Interpreting Lawsuits Contexts through Probabilistic Topic Modeling - Santos, Étore Braga e; Rodello, Ildeberto Aparecido (Revista Eletrônica de Iniciação Científica em Computação, 2025)
The increasing volume and complexity of digital legal records have underscored the need for scalable analytical tools capable of extracting meaningful insights from unstructured text. This study investigates the application of topic modeling techniques Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Probabilistic Latent Semantic Analysis (pLSA) to the classification and interpretation of legal documents, with a focus on lawsuits related to special education policies in Brazil. A corpus of 4,259 judicial cases was collected from the São Paulo Court of Justice, preprocessed, and analyzed to identify latent thematic structures. Model performance was evaluated using the coherence score and further validated through human interpretability assessments. The results indicate that pLSA performs well with fewer topics, capturing broader legal themes, while LDA excels at higher topic counts, effectively distinguishing nuanced legal issues such as access to education and contractual disputes. The findings highlight the potential of probabilistic topic modeling as a decision-support tool, reinforcing the role of artificial intelligence in improving legal transparency, accessibility, and analytical depth, without compromising the autonomy of legal interpretation.