TRANSFORMING CUSTOMER EXPERIENCE THROUGH NLP AND SENTIMENT ANALYSIS

The integration of Sentiment Analysis (SA) and Natural Language Processing (NLP) assists companies in improving customer service by examining actionable insights from unstructured data sources like social media tweets and customer reviews. RoBERTa acts as a sophisticated transformer-based model that is capable of analyzing complex customer sentiment data. This paper assesses the performance of RoBERTa for sentiment classification while investigating solutions to class imbalance issues, sarcasm detection difficulties, and ethical issues. The discussion proposes optimization techniques for RoBERTa on different data sets to register high accuracy and outline future research directions to improve fairness and explainability. The paper concludes its material by introducing mathematical frameworks as performance metric tools in addition to optimization and evaluation procedures.