Improving Clothing Product Quality and Reducing Waste Based on Consumer Review Using RoBERTa and BERTopic Language Model

The disposability of clothing has emerged as a critical concern, precipitating waste accumulation due to product quality degradation. Such consequences exert significant pressure on resources and challenge sustainability efforts. In response, this research focuses on empowering clothing companies to elevate product excellence by harnessing consumer feedback. Beyond insights, this research extends to sustainability by providing suggestions on refining product quality by improving material handling, gradually mitigating waste production, and cultivating longevity, therefore decreasing discarded clothes. Managing a vast influx of diverse reviews necessitates sophisticated natural language processing (NLP) techniques. Our study introduces a Robustly optimized BERT Pretraining Approach (RoBERTa) model calibrated for multilabel classification and BERTopic for topic modeling. The model adeptly distills vital themes from consumer reviews, exhibiting astounding accuracy in projecting concerns across various dimensions of clothing quality. NLP's potential lies in endowing companies with insights into consumer review, augmented by the BERTopic to facilitate immersive exploration of harvested review topics. This research presents a thorough case for integrating machine learning to foster sustainability and waste reduction. The contribution of this research is notable for its integration of RoBERTa and BERTopic in multilabel classification tasks and topic modeling in the fashion industry. The results indicate that the RoBERTa model exhibits remarkable performance, as demonstrated by its macro-averaged F1 score of 0.87 and micro-averaged F1 score of 0.87. Likewise, BERTopic achieves a coherence score of 0.67, meaning the model can form an insightful topic.