This paper explores an innovative approach to Environmental, Social, and Governance (ESG) scoring by integrating Natural Language Processing (NLP) techniques with Item Response Theory (IRT), specifically the Rasch model. The study utilizes a comprehensive dataset of news articles in Portuguese related to Petrobras, a major oil company in Brazil, collected from 2022 and 2023. The data is filtered and classified for ESG-related sentiments using advanced NLP methods. The Rasch model is then applied to evaluate the psychometric properties of these ESG measures, providing a nuanced assessment of ESG sentiment trends over time. The results demonstrate the efficacy of this methodology in offering a more precise and reliable measurement of ESG factors, highlighting significant periods and trends. This approach may enhance the robustness of ESG metrics and contribute to the broader field of sustainability and finance by offering a deeper understanding of the temporal dynamics in ESG reporting.

翻译：本文探索了一种创新的环境、社会和治理（ESG）评分方法，该方法通过将自然语言处理（NLP）技术与项目反应理论（IRT）——特别是拉希模型——相结合来实现。研究使用了一个全面的葡萄牙语新闻文章数据集，这些文章涉及巴西主要石油公司Petrobras，收集时间为2022年至2023年。数据经过筛选，并利用先进的NLP方法根据ESG相关情感进行分类。随后应用拉希模型评估这些ESG测量的心理测量特性，从而对ESG情感随时间变化的趋势进行细致评估。结果表明，该方法在提供更精确、更可靠的ESG因素测量方面具有显著效果，并突出了关键时期和趋势。该方法有望增强ESG指标的稳健性，并通过深化对ESG报告时间动态的理解，为可持续发展和金融的更广泛领域做出贡献。