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Leveraging Natural Language and Item Response Theory Models for ESG Scoring

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  • C'esar Pedrosa Soares

Abstract

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.

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  • C'esar Pedrosa Soares, 2024. "Leveraging Natural Language and Item Response Theory Models for ESG Scoring," Papers 2407.20377, arXiv.org.
    • Handle: RePEc:arx:papers:2407.20377
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    1. Soares, César Pedrosa & da Penha Vasconcellos, Maria, 2022. "Proposal Of A Socio-Environmental Vulnerability Scale Of Municipalities That Receive Financial Compensation – Brazil," SocArXiv jg4a8, Center for Open Science.
    2. Georgios Zairis & Panagiotis Liargovas & Nikolaos Apostolopoulos, 2024. "Sustainable Finance and ESG Importance: A Systematic Literature Review and Research Agenda," Sustainability, MDPI, vol. 16(7), pages 1-14, March.
    3. Tanja Aue & Adam Jatowt & Michael Farber, 2022. "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis," Papers 2212.11765, arXiv.org.
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