# Conclusion

In this paper, a simple and effective instruction fine-tuning method is proposed to solve the numerical understanding and financial context difficulties faced by large-scale language models (LLMs) in financial sentiment analysis. By turning small amounts of supervised learning data into instruction data and using this approach to fine-tune the generic LLM, we significantly improved its performance in financial sentiment analysis. Experimental results show that, compared to existing state-of-the-art supervised sentiment analysis models and widely used LLMS such as ChatGPT and LLaMA, our approach shows higher accuracy and F1 scores in situations where numerical and contextual understanding is critical. This verifies the validity of the instruction fine-tuning method in sentiment analysis in the financial field, especially in the application scenarios involving large amounts of financial data and complex contexts and can significantly improve the performance and practicality of the model. Despite the favorable experimental results of our study, future research could further explore how to further improve the model’s performance in complex sentiment analysis tasks with larger financial datasets and more refined instruction tuning. In addition, the combination of more advanced multimodal data and cross-domain data may help to further improve the model’s contextual understanding and emotion inference ability. In practical applications, sentiment analysis in the financial field can be applied to real-time market sentiment monitoring, investment strategy optimization, etc., to provide more accurate support for financial decision-making.