Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification

arXiv:2404.01805v1 Announce Type: new
Abstract: 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 …

abstract arxiv classification computer cs.lg data detection emotion emotion detection emotions human human-computer interaction ordinal paper pivotal prediction systems text textual type