Sentiment Analysis in Retail Leveraging BERT and NLP Techniques for Customer Insights

The speedy e-commerce and digital retail expansion generated online reviews and social network posts and feedback surveys among an explosion of customer-generated content. Organizations wanting to gain maximum customer satisfaction along with optimized marketing plans and expanded product lines need to analyze consumer sentiment from unstructured data. Traditional sentiment analysis systems face challenges analyzing contextual meaning as well as detecting sarcasm from unclear language and contextual confusion in both classical machine learning models and rule-based algorithms. Sentiment analysis of retail consumer feedback involves Natural Language Processing (NLP) together with the Bidirectional Encoder Representations from Transformers (BERT). BERT achieves remarkable text categorisation results when it captures sentiments together with deep contextual relationships with greater precision. The research explores ways to boost sentiment prediction capabilities through BERT optimization on datasets that focus on the retail industry. The proposed methodology includes data preprocessing along with feature extraction steps and BERT-based architecture classification methods using DistilBERT, RoBERTa and ALBERT. The performance assessment relies on comparison tests between traditional machine learning models Naïve Bayes and Support Vector Machines (SVM) and LSTMs. The paper addresses essential obstacles which include noisy data management alongside language variant control alongside computational challenges.