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

TL;DR

This paper investigates the use of BERT, a deep learning model, for sentiment analysis, demonstrating its effectiveness and potential for future applications through experimental validation.

Contribution

It provides a detailed analysis of BERT's architecture, application effects, and optimization strategies specifically for sentiment analysis tasks.

Findings

BERT models show robust performance in sentiment analysis.

Fine-tuning significantly improves BERT's accuracy.

Potential for practical applications and future research directions.

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.