Abstract：In recent years, emotion analysis has experienced rapid development. As one of the tasks of emotion analysis, the emotion regression is more generalized and less affected by the classification taxonomy, lacking of sufficient corpus, though. In this paper, we propose a multi-dimensional emotion regression method via dimension-label information to predict the input text scores in three dimensions (Valence, Arousal, Dominance). This method conducts emotion regression by the probability of emotion classification prediction, with an objective to maximize the distance between two texts with different emotion labels. Experimental results on EMOBANK show that the proposed method has achieved significant improvement according to the mean square error and Pearson correlation coefficient, especially in the Valence and Arousal dimensions.
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