Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems. This paper introduces a method for categorizing emotions from text, which acknowledges and differentiates between the diversified similarities and distinctions of various emotions. Initially, we establish a baseline by training a transformer-based model for standard emotion classification, achieving state-of-the-art performance. We argue that not all misclassifications are of the same importance, as there are perceptual similarities among emotional classes. We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels. Finally, we propose a method that performs ordinal classification in the two-dimensional emotion space, considering both valence and arousal scales. The results show that our approach not only preserves high accuracy in emotion prediction but also significantly reduces the magnitude of errors in cases of misclassification.

翻译：文本情绪检测近年来受到越来越多的关注，因为它对于开发共情的人机交互系统至关重要。本文提出了一种从文本中分类情绪的方法，该方法识别并区分了不同情绪之间的多样相似性和差异性。首先，我们通过训练基于Transformer的模型进行标准情绪分类来建立基线，取得了最先进的性能。我们认为并非所有错误分类都具有同等重要性，因为不同情绪类别之间存在感知相似性。因此，我们将情绪标签问题重新定义，从传统分类模型转变为序数分类模型，其中离散情绪根据其效价水平按顺序排列。最后，我们提出了一种在二维情绪空间中考虑效价和唤醒度尺度进行序数分类的方法。结果表明，我们的方法不仅保持了情绪预测的高准确率，而且显著减少了错误分类情况下的误差幅度。