Big Data Analysis on Emotional Drivers and Strategies for Slow Fashion Consumption

• الحاوية / القاعدة: ENVIRONMENT-BEHAVIOUR PROCEEDINGS JOURNAL
• المؤلفون الرئيسيون: Suxia, Yu; Tajuddin, Rosita Mohd; Shariff, Shaliza Mohd; Tao, Meng
• التنسيق: Proceedings Paper
• اللغة: English
• منشور في: E-IPH LTD UK 2025
• الموضوعات: Environmental Sciences & Ecology
• الوصول للمادة أونلاين: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001428648800001

This study explored the emotional drivers of slow fashion consumption through big data analysis. Python was used to capture more than 10,000 slow fashion clothing review data from e-commerce platforms, and advanced data analysis (LDA, TF-IDF, semantic network) was used to reveal the emotional drivers of slow fashion consumers systematically. The research results show that consumers' purchase decisions are no longer limited to traditional quality and comfort but multi-dimensional emotional needs. Highlight the connection between the emotional needs of slow fashion consumers and better serve consumers to demonstrate the community's well-being and quality of life.