[Submitted on 29 Sep 2026]
Title:A Large-Scale Dataset for Multi-Class Financial News Classification in Brazilian Portuguese
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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.
Comments: Paper accepted for presentation at SMC 2026
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2610.20630 [cs.CL]
(or arXiv:2610.20630v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.20630
arXiv-issued DOI via DataCite
Submission history
From: Dalton Valadares [view email] [v1] Tue, 29 Sep 2026 19:06:20 UTC (228 KB)
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