As consumer interest in environmental sustainabilit y and responsible consumption grows, fashion compa nies are increasingly recognizing the importance of i ncorporating sustainable management practices. This study investigates consumer perceptions and issues r elated to eco-fashion through the analysis of Social N etworking Service (SNS) big data. Text mining techniq ues were employed to collect data from Naver, Daum, Google, and YouTube spanning from January 2015 to January 2020. The data underwent TEXTOM collectio n, followed by frequency and matrix analyses. NodeX L was utilized to analyze connection structures and c entrality between keywords, while NetDraw aided in vi sualizing the network. CONCOR analysis facilitated th e identification of keyword clusters. The study amass ed a dataset comprising 24,373 eco-fashion keyword s, with the top 70 keywords identified based on frequency. Notably, the top 5 keywords included fashion, ec o-bag, brand, ESG-ECO EXPO Korea, and bag. Centra lity analysis highlighted product, eco-friendly, fashio n, material, and brand as the keywords. The eco-fashion category was further categorized into 7 groups: industry, item, campaign, purchase behavior, reason for purchase, emotional words, and eco. Clustering similar keywords revealed four distinct groups: eco-fashi on product, sustainable production, domestic eco-campaign, and global eco-campaign. This research enhances our understanding of consumer awareness regarding fashion companies' eco-friendly marketing strategies, providing valuable insights for developing customer-centric marketing approaches.