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arXiv · 2405.09770

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

Tong Zhan·Chenxi Shi·Yadong Shi·Huixiang Li·Yiyu Lin
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Abstract

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.

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Tong Zhan, Chenxi Shi, Yadong Shi, Huixiang Li, Yiyu Lin. 2024-05-16. Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3). https://arxiv.org/abs/2405.09770

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