用新闻数据自动分析企业可持续发展表现，提升透明度与可复现性。
Nano-ESG: Extracting Corporate Sustainability Information from News Articles
- 基于84万篇德企新闻，用NLP与大模型提取可持续性话题与情感
- 通过评估验证大模型输出准确，可替代传统评级体系
- 适合关注ESG投资、舆情分析的研究者与从业者
评估企业可持续发展影响是一项复杂且日益受关注的议题。当前投资者主要依赖权威评级机构提供的可持续性评分，但这些评分常因难以理解且不可复现而受到批评。一种独立的替代方法是利用丰富的新闻文本数据。本文提出一种新方法，针对2023年1月至2024年9月期间德国主要企业的84万余篇新闻文章构建了首个公开数据集。通过自然语言处理技术筛选相关文章，并借助大语言模型（LLMs）进行摘要生成、可持续性主题识别与情感分析。我们对模型输出进行了评估，结果表明其准确性较高。相关数据集已发布于https://github.com/Bailefan/Nano-ESG。
原文摘要 · Abstract (English)
Determining the sustainability impact of companies is a highly complex subject which has garnered more and more attention over the past few years. Today, investors largely rely on sustainability-ratings from established rating-providers in order to analyze how responsibly a company acts. However, those ratings have recently been criticized for being hard to understand and nearly impossible to reproduce. An independent way to find out about the sustainability practices of companies lies in the rich landscape of news article data. In this paper, we explore a different approach to identify key opportunities and challenges of companies in the sustainability domain. We present a novel dataset of more than 840,000 news articles which were gathered for major German companies between January 2023 and September 2024. By applying a mixture of Natural Language Processing techniques, we first identify relevant articles, before summarizing them and extracting their sustainability-related sentiment and aspect using Large Language Models (LLMs). Furthermore, we conduct an evaluation of the obtained data and determine that the LLM-produced answers are accurate. We release both datasets at https://github.com/Bailefan/Nano-ESG.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。