Consumer Emotional Needs and Strategic Marketing for Slow Fashion: A Big Data approach
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Abstract
The research uses Python to analyze over 10,000 consumer reviews of slow fashion apparel from Taobao and Tmall. Advanced analytics (TF-IDF, word cloud, semantic network, LDA topic modelling) systematically uncover emotional drivers behind slow fashion consumption. Findings indicate a shift toward personalized, value-driven experiences beyond traditional quality and comfort. Key emotional drivers include aesthetic experience, brand identity resonance, and comfort. Negative sentiments arise from unmet expectations and poor customer service. The study provides strategic insights for brands and policymakers to enhance production, marketing, and service strategies, promoting sustainable consumption and aligning with consumers' emotional and ethical demands.
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Copyright (c) 2025 Yu Suxia, Rosita Mohd Tajuddin, Shaliza Mohd Shariff, Hu Jialin
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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