利用ChatGPT变革金融领域情感分析
Transforming Sentiment Analysis in the Financial Domain with ChatGPT
AI总结：
本研究聚焦外汇市场，采用零样本提示方法测试ChatGPT 3.5的金融情感分析性能，发现其较FinBERT分类性能提升35%、与市场收益相关性高36%，凸显提示工程价值并公开数据集推动领域研究。
摘要：
金融情感分析在解读市场趋势、指导战略性交易决策方面发挥着关键作用。尽管已有先进的深度学习技术和语言模型被用于优化金融领域的情感分析，本研究仍开辟了新方向：探究大语言模型，尤其是ChatGPT 3.5，在金融情感分析中的潜力，研究重点聚焦于外汇市场（forex）。\n我们采用零样本提示（zero-shot prompting）方法，在一个精心整理的外汇相关新闻标题数据集上测试了多种ChatGPT提示词，使用精确率（precision）、召回率（recall）、F1值（f1-score）以及情感类别的平均绝对误差（Mean Absolute Error, MAE）等指标衡量性能。此外，我们还探究了预测情感与市场收益之间的相关性，作为额外的评估方式。\n与金融文本领域成熟的情感分析模型FinBERT相比，ChatGPT的情感分类性能提升了约35%，与市场收益的相关性高出36%。本研究强调了提示工程（prompt engineering）的重要性，尤其是在零样本场景下的作用，凸显了ChatGPT大幅提升金融应用中情感分析效果的潜力。我们还公开了所用数据集，旨在推动金融服务领域的进一步研究与发展。
英文摘要：
Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
