Numerical sensitivity analysis shows that numerical data play a key role in financial sentiment analysis, as they often reflect important financial indicators (see Table 2). For example, in Example 1, FinBERT failed to correctly identify an emotion that dropped from 2.2 million to 300,000, while ChatGPT 4.0 and Instruct-FinGPT correctly identified this significant change as a positive emotion. For the assessment of context understanding ability (see Table 4), our model performed well in identifying emotions in different contexts, accurately distinguishing between positive, negative, and neutral emotions. These results highlight the importance of context understanding in financial sentiment analysis and the significant differences in performance between different models in this area.
Table 5: Zero-shot evaluation between ChatGPT and InstructD FinGPT on the entire dataset of financial phase bank.