外部观察者标签难预测内在情绪，个人意义内容是关键突破口
What You Feel Is Not What They See: On Predicting Self-Reported Emotion from Third-Party Observer Labels
- 用第三方标注训练模型，测试其对自我报告情绪的预测能力
- 情绪效价可中度预测（CCC≈0.3），唤醒度几乎无法预测（CCC≈0）
- 当内容对说话者有个人意义时，效价预测性能显著提升（CCC≈0.6-0.8）
自我报告情绪反映内在体验，第三方观察者标签体现外部感知，二者常不一致，限制了第三方训练模型在自我报告场景的应用。这一差距在心理健康领域尤为关键，因准确建模自我报告对干预指导至关重要。本文首次开展跨语料库评估，检验第三方训练模型对自我报告的预测表现。结果显示，唤醒度预测几乎无效（CCC≈0），效价可中度预测（CCC≈0.3）。关键发现是：当内容对说话者具有个人意义时，模型对效价的预测性能显著提升（CCC≈0.6-0.8）。研究揭示个人意义是连接外部感知与内部体验的关键路径，也凸显了唤醒度建模的严峻挑战。
原文摘要 · Abstract (English)
Self-reported emotion labels capture internal experience, while third-party labels reflect external perception. These perspectives often diverge, limiting the applicability of third-party-trained models to self-report contexts. This gap is critical in mental health, where accurate self-report modeling is essential for guiding intervention. We present the first cross-corpus evaluation of third-party-trained models on self-reports. We find activation unpredictable (CCC approximately 0) and valence moderately predictable (CCC approximately 0.3). Crucially, when content is personally significant to the speaker, models achieve high performance for valence (CCC approximately 0.6-0.8). Our findings point to personal significance as a key pathway for aligning external perception with internal experience and underscore the challenge of self-report activation modeling.
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