Leveraging Natural Language and Item Response Theory Models for ESG Scoring
This is an incremental improvement for sustainability and finance, offering enhanced ESG metrics through temporal analysis.
The paper tackled the problem of ESG scoring by integrating NLP with the Rasch model from IRT on news articles about Petrobras, resulting in a more precise and reliable measurement of ESG sentiment trends over time.
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.