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

Authors

  • Yu Suxia College of Creative Arts, Universiti Teknologi Mara (UiTM), Shah Alam, Malaysia
  • Rosita Mohd Tajuddin College of Creative Arts, Universiti Teknologi Mara (UiTM), Shah Alam, Malaysia
  • Shaliza Mohd Shariff College of Creative Arts, Universiti Teknologi Mara (UiTM), Shah Alam, Malaysia
  • Meng Tao College of Art and Design, Shijiazhuang Information Engineering Vocational College, Shijiazhuang, China

DOI:

https://doi.org/10.21834/e-bpj.v10i31.6537

Keywords:

Slow fashion, Consumer sentiment, Text mining

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.

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Published

2025-01-30

How to Cite

Yu Suxia, Mohd Tajuddin, R., Mohd Shariff, S., & Meng Tao. (2025). Big Data Analysis on Emotional Drivers and Strategies for Slow Fashion Consumption . Environment-Behaviour Proceedings Journal, 10(31), 73–79. https://doi.org/10.21834/e-bpj.v10i31.6537

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