Emotion Detection and Sentiment Analysis of Women’s E-Commerce Clothing Reviews Using DistilBERT Transformer

Abstract

Customer reviews on e-commerce platforms have become an essential source of information for understanding user perceptions, satisfaction levels, and product quality. However, most existing studies still focus on sentiment polarity classifying opinions only as positive or negative without examining deeper emotional expressions that may reflect customer experiences more comprehensively. To address this gap, this study applies a Natural Language Processing (NLP) approach using the pre-trained DistilBERT transformer model to detect emotional patterns in women’s fashion product reviews. The dataset, obtained from Kaggle’s Women’s E-Commerce Clothing Reviews, contains approximately 23,000 entries and includes review texts along with demographic and product-related attributes. The research workflow consists of data cleaning, feature engineering, exploratory text analysis, and emotion detection using the DistilBERT-based emotion classifier. All analyses were performed using Python in the Google Colab environment. The results reveal that positive emotions, particularly joy and admiration, dominate customer feedback, indicating strong satisfaction with product fit and quality. Conversely, negative emotions such as anger and sadness appear more frequently in reviews mentioning sizing inconsistencies, fabric issues, or unmet expectations. The combination of sentiment context, emotional tone, and engineered features provides a more nuanced understanding of customer behavior compared to sentiment polarity alone. These findings highlight the potential of emotion-aware analytical approaches to support e-commerce businesses in improving product development, enhancing customer experience, and designing data-driven marketing strategies.