Representing computationally everyday emotional states is a challenging task and, arguably, one of the most fundamental for affective computing. Standard practice in emotion annotation is to ask humans to assign a value of intensity or a class value to each emotional behavior they observe. Psychological theories and evidence from multiple disciplines including neuroscience, economics and artificial intelligence, however, suggest that the task of assigning reference-based values to subjective notions is better aligned with the underlying representations. This paper draws together the theoretical reasons to favor ordinal labels for representing and annotating emotion, reviewing the literature across several disciplines. We go on to discuss good and bad practices of treating ordinal and other forms of annotation data and make the case for preference learning methods as the appropriate approach for treating ordinal labels. We finally discuss the advantages of ordinal annotation with respect to both reliability and validity through a number of case studies in affective computing, and address common objections to the use of ordinal data. Overall, the thesis that emotions are by nature ordinal is supported by both theoretical arguments and evidence, and opens new horizons for the way emotions are viewed, represented and analyzed computationally.

中文翻译：

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情绪的序数性质：一种新兴的方法

用计算表示日常情绪状态是一项具有挑战性的任务，可以说是情感计算最基本的任务之一。情感注释的标准做法是要求人类为他们观察到的每个情感行为分配一个强度值或一个类值。然而，来自包括神经科学、经济学和人工智能在内的多个学科的心理学理论和证据表明，将基于参考的值分配给主观概念的任务更符合潜在的表征。本文总结了支持使用序数标签来表示和注释情感的理论原因，并回顾了多个学科的文献。我们继续讨论处理序数和其他形式的注释数据的好坏做法，并将偏好学习方法作为处理序数标签的适当方法。我们最后通过情感计算中的一些案例研究讨论了序数注释在可靠性和有效性方面的优势，并解决了对使用序数数据的普遍反对意见。总体而言，情绪本质上是有序的这一论点得到了理论论据和证据的支持，并为以计算方式查看、表示和分析情绪的方式开辟了新的视野。我们最后通过情感计算中的一些案例研究讨论了序数注释在可靠性和有效性方面的优势，并解决了对使用序数数据的普遍反对意见。总体而言，情绪本质上是有序的这一论点得到了理论论据和证据的支持，并为以计算方式查看、表示和分析情绪的方式开辟了新的视野。我们最后通过情感计算中的一些案例研究讨论了序数注释在可靠性和有效性方面的优势，并解决了对使用序数数据的普遍反对意见。总体而言，情绪本质上是有序的这一论点得到了理论论据和证据的支持，并为以计算方式查看、表示和分析情绪的方式开辟了新的视野。