Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification

• URL: http://arxiv.org/abs/2404.01805v1
• Date: Tue, 2 Apr 2024 10:06:30 GMT
• Title: Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification
• Authors: Michael Mitsios, Georgios Vamvoukakis, Georgia Maniati, Nikolaos Ellinas, Georgios Dimitriou, Konstantinos Markopoulos, Panos Kakoulidis, Alexandra Vioni, Myrsini Christidou, Junkwang Oh, Gunu Jho, Inchul Hwang, Georgios Vardaxoglou, Aimilios Chalamandaris, Pirros Tsiakoulis, Spyros Raptis,
• Abstract summary: We introduce a method for categorizing emotions from text, which acknowledges and differentiates between the diversified similarities and distinctions of various emotions. Our approach not only preserves high accuracy in emotion prediction but also significantly reduces the magnitude of errors in cases of misclassification.
• Score: 37.823815777259036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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