Perception Analysis on Fashion and Environment Contamination from Big Data Analysis

This study sought to investigate consumer awareness of environmental contamination caused by the fashion industry. It provides fundamental data to develop consumer consciousness for a future-oriented sustainable fashion system to preserve the global environment. TEXTOM was used to analyze the Big Data. The data was collected through online media from Naver, Daum, Google, Twitter, and YouTube. The data collection period was from January 2023 to January 2024. The mixed keywords 'fast fashion' and 'environmental pollution' were used for the search. A total of 3,015 refined texts were collected text-mining, UclNet 6.0, NetDraw, and the CONvergence of iterated CORrelations (CONCOR) algorithm were used for the analysis. The results of this study were as follows: First, many keywords related to fast fashion and environmental contamination were seen in the frequency analysis results. These included 'fashion', 'environment contamination', 'environment', and 'clothing'. Specifically, the current situation of environmental pollution caused by fast fashion was recognized by consumers. Second, the keywords 'fast fashion', 'environment', and 'contamination' were highly correlated with each other. These keywords were fundamentally related to the research topic. Third, the keywords in the network showed attributes similar to the results of the CONCOR analysis, which were classified into 2 groups: the cause and management characteristics of fashion and environment. Lastly, negative emotions, including destruction, seriousness, and excessiveness, were expressed in the results of the sentimental analysis.