A Comparative Review of VADER and BERT for Sentiment Analysis: Methods, Performance, and Applications

– Sentiment analysis is a key task in Natural Language Processing (NLP), enabling machines to understand human emotions in textual data. Among various approaches, lexicon-based methods such as VADER and deep learning-based models like BERT have gained significant attention. This paper presents a comprehensive review of VADER (Valence Aware Dictionary and Sentiment Reasoner) and BERT (Bidirectional Encoder Representations from Transformers), focusing on their architectures, methodologies, advantages, limitations, and comparative performance. Experimental findings from recent studies are also discussed to highlight differences in accuracy, interpretability, and computational efficiency.