  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.
