Improving Sentiment Analysis Using Transformer-Based NLP Models
TL;DR
Experimental results on benchmark datasets show that transformer models outperform traditional methods in accuracy, precision, recall, and F1-score, highlighting that transformer-based approaches provide more efficient and scalable solutions for real-world sentiment analysis applications.
Abstract
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.