融合CNN和EWC算法的不平衡文本情绪分类方法

Emotion Classification Based on CNN and EWC Algorithm for Unbalanced Texts

文本情绪分类是自然语言处理领域的一个基本任务。然而,基于不平衡数据的学习使得传统文本情绪分类方法的分类性能降低。针对这个问题,该文提出了一种融合CNN和EWC算法的不平衡文本情绪分类方法。首先,该方法使用随机欠采样方法得到多组平衡数据;其次,按顺序单独使用每一组平衡数据输入CNN训练,同时在训练过程中引入EWC算法用以克服CNN中的灾难性遗忘;最后,把使用最后一组平衡数据输入CNN训练得到的模型作为最终分类模型。实验结果表明,该方法在分类性能上明显优于基于欠采样和多分类算法的集成学习框架,且该方法比基于多通道LSTM神经网络的不平衡情绪分类方法在Accuracy和G-mean上分别提高了1.9%和2.1%。

Text emotion classification is a well-addressed task in the field of natural language processing. To deal with the unbalanced data which hurt the classification performance, this paper proposes an emotion classification method combining CNN and EWC algorithms. First, the method uses the random under-sampling method to obtain multiple sets of balanced data for training. Then it feeds each balanced dataset to CNN training in sequence, introducing EWC algorithm in the training process to overcome the catastrophic forgetting issue in CNN. Finally, the CNN model trained by the last data set is treated as the final classification model. The experimental results show that the proposed method is superior to the ensemble learning framework based on under-sampling and multi-classification algorithms, and outperforms the multi-channel LSTM neural network with 1.9% and 2.1% improvements in accuracy and G-mean, respectively.