Review Radar: Context-Aware Sentiment Classification using RoBERTa on Multi-Domain Review Analysis

The growing reliance on sentiment analysis for decision making in e-commerce, social media, and customer service is challenged by the complexity of sarcasm, which often misleads conventional models. This research presents ReviewRadar, a sarcasm aware sentiment analysis framework fine-tuned using the RoBERTa transformer architecture. Our dataset integrates a stable multi-domain corpus with targeted sarcasm augmentation, encompassing diverse review categories such as technology, fashion, travel, food delivery, and customer feedback. Unlike general sentiment models, ReviewRadar emphasizes the nuanced detection of sarcastic remarks, which are specially challenging due to contextual and lingual ambiguity. Preprocessing involved noise removal, tokenization, and stable class distribution, followed by fine tuning RoBERTa with optimized hyperparameters. trial evaluation demonstrated an overall accuracy exceeding 90%, with important improvement in sarcasm classification compared to baseline models such as DistilBERT. The proposed approach offers enhanced reliability for real-world sentiment monitoring systems, enabling businesses to better interpret user opinions and respond effectively.