This paper proposes a microblog sentiment analysis model based on dual-channel feature fusion.Firstly,the dynamic word vectors obtained from the BERT pre-trained language model are used as the input of the sentiment classification model,and then a dual-channel feature extraction network is used for feature extraction,which extracts the local features of the text using TextCNN-Attention on one hand,and extracts the global thematic features of the text using the neural thematic model based on graph convolutional neural network on the other hand.Then the local and global features are fused to get the final text vector,and finally the sentiment polarity is output by Softmax.Experiments are conducted on the constructed microblog comment text dataset,and the F1 value of the model reaches 91.36％,having an improvement of 0.73％～8.82％compared with the mainstream baseline models,which verifies the effectiveness of the model on the task of sentiment analysis.
关键词
情感分析/预训练语言模型/图卷积神经网络/神经主题模型/特征融合
Key words
sentiment analysis/pre-trained language model/graph convolutional neural network/neural thematic model/feature fusion
维普
万方数据