用专家设计的提示词提升金融文本情感分析效果
Enhancing Financial Sentiment Analysis with Expert-Designed Hint
- 引入专家设计的提示，引导模型关注数字信息
- 在需换位思考的任务中，性能显著提升
- 特别适合需要理解金融数值的场景
本文研究了专家设计的提示在金融社交媒体文本情感分析中的作用。我们探讨了大语言模型（LLMs）共情作者视角并分析情感的能力。研究发现，专家设计的提示（如强调数字的重要性）能显著提升多种LLM的表现，尤其是在需要换位思考的任务中。对包含不同类型数值信息的推文进一步分析表明，引入专家提示后，尤其是涉及金钱相关数字的推文，情感分析性能有明显提升。该研究为自然语言处理中心智理论的应用提供了新见解，并通过策略性使用专家知识，开辟了提升金融领域情感分析的新路径。
原文摘要 · Abstract (English)
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.
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