小数据下金融新闻情感分析效果差，嵌入表示作用有限。
Comparative Evaluation of Embedding Representations for Financial News Sentiment Analysis
- 用词向量和句子嵌入结合梯度提升做情感分类
- 验证集表现好但测试集远低于简单基线
- 数据不足时预训练嵌入收益递减，适合小样本研究者
金融情感分析有助于理解市场。然而，标准自然语言处理方法在小数据集上面临显著挑战。本研究针对资源受限环境下的金融新闻情感分类，对基于嵌入的技术进行了对比评估。在包含349条人工标注金融新闻标题的数据集上，评估了Word2Vec、GloVe及句子转换器表示，并与梯度提升结合使用。实验结果揭示验证集与测试集性能存在显著差距：尽管验证指标表现良好，模型在测试集上仍显著低于简单基线。分析表明，当数据量低于临界阈值时，预训练嵌入的收益急剧下降；小规模验证集导致模型选择中过拟合。通过每周情感聚合与叙事摘要展示其实际应用价值。总体发现，嵌入质量本身无法解决情感分类中的根本性数据稀缺问题。建议数据有限的实践者考虑少样本学习、数据增强或词典增强的混合方法。
原文摘要 · Abstract (English)
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.
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