情绪分类标签掩盖情感细微差别，需用连续维度定义提升AI情感理解能力
Modelling Emotions is an Elusive Pursuit in Affective Computing
- 主张用连续维度替代传统情绪分类标签
- 连续模型可降低系统不确定性，提升应用效果
- 适合关注情感计算精准性的研究者与开发者
情感计算融合传感技术、机器学习与心理学，已研究三十余年，被用于提升AI的情感感知能力，并检测焦虑、抑郁等心理疾病症状。然而，现有系统不确定性仍高，应用受限于情绪的类别化定义。本文认为，情绪的类别标签掩盖了情感的细微差别，因此需要采用连续维度定义来推动该领域发展，提高应用实用性并降低不确定性。
原文摘要 · Abstract (English)
Affective computing - combining sensor technology, machine learning, and psychology - have been studied for over three decades and is employed in AI-powered technologies to enhance emotional awareness in AI systems, and detect symptoms of mental health disorders such as anxiety and depression. However, the uncertainty in such systems remains high, and the application areas are limited by categorical definitions of emotions and emotional concepts. This paper argues that categorical emotion labels obscure emotional nuance in affective computing, and therefore continuous dimensional definitions are needed to advance the field, increase application usefulness, and lower uncertainties.
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