用评价视角标注文本情绪，发现模型可捕捉情绪判断的规律。
Annotation and modeling of emotions in a textual corpus: an evaluative approach
- 采用评价理论框架人工标注工业语料中的情绪
- 语言模型能学习标注者差异背后的语言特征
- 适合研究情绪计算与标注一致性的人参考
情绪是人类社会运作中的关键现象，但其在文本中的表现仍属开放课题。本文基于评价理论框架，对工业语料进行人工标注，发现标注存在显著分歧，但统计趋势稳定。通过训练语言模型分析这些标注，结果表明模型可建模标注过程，且标注差异由潜在语言特征驱动。反观而言，语言模型已具备根据评价标准区分情感情境的能力。
原文摘要 · Abstract (English)
Emotion is a crucial phenomenon in the functioning of human beings in society. However, it remains a widely open subject, particularly in its textual manifestations. This paper examines an industrial corpus manually annotated following an evaluative approach to emotion. This theoretical framework, which is currently underutilized, offers a different perspective that complements traditional approaches. Noting that the annotations we collected exhibit significant disagreement, we hypothesized that they nonetheless follow stable statistical trends. Using language models trained on these annotations, we demonstrate that it is possible to model the labeling process and that variability is driven by underlying linguistic features. Conversely, our results indicate that language models seem capable of distinguishing emotional situations based on evaluative criteria.
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