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- Title
Text Emotion Recognition Based on XLNet-BiGRU-Att.
- Authors
Han, Tian; Zhang, Zhu; Ren, Mingyuan; Dong, Changchun; Jiang, Xiaolin; Zhuang, Quansheng
- Abstract
Text emotion recognition (TER) is an important natural language processing (NLP) task which is widely used in human–computer interaction, public opinion analysis, mental health analysis, and social network analysis. In this paper, a deep learning model based on XLNet with bidirectional recurrent unit and attention mechanism (XLNet-BiGRU-Att) is proposed in order to improve TER performance. XLNet is used to build bidirectional language models which can learn contextual information simultaneously, while the bidirectional gated recurrent unit (BiGRU) helps to extract more effective features which can pay attention to current and previous states using hidden layers and the attention mechanism (Att) provides different weights to enhance the 'attention' paid to important information, thereby improving the quality of word vectors and the accuracy of sentiment analysis model judgments. The proposed model composed of XLNet, BiGRU, and Att improves performance on the whole TER task. Experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database and the Chinese Academy of Sciences Institute of Automation (CASIA) dataset were carried out to compare XLNet-BiGRU-Att, XLNet, BERT, and BERT-BiLSTM, and the results show that the model proposed in this paper has superior performance compared to the others.
- Subjects
CHINESE Academy of Sciences (Beijing, China); TEXT recognition; EMOTION recognition; LANGUAGE models; NATURAL language processing; MOTION capture (Human mechanics); MOTION capture (Cinematography); SENTIMENT analysis
- Publication
Electronics (2079-9292), 2023, Vol 12, Issue 12, p2704
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics12122704