Advanced NLP Framework for Analysing Elasticity Feedback in Apparel Design

Abstract

Elasticity is critical in modern apparel design, directly affecting product performance and customer satisfaction. This study presents a scalable framework leveraging advanced Natural Language Processing (NLP) to systematically analyze elasticity-related customer feedback. Utilizing 93,860 customer reviews of leggings, pants, and crops, the framework combines manual annotation, keyword extraction, and Named Entity Recognition (NER) to identify and refine elasticity-specific reviews. A GPT-4o–based categorization model and hierarchical clustering of themes further segment reviews into aspects: attributes (e.g., flexibility, durability), components (e.g., stretch fabric, elastic waistbands), usage situations (e.g., washing, wearing), and common issues (e.g., pilling, sagging). Aspect-Based Sentiment Analysis (ABSA) and VADER scoring capture explicit and implicit sentiments, while advanced visualizations (heatmaps, scatter plots) reveal correlations among product features, consumer sentiment, and usage contexts. Comparative evaluations demonstrate the proposed framework’s superiority over lexicon-based and other machine learning methods. The results, integrated with product metadata, provide actionable insights for manufacturers, highlighting the importance of activity-specific designs and innovative fabric development. This work establishes a foundation for future integration of multimodal data, further enhancing customer feedback analytics in the apparel industry.