A Multi-channel Neural Network for Imbalanced Emotion Recognition

Abstract

Imbalanced issue becomes one of major bottleneck for further popularizing of emotion recognition in actual applications. Recently, some resampling methods have been proposed to improve performance by balancing the training samples. However, over-sampling methods may lead to overfitting, and undersampling methods would lose useful emotion information. In this paper, we propose a multi-channel deep architecture to improve performance in both samples and features imbalance. Specifically, we design a class correction loss function to overcome the gap between majority and minority emotions. Meanwhile, emotionspecific word embedding and a fine-tuning BERT are used to increase the differentiation of emotion words and sentences. Experimental results on two Chinese micro-blog emotion classification datasets show that our proposed architecture outperforms state-of-the-art in imbalanced emotion recognition.