# Performance Evaluation

In this section, we evaluate the validity of our proposed approach from three perspectives: general sentiment analysis, numerical understanding, and general knowledge supplement. To verify the performance of our approach, we compared it to the state-of-the-art sentiment analysis model FinBERT and the general-purpose language model ChatGPT. Our experimental results validate the validity of our method. Using only a small amount of fine-tuning data, our model consistently outperforms FinBERT and ChatGPT in sentiment analysis[50].

## Dataset

Our training data is a combination of the Twitter Financial News dataset [Magic, 2022] and the FiQA dataset, which contains a total of 10,501 samples. Table 1: Experimental results Twitter financial news sentiment validation, numerical, and contextual datasets