ODSBAHIA-PTBR: A Natural Language Processing Model to Support Sustainable Development Goals

Abstract

Objective: The present study aims to propose an approach for the objective classification of texts in Portuguese in relation to the Sustainable Development Goals (SDGs) of Brazil's 2030 Agenda. Theoretical Framework: The study uses natural language processing (NLP) techniques with deep learning, using pre-trained models such as BERTimbau Base, DeBERTinha and Albertina. In addition, it considers the existing gaps in the literature regarding the classification of texts in Portuguese related to the 17 UN SDGs and also including three new SDGs proposed in the document Guide Agenda 2030: Integrating SDGs, Education and Society, prepared in 2020 in partnership between UnB and UNESP, SDGs 18 (Ethnic-Racial Equality), 19 (Art, Culture and Communication) and 20 (Rights of Indigenous Peoples and Traditional Communities). Method: La investigación es exploratoria, descriptiva y aplicada, con enfoque cuantitativo y procedimientos experimentales. Los modelos previamente entrenados se ajustaron al conjunto de datos de etiquetas múltiples creado específicamente para la tarea. La Base BERTimbau presentó el mejor rendimiento y se utilizó como base para la creación del modelo ODSBahia-PTBR, evaluado con métricas como precisión (82%), recuerdo (72%) y F1-Score (77%). Results and Discussion: El ODSBahia-PTBR logró una precisión del 95% al traducir y clasificar el conjunto de datos OSDG. Los resultados ponen de manifiesto la efectividad del modelo en la identificación y categorización de textos alineados con los ODS, siendo especialmente relevante para el seguimiento de las interseccionalidades entre los ODS propuestos. Research Implications: The SDGbahia-PTBR model has practical implications by offering an innovative tool for different stakeholders to monitor and analyze initiatives aligned with the SDGs, contributing to the evaluation and promotion of the 2030 Agenda. Originality/Value: This research is a pioneer in including SDGs 18, 19 and 20 in Portuguese-language text classifiers, offering an unprecedented and applicable approach to sustainable monitoring in Brazil and other Portuguese-speaking countries.