling, central to creative industries, receive limited attention. A gap also exists between industry practices, where online plat- forms, personal brands, and direct-to-consumer models are expanding rapidly, and academic research, which remains nar- row. Technology-driven entrepreneurship such as digital fashion, AI-based design, and smart wearables, though central to industry transformation, has not been thoroughly explored. This study structures fragmented scholarship into a data- driven overview and highlights traits of creative industry entrepreneurship. Academically, it demonstrates the value of text mining for trend analysis, while practically, it provides insights for policy, curriculum design, and strategic support.