Not only what, but also when
Understanding Brazilian political comments on legislative bills over time through Stance Detection and Topic Modeling
DOI:
https://doi.org/10.59490/dgo.2025.955Keywords:
Natural Language Processing, Legal Text Analysis, Stance Detection, Topic ModelingAbstract
Legislative public spaces are important structures for participatory democracy, allowing citizens’ voices to get engaged with politic decisions. As a consequence of the popularization of information and communication technologies, internet based tools have played an important role to improve public participation in political decisions, known as e-Democracy. These tools are usually composed of a set of functionalities or small services, named microservices. The better the microservices, the higher the citizen participation. This work investigates how to extract useful knowledge from citizen participation in the microservices of the public portal of the Brazilian Chamber of Deputies. For such, it analyzes public comments incorporating Natural Language Processing and Artificial Intelligence techniques in a platform named Ulysses. The tasks developed on this paper focus on a temporal analysis of comments on bills in the portal through Stance Detection and dynamic Topic Modeling tasks. For the first task, OxêSD, a BERTimbau-based model, was trained on two different corpora, one of them translated into Portuguese, and its predictive performance was evaluated using the F1 and ROC-AUC metrics, achieving 73% for both on our proposed Political-BRSD a mixed dataset containing both translated content from a bigger multilingual dataset (adapted from x-Stance) and bill-specific content (adapted from Ulysses-SD); for the second, BERTopic, a Topic Modeling framework, was used. Visualization tools to analyze how the proposed approach addressed the task were also used to explore the knowledge extracted. They allow the user to understand over time how the comments relate to each other and how the comments relate to a given legislative bill.
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Copyright (c) 2025 Matheus Cerqueira, Nádia F.F. da Silva, Ellen Souza, Hidelberg O. Albuquerque, Márcio de S. Dias, André C.P.L.F. de Carvalho
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