Sentiment Classification for Chinese Text Based on Interactive Multitask Learning

In this paper, an interactive multitask learning method for Chinese text sentiment classification is proposed. Here, the classic BiLSTM + attention + CRF model is used to obtain full use of the interaction relationship between tasks, and it simultaneously solves the two tasks of emotional dictionary expansion and sentiment classification. The proposed method divides text sentiment classification and emotional dictionary expansion into primary task and subtask, and it adopts the Enhanced Language Representation with Informative Entities (ERNIE) model as the text representation learning model for the primary task. Then, through the maximum pooling layer and the fully connected layer, the text sentiment classification task is completed. Meanwhile, the classical BiLSTM + attention + CRF model is used to extract emotional words from the text in the subtask. In addition, the multitask information interaction mechanism is used, and the prediction information on the autonomous subtask is fed back into the potential representation of the two tasks. After iterative training, the performance of the two tasks is further optimized. Micro-blogs with COVID-19 are used here as the subject to form the experimental data set. The results demonstrate the superiority of the proposed method over other approaches, and they further verify the superiority of ERNIE over BERT, RoBERTa and XLNet for the sentiment classification of Chinese text.