Improving Sentiment Analysis Using Transformer-Based NLP Models
Sentiment analysis is an important area of Natural Language Processing (NLP) used to interpret opinions from text such as reviews and social media. Traditional methods, including rule-based and machine learning approaches, struggled with complex language features like sarcasm and context. Deep learning models like RNNs and CNNs improved performance but had limitations in capturing long-range dependencies. Transformer-based models such as BERT, RoBERTa, DistilBERT, and XLNet overcome these issues using self-attention mechanisms to better understand context. This study explores how these models enhance sentiment analysis accuracy through transfer learning, fine-tuning, and domain adaptation. Experimental results on benchmark datasets show that transformer models outperform traditional methods in accuracy, precision, recall, and F1-score. The findings highlight that transformer-based approaches provide more efficient and scalable solutions for real-world sentiment analysis applications.