A Text Mining and Topic Modeling Approach to Analyzing Research Trends in Timor-Leste

Edio da Costa

Edio da Costa Department of Computer Science, Dili Institute of Technology, Dili, Timor-Leste

Keywords: Text Mining; Topic Modeling; Research Trends; LDA; Timor-Leste

Abstract

Identifying national research trends is crucial for supporting academic development and evidence-based policymaking. In Timor-Leste, however, systematic and data-driven analyses of research outputs remain limited. This study applies text mining and topic modeling techniques to examine dominant research themes and emerging trends within Timor-Leste’s academic landscape. A corpus of academic publications and institutional research documents was collected and preprocessed using standard natural language processing methods, including case folding, tokenization, stop-word removal, and lemmatization. Topic modeling in this study was conducted using the Latent Dirichlet Allocation (LDA) algorithm, with five core topics specified to identify emerging thematic clusters during the 2021–2025 period. The result findings reveal several major research clusters, such as Education & Human Capital Development, Public Health and Social Wellbeing, Governance & Public Policy, Agriculture & Rural Development, and Digital Technology Innovation as well as their relative prominence. In addition, the analysis highlights underrepresented research areas that offer opportunities for future investigation

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