Financial sentiment analysis enhances market understanding. However, standard Natural Language Processing (NLP) approaches encounter significant challenges when applied to small datasets. This study presents a comparative evaluation of embedding-based techniques for financial news sentiment classification in resource-constrained environments. Word2Vec, GloVe, and sentence transformer representations are evaluated in combination with gradient boosting on a manually labeled dataset of 349 financial news headlines. Experimental results identify a substantial gap between validation and test performance. Despite strong validation metrics, models underperform relative to trivial baselines. The analysis indicates that pretrained embeddings yield diminishing returns below a critical data sufficiency threshold. Small validation sets contribute to overfitting during model selection. Practical application is illustrated through weekly sentiment aggregation and narrative summarization for market monitoring. Overall, the findings indicate that embedding quality alone cannot address fundamental data scarcity in sentiment classification. Practitioners with limited labeled data should consider alternative strategies, including few-shot learning, data augmentation, or lexicon-enhanced hybrid methods.

翻译：金融情感分析有助于增进市场理解。然而，标准自然语言处理方法在应用于小数据集时会遇到显著挑战。本研究提出在资源受限环境下，对基于嵌入技术的金融新闻情感分类方法进行比较评估。我们在一个包含349条金融新闻标题的手工标注数据集上，结合梯度提升方法评估了Word2Vec、GloVe和句子变换器表示的性能。实验结果显示验证集与测试集性能之间存在显著差距。尽管验证指标表现出色，但模型性能仍低于简单基线。分析表明，当数据量低于临界充分性阈值时，预训练嵌入的边际收益递减。小的验证集在模型选择过程中导致过拟合。通过每周情感聚合和叙事摘要用于市场监控，说明了实际应用。总体而言，研究结果表明，仅靠嵌入质量无法解决情感分类中的基本数据稀缺问题。拥有有限标注数据的从业者应考虑替代策略，包括少样本学习、数据增强或融合词典的混合方法。