텍스트마이닝을 적용한 친환경 패션에 대한 소비자 인식 비교

Abstract

This study investigates changes in consumer perceptions of eco-friendly fashion using big data analysis and derives strategic directions based on the frequency of related keywords. Data were collected on "eco-friendly fashion" from blogs, online communities, and web documents on portal sites (Naver, Daum, and Google) and analyzed using text mining and Ucinet6 across the pre-pandemic (2017–2019), mid-pandemic (2020–2022), and post-pandemic (2023–2025) phases. The seven most frequent keywords—“eco-friendly,” “fashion,” “brand,” “product,” “material,” “clothing,” and “production”—remained consistently within the top ten across all phases. In Phase 2, new keywords such as “mask,” “fabric,” “upcycling,” and “certification” emerged, reflecting pandemic and sustainability-related concerns, while the terms “protection,” “value,” “ethics,” “Patagonia,” and “practice” emerged in Phase 3, emphasizing ethical and practical engagement in sustainability. The keywords whose rankings changed—"sustainability" and "recycling"—appeared more frequently than before COVID-19 pandemic, and the rankings of "sustainability," "environment," and "utilization" continued to rise. This can be interpreted as a heightened awareness of the importance of using sustainable materials and protecting the environment. CONCOR cluster analysis identified four key categories in each period: (1) interest in and campaigns for eco-friendly fashion Phase 1, (2) sustainable materials and certification trends Phase 2, and (3) technology, ethical consumption, and upcycling practices Phase 3. Overall, the findings suggest that consumer awareness and engagement in eco-friendly and sustainable fashion have significantly increased, with the industry shifting toward ethical and practical sustainability practices.