  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.
