Chinese Implicit Sentiment Analysis based on Hybrid Neural Networks

• Published in: Journal of physics. Conference series Vol. 1802; no. 4; p. 42069
• Main Authors: Qi, Yanfang, Wang, Chao, Zhu, Guoan
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.03.2021
• Subjects: Artificial neural networks; Data mining; Feature extraction; Hierarchies; Knowledge representation; Machine learning; Model accuracy; Neural networks; Physics; Sentiment analysis
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1802/4/042069

Abstract Implicit sentiment analysis is an important part of sentiment computing, especially sentiment analysis based on deep learning has become a research hotspot in recent years. In this paper, convolutional neural networks are used to extract text features, combined with long-term and short-term memory network (LSTM) structure to extract context information, and add attention mechanism to the network to construct a new hybrid neural network model to achieve implicit emotion for text analysis. The hybrid neural network model extracts more meaningful sentence semantic and structural hidden features from the word-level and sentence-level hierarchies, respectively, and pays attention to the features with a large emotional contribution rate through the attention mechanism. The classification accuracy rate of the model on the public implicit sentiment dataset has reached 77%. The research of implicit sentiment analysis can improve the effect of text sentiment analysis more comprehensively, and further promote the application of text sentiment analysis in the fields of knowledge embedding, text representation learning, user modeling and natural language.