Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909135400861696 |
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| author | Wu, Yichao Jin, Zhengyu Shi, Chenxi Liang, Penghao Zhan, Tong |
| author_facet | Wu, Yichao Jin, Zhengyu Shi, Chenxi Liang, Penghao Zhan, Tong |
| contents | This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of sentiment analysis and how deep learning methods are utilized in this domain. Subsequently, it delves into the architecture and characteristics of BERT models. Through detailed explanation, it elucidates the application effects and optimization strategies of BERT models in sentiment analysis, supported by experimental validation. The experimental findings indicate that BERT models exhibit robust performance in sentiment analysis tasks, with notable enhancements post fine-tuning. Lastly, the paper concludes by summarizing the potential applications of BERT models in sentiment analysis and suggests directions for future research and practical implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08217 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis Wu, Yichao Jin, Zhengyu Shi, Chenxi Liang, Penghao Zhan, Tong Computation and Language Machine Learning This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of sentiment analysis and how deep learning methods are utilized in this domain. Subsequently, it delves into the architecture and characteristics of BERT models. Through detailed explanation, it elucidates the application effects and optimization strategies of BERT models in sentiment analysis, supported by experimental validation. The experimental findings indicate that BERT models exhibit robust performance in sentiment analysis tasks, with notable enhancements post fine-tuning. Lastly, the paper concludes by summarizing the potential applications of BERT models in sentiment analysis and suggests directions for future research and practical implementations. |
| title | Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2403.08217 |