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

Authors

  • YU SUXIA Universiti Teknologi Mara (UiTM)
  • Rosita binti Mohd Tajuddin
  • Shaliza binti Mohd Shariff
  • Chen Yingyi

Abstract

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.

Published

2025-02-01

How to Cite

YU SUXIA, binti Mohd Tajuddin, R., binti Mohd Shariff, S., & Chen Yingyi. (2025). Big Data Analysis on Emotional Drivers and Strategies for Slow Fashion Consumption . Environment-Behaviour Proceedings Journal, 10(31). Retrieved from https://ebpj.e-iph.co.uk/index.php/EBProceedings/article/view/6537