Leveraging Natural Language and Item Response Theory Models for ESG Scoring

TL;DR

This paper presents a novel method combining NLP and IRT models to improve ESG scoring accuracy by analyzing Portuguese news articles about Petrobras, capturing sentiment trends and temporal dynamics.

Contribution

It introduces an innovative integration of NLP and Rasch IRT models for ESG measurement, enhancing reliability and depth of ESG assessments.

Findings

Effective sentiment classification of ESG-related news

Identification of significant ESG trend periods

Improved measurement precision of ESG factors

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.