텍스트마이닝을 활용한 에코패션에 나타난 소비자 인식 분석

초록

As consumer interest in environmental sustainability and responsible consumption grows, fashion companies are increasingly recognizing the importance of incorporating sustainable management practices. This study investigates consumer perceptions and issues related to eco-fashion through the analysis of Social Networking Service (SNS) big data. Text mining techniques were employed to collect data from Naver, Daum, Google, and YouTube spanning from January 2015 to January 2020. The data underwent TEXTOM collection, followed by frequency and matrix analyses. NodeXL was utilized to analyze connection structures and centrality between keywords, while NetDraw aided in visualizing the network. CONCOR analysis facilitated the identification of keyword clusters. The study amassed a dataset comprising 24,373 eco-fashion keywords, with the top 70 keywords identified based on frequency. Notably, the top 5 keywords included fashion, eco-bag, brand, ESG-ECO EXPO Korea, and bag. Centrality analysis highlighted product, eco-friendly, fashion, 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-fashion 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.

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