Abstract
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.
Highlights
With the increasing popularity of social media, people are accustomed to using the Internet as a place to vent their personal feelings
The results show that Enhanced Language Representation with Informative Entities (ERNIE) won by a narrow margin in terms of performance
The Adam weight decay strategy of the migration optimization was adopted for ERNIE, BERT, XLNet and the other models used in the experiment
Summary
With the increasing popularity of social media, people are accustomed to using the Internet as a place to vent their personal feelings. An interactive multitask learning model is proposed to integrate emotional dictionary expansion with text sentiment classification. It takes full advantage of the relationship between the two tasks and iterates the prediction results of the two tasks through an information transmission mechanism to improve the performance of the model. The extracted emotional words are inserted into the emotional dictionary during the tth iteration To solve these two tasks, we introduce a multitask learning network, which combines the two tasks of emotional dictionary expansion and text sentiment classification.
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