想低成本做金融情感分析？RA-FinBERT用LoRA加规则特征，只加千把个参数就把准确率拉高6个点，中性类召回翻倍，值得看看。
RA-FinBERT是一种参数高效框架，将低秩适配（LoRA）与三个连续VADER情感比例及源级元数据特征结合。该设计仅增加1,024个可训练权重，与文本-only FinBERT相比，在金融新闻标题和描述的三分类情感任务上，准确率从63.44%提升至69.89%，宏F1从0.526提升至0.634。中性类召回率从18.18%大幅提升至45.45%。框架支持CPU和GPU执行，适用于计算资源受限场景。
RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification
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.