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.

翻译：本文探讨了深度学习技术，特别是BERT模型在情感分析中的应用。首先介绍了情感分析的基本概念以及深度学习方法在该领域的应用方式。随后深入阐述了BERT模型的架构与特征。通过详细说明，结合实验验证，阐明了BERT模型在情感分析中的应用效果与优化策略。实验结果表明，BERT模型在情感分析任务中表现出色，经过微调后性能显著提升。最后，论文总结了BERT模型在情感分析中的潜在应用，并提出了未来研究与实践方向。