With the rapid development of natural language processing (NLP) technology, large-scale pre-trained language models such as GPT-3 have become a popular research object in NLP field. This paper aims to explore sentiment analysis optimization techniques based on large pre-trained language models such as GPT-3 to improve model performance and effect and further promote the development of natural language processing (NLP). By introducing the importance of sentiment analysis and the limitations of traditional methods, GPT-3 and Fine-tuning techniques are introduced in this paper, and their applications in sentiment analysis are explained in detail. The experimental results show that the Fine-tuning technique can optimize GPT-3 model and obtain good performance in sentiment analysis task. This study provides an important reference for future sentiment analysis using large-scale language models.

翻译：随着自然语言处理（NLP）技术的快速发展，以GPT-3为代表的大规模预训练语言模型已成为NLP领域的热点研究对象。本文旨在探索基于GPT-3等大规模预训练语言模型的情感分析优化技术，以提升模型性能与效果，进一步推动自然语言处理（NLP）领域的发展。通过阐述情感分析的重要性及传统方法的局限性，本文引入了GPT-3与Fine-tuning技术，并详细解析了其在情感分析中的应用。实验结果表明，Fine-tuning技术能够有效优化GPT-3模型，在情感分析任务中取得良好性能。本研究为未来利用大规模语言模型开展情感分析提供了重要参考。