Ecological Discourse Modeling in a Low-Resource Setting: A Longitudinal Vietnamese Climate Corpus with Comparative Topic Modeling
Year: 2026
Type: conference-paper
Abstract: Climate change discourse has expanded substantially in recent decades, yet computational analyses remain concentrated on high-resource languages.In this paper, we construct a longitudinal Vietnamese climate news corpus and examine thematic structure and temporal evolution in a low-resource setting.The corpus comprises 10,401 articles published between 2004 and 2026 and is systematically preprocessed using linguistically informed word segmentation.To ensure domestic relevance, we apply transformer-based Named Entity Recognition and construct a geographically grounded subset of 4,501 Vietnam-focused documents.We analyze this dataset using both Latent Dirichlet Allocation and BERTopic.Results reveal stable thematic dimensions alongside longitudinal shifts from event-driven pollution reporting toward governance-and energy-centered narratives.Embedding-based modeling achieves higher semantic coherence while maintaining comparable topic diversity.The main contribution of this work is thus the compilation of a structured Vietnamese climate corpus and a systematic analysis of discourse evolution in an underrepresented language context.
Author: Huyen Phuong Nguyen
Institution: Toulouse School of Economics
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W7166556637