Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis

• URL: http://arxiv.org/abs/2403.08217v1
• Date: Wed, 13 Mar 2024 03:31:26 GMT
• Title: Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis
• Authors: Yichao Wu, Zhengyu Jin, Chenxi Shi, Penghao Liang, Tong Zhan
• Abstract summary: This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. 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.
• Score: 8.504422968998506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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