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Life sciences · Preprint
arXiv · August 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
RA-FinBERT, a lightweight parameter-efficient framework combining LoRA-adapted FinBERT with rule-derived VADER sentiment features and metadata, achieved higher accuracy (69.89%) and macro F1 (0.634) than text-only FinBERT (63.44% accuracy, 0.526 F1) on a single financial news sentiment classification task. This is an unreviewed computational study demonstrating modest empirical gains on a surrogate endpoint without evidence of generalization, reproducibility, or downstream financial utility.
Single-dataset algorithm development and benchmarking study. Financial news titles and descriptions from an unspecified financial news dataset used for sentiment classification.. Intervention: RA-FinBERT: LoRA-adapted FinBERT with VADER-derived sentiment features and source metadata, introducing 1,024 additional trainable weights.. Compared with: Text-only FinBERT and lightweight DistilBERT baseline.
RA-FinBERT achieved 69.89% accuracy on held-out test set, compared to 63.44% for text-only FinBERT Macro F1 score of 0.634 for RA-FinBERT vs. 0.526 for text-only FinBERT Neutral-class recall increased from 18.18% to 45.45% with rule-aware features
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Single-dataset computational study showing improved accuracy on a sentiment classification task, but lacks peer review, clinical validation, comparative rigor, and generalization evidence across datasets or domains.
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Financial sentiment analysis converts unstructured financial news into quantitative signals that can support market analysis and decision-making. Existing work on resource-efficient financial NLP has largely focused on compressing or adapting pretrained language models, with less attention to combining contextual representations with lightweight rule-derived features. This study develops Rule-Aware FinBERT (RA-FinBERT), a parameter-efficient framework that integrates low-rank adaptation (LoRA) with three continuous VADER-derived sentiment proportions (positive, negative, and neutral) and a source-level metadata feature. The standardized four-dimensional feature vector is directly concatenated with the 768-dimensional final-layer FinBERT [CLS] representation and passed through a lightweight classification head. This design introduces only 1,024 additional trainable weights relative to a structurally matched text-only FinBERT model. RA-FinBERT was evaluated against text-only FinBERT and a lightweight DistilBERT baseline for three-class sentiment classification of financial-news titles and descriptions. On the held-out test set, RA-FinBERT achieved 69.89% accuracy and a macro F1 score of 0.634, compared with 63.44% and 0.526 for text-only FinBERT. Neutral-class recall increased from 18.18% to 45.45%. The framework supports both CPU and GPU execution, offering a lightweight and practical approach to financial sentiment classification under constrained computational resources. These findings indicate that rule-derived sentiment information and source metadata can provide complementary signals to contextual FinBERT representations and improve performance with minimal additional model complexity.
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