Enhancing Financial Sentiment Analysis with Expert-Designed Hint
This work addresses sentiment analysis challenges in the financial domain, though it appears incremental as it builds on existing methods with expert hints.
This paper tackles the problem of sentiment analysis on financial social media posts by using expert-designed hints to improve large language models' performance, finding that hints about the importance of numbers lead to significant gains, especially for tweets with monetary-related data.
This paper investigates the role of expert-designed hint in enhancing sentiment analysis on financial social media posts. We explore the capability of large language models (LLMs) to empathize with writer perspectives and analyze sentiments. Our findings reveal that expert-designed hint, i.e., pointing out the importance of numbers, significantly improve performances across various LLMs, particularly in cases requiring perspective-taking skills. Further analysis on tweets containing different types of numerical data demonstrates that the inclusion of expert-designed hint leads to notable improvements in sentiment analysis performance, especially for tweets with monetary-related numbers. Our findings contribute to the ongoing discussion on the applicability of Theory of Mind in NLP and open new avenues for improving sentiment analysis in financial domains through the strategic use of expert knowledge.