Abstract
Financial sentiment analysis enhances market understanding; however, standard natural language processing approaches encounter significant challenges when applied to small datasets. This study provides a comparative evaluation of embedding-based methods for financial news sentiment classification in resource-constrained environments. Word2Vec, GloVe, and sentence transformer representations are evaluated in combination with gradient boosting on manually labeled headlines. Experimental results identify a substantial gap between validation and test performance, with models performing worse than trivial baselines despite strong validation metrics. The analysis demonstrates that pretrained embeddings yield diminishing returns below a critical data sufficiency threshold, and that small validation sets contribute to overfitting during model selection. Practical application is illustrated through weekly sentiment aggregation and narrative summarization for market monitoring workflows. The findings offer empirical evidence that embedding quality alone cannot address fundamental data scarcity in sentiment classification. For practitioners operating with limited resources, the results indicate the need to consider alternative approaches such as few-shot learning, data augmentation, or lexicon-enhanced hybrid methods when labeled samples are scarce.
Abstract (translated)
财务情绪分析增强了市场理解；然而，标准的自然语言处理方法在应用于小数据集时会遇到重大挑战。这项研究对资源受限环境下基于嵌入的方法进行了比较评估，用于金融新闻的情绪分类。研究评估了Word2Vec、GloVe和句子变换器表示与梯度提升结合使用的效果，并且这些评估是基于人工标记的标题进行的。实验结果表明，在验证集和测试集中性能存在显著差距：尽管模型在验证指标上表现出色，但其实际表现不如简单的基准方法。分析还显示，预训练嵌入在数据充足性阈值以下会收益递减，并且较小的验证集会导致在模型选择过程中过度拟合。 这项研究通过每周情绪汇总和叙事总结来说明市场监控工作流程的实际应用情况。这些发现提供了实证证据，证明仅靠嵌入质量无法解决情感分类中根本的数据稀缺问题。对于资源有限的操作人员来说，结果表明，在样本标签稀少的情况下需要考虑替代方法，如少量学习、数据增强或词汇表增强的混合方法。
URL
https://arxiv.org/abs/2512.13749
