Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909205359755264 |
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| author | Zhan, Tong Shi, Chenxi Shi, Yadong Li, Huixiang Lin, Yiyu |
| author_facet | Zhan, Tong Shi, Chenxi Shi, Yadong Li, Huixiang Lin, Yiyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3) Zhan, Tong Shi, Chenxi Shi, Yadong Li, Huixiang Lin, Yiyu Computation and Language Artificial Intelligence 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. |
| title | Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3) |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.09770 |