Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)

Understand

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.