ViFinClass: A Benchmark Dataset for Vietnamese Financial News Topic Classification

In this paper, we introduce ViFinClass, the first large-scale benchmark dataset for Vietnamese financial news topic classification. Collected from CafeF between 2006 and 2023, the dataset contains over 30,000 curated articles across five major financial topics: Banking and Finance, Stock Market, Real Estate, International Finance, and Macroeconomics. We evaluate multiple baselines, including traditional machine learning models and transformer-based approaches. Experimental results show that PhoBERT achieves the best performance, outperforming multilingual models such as XLM-R. These findings highlight the importance of domain-specific resources in improving financial NLP for low-resource languages. ViFinClass provides a solid foundation for future research on text classification, information extraction, and financial sentiment analysis in Vietnamese, supporting both academic studies and real-world financial applications.