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

2024/03/13 by Yichao Wu, Wu, Yichao, Zhengyu Jin +7 · 1 citation
Medicine · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Research and Treatments

paper · pdf · doi:10.48550/arxiv.2403.08217

openalex publication_date 2024/03/13 · openalex created_date 2024/03/15 · openalex updated_date 2026/07/28

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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