A Large-Scale Dataset for Multi-Class Financial News Classification in Brazilian Portuguese

A new arXiv paper (2610.20630v1) presents a corpus of approximately 160,000 Brazilian Portuguese financial news articles collected from web sources to address the scarcity of domain-specific datasets for Financial NLP in the language. Benchmarking three similarity-based methods with multilingual semantic embeddings, the authors report that K-Nearest Neighbors achieved the best performance on the manually labeled subset at 47.3% accuracy and 31.9% F1-score, while the LLM-annotated subset reached roughly 50% accuracy but F1-scores near 20% due to label imbalance and noisy weak annotations. The authors conclude the results establish foundational baselines and underscore the need for high-quality manual annotations in Brazilian Portuguese Financial NLP.

arXiv:2610.20630v1 Announce Type: new Abstract: The development of Financial Natural Language Processing NLP systems for Brazilian Portuguese is hindered by the scarcity of domain-specific datasets. To address this, we present a large-scale corpus of approximately 160,000 Brazilian Portuguese financial news articles collected from web sources. To establish initial benchmarks for a weakly supervised multi-class classification approach based on semantic similarity, we annotated subsets of this corpus both manually and using a Large Language Model LLM . We evaluated three similarity-based methods -- cosine similarity, K-Nearest Neighbors KNN , and FAISS-based nearest-neighbor search -- using multilingual semantic embeddings. Our experiments reveal that the task is highly sensitive to label quality and class distribution. On the manually labeled subset, KNN achieved the best performance 47.3% accuracy, 31.9% F1-score , closely followed by FAISS. Conversely, on the LLM-annotated subset, while accuracy reached approximately 50%, F1-scores dropped to near 20%, highlighting the negative impact of label imbalance and noisy weak annotations. These findings establish foundational baselines for a challenging task, underscoring the critical need for high-quality manual annotations. Ultimately, this dataset and our preliminary results provide a reference point for future research in Brazilian Portuguese Financial NLP.