用连续数值评估文本情绪强度，比分类更适配金融等场景。
Beyond Sentiment Classification: A Generative Framework for Emotion Intensity Evaluation in Text
- 用生成模型输出0-100的连续情绪分值，替代传统分类。
- 在金融文本上表现优于分类基线，且能泛化到情感和唤醒度。
- 适合需要量化情绪程度的金融、舆情分析等应用。
我们提出一种新型情绪建模方法，将焦点从情绪识别转向情绪强度评估，以克服离散分类在金融等实际场景中的局限。通过构建包含情绪强度评分的数据集，并微调开源生成语言模型输出0-100之间的连续数值，验证了该框架在表达性和泛化性上的优势。实验表明，该方法不仅超越分类基线，还展现出惊人的泛化能力与迁移效应，适用于情感和唤醒度等关联概念。本研究推动NLP跨学科重构，主张情绪强度评估应作为分类的替代方案，更契合金融等领域对情绪程度敏感的分析需求。
原文摘要 · Abstract (English)
We introduce a novel approach to emotion modeling that shifts the focus from identification to evaluation, addressing the limitations of discrete classification in applied domains such as finance. By constructing a dataset of emotional intensity scores and fine-tuning open-weight generative language models to output continuous values from 0-100, we demonstrate a more expressive, generalizable framework for sentiment and emotion analysis. Our findings not only outperform classification baselines but also reveal surprising generalization capabilities and transfer effects to related constructs such as sentiment and arousal. This work contributes to the interdisciplinary recontextualization of NLP by introducing emotion intensity evaluation as an alternative to classification, arguing that this shift better aligns with the needs of domains--such as finance--where the degree of emotional content is central to interpretation and decision-making.
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