Chinese text sentiment analysis based on joint network and attention model

For the traditional convolutional neural network (CNN), the information between the same layer of neurons cannot be transmitted to each other, and the feature information at the same level cannot be fully utilized, and the problem of long-distance context-related features cannot be extracted. In this paper, based on the Chinese text, this paper proposes a model of character-level joint network feature fusion for sentiment analysis. Based on the character level, BiGRU and CNN-BiGRU parallel joint network are used to extract features, and CNN's strong learning ability is used to extract deep features and reuse. The two-way threshold cyclic neural network (BiGRU) performs deep learning and enhances the model's ability to learn features. On the other hand, BiGRU is used to extract context-related features to enrich feature information. Finally, the attention mechanism is introduced unilaterally to perform feature weight distribution to reduce noise interference. Multi-group comparison experiments were carried out on the dataset. The method obtained 92.36% F1 value. The results show that the proposed model can effectively improve the accuracy of text classification.