We create a large language model with high accuracy to investigate the relatedness between 12 environmental, social, and governance (ESG) topics and more than 2 million news reports. The text match pre‐trained transformer (TMPT) with 138,843,049 parameters is built to probe whether and how much a news record is connected to a specific topic of interest. The TMPT, based on the transformer structure and a pre‐trained model, is an artificial intelligence model trained by more than 200,000 academic papers. The cross‐validation result reveals that the TMPT's accuracy is 85.73%, which is excellent in zero‐shot learning tasks. In addition, combined with sentiment analysis, our research monitors news attitudes and tones toward specific ESG topics daily from September 2021 to September 2023. The results indicate that the media is increasing discussion on social topics, while the news regarding environmental issues is reduced. Moreover, toward almost all topics, the attitudes are gradually becoming positive. Our research highlights the temporal shifts in public perception regarding 12 key ESG issues:ESG has been incrementally accepted by the public. These insights are invaluable for policymakers, corporate leaders, and communities as they navigate sustainable decision‐making.

中文翻译：

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来自人工智能调查的新闻的 ESG 趋势 人工智能训练


我们创建了一个高精度的大型语言模型，以调查 12 个环境、社会和治理 （ESG） 主题与 200 多万篇新闻报道之间的相关性。具有 138,843,049 个参数的文本匹配预训练转换器 （TMPT） 用于探测新闻记录是否与感兴趣的特定主题相关以及有多少相关。TMPT 基于 transformer 结构和预训练模型，是一种由 200,000 多篇学术论文训练的人工智能模型。交叉验证结果表明，TMPT 的准确率为 85.73%，这在零样本学习任务中非常出色。此外，结合情绪分析，我们的研究从 2021 年 9 月到 2023 年 9 月每天监测新闻对特定 ESG 主题的态度和语气。结果表明，媒体对社会话题的讨论越来越多，而关于环境问题的新闻却在减少。此外，对于几乎所有主题，人们的态度都逐渐变得积极。我们的研究强调了公众对 12 个关键 ESG 问题的看法的时间变化：ESG 已逐渐被公众接受。这些见解对于政策制定者、企业领导者和社区在制定可持续决策时非常宝贵。