基于维度-标签信息的多元情绪回归方法

Multi-dimensional Emotion Regression via dimension-Label Information

近年来,情绪分析方法的研究得到了飞跃式的进展,但作为情绪分析研究任务之一的情绪回归任务因语料的匮乏,目前还没有取得突破性的成果。相比情绪分类的研究,情绪回归方法受分类体系的影响较小,更具有泛化性。该文提出了一种基于维度-标签信息的多元情绪回归方法,可以同时预测输入文本在极性、强度和可控性三个维度的分值。该方法利用情绪维度和情绪类别的互信息,具体的方法是尽可能最大化两个不同情绪标签的文本在表示空间中的距离,从而输出与真实值更接近的预测分数。在英文数据集EMOBANK上的实验结果表明,该方法在均方误差和皮尔森相关系数两个指标上取得了显著提升,尤其是在极性和强度这两个维度上有较好的性能表现。

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.