An emotional classification method of Chinese short comment text based on ELECTRA. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Zhang, Shunxiang; Yu, Hongbin; Zhu, Guangli
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 254
• Page End: 273
• Publication Date: 2022-12-31
• Subjects: ELECTRA pre-trained model -- text sentiment classification -- attention mechanism -- BiLSTM; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2021.1985968
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Chinese short comment texts have the characteristics of feature sparseness, interlacing, irregularity, etc., which makes it difficult to fully grasp the overall emotional tendency of users. In response to such problem, the text proposes a new method based on ELECTRA and hybrid neural network. This method can more accurately capture the emotional features of the text, improve the classification effect, enhance the evaluation feedback mechanism, and facilitate user decision-making. First, in the embedding layer, ELECTRA model is used to replace BERT model, which can avoid the inconsistency of the mask training and fine-tuning process of the traditional pre-training model. Then, in the training layer, the self-attention mechanism and the BiLSTM are selected to obtain the fine-grained semantic representation information of the review text more comprehensively. Finally, in the output layer, the softmax classifier classifies the input corpus according to the sentiment characteristics of the Chinese short text. The experimental results show that the proposed model has an efficiently improvement in accuracy and there are some discoveries about the training effect of the pre-training model on text sentiment analysis tasks.