Leveraging Natural Language and Item Response Theory Models for ESG Scoring

• Main Author: Soares, César Pedrosa
• Format: Journal Article
• Language: English
• Published: 29-07-2024
• Subjects: Computer Science - Artificial Intelligence; Quantitative Finance - General Finance; Statistics - Methodology
• DOI: 10.48550/arxiv.2407.20377

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.