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.

本研究论文探讨了深度学习技术（尤其是 BERT 模型）在情感分析中的应用，介绍了情感分析的基本概念和深度学习方法的应用。通过详细的解释，阐明了 BERT 模型在情感分析中的应用效果和优化策略，并通过实验证实了这一点。实验结果表明 BERT 模型在情感分析任务中表现出强大的性能，经过微调后得到了显著的提升。最后，该论文总结了 BERT 模型在情感分析中的潜在应用和未来研究方向，以及实际实施的建议。