  This paper explores the application of deep learning techniques, particularly
focusing on BERT models, in sentiment analysis. It begins by introducing the
fundamental concept of sentiment analysis and how deep learning methods are
utilized in this domain. Subsequently, it delves into the architecture and
characteristics of BERT models. Through detailed explanation, it elucidates the
application effects and optimization strategies of BERT models in sentiment
analysis, supported by experimental validation. The experimental findings
indicate that BERT models exhibit robust performance in sentiment analysis
tasks, with notable enhancements post fine-tuning. Lastly, the paper concludes
by summarizing the potential applications of BERT models in sentiment analysis
and suggests directions for future research and practical implementations.
