Fine-grained Sentiment Classification of Chinese Microblogs Combining Dual Weight Mechanismand Graph Convolutional Neural Network

Fine-grained Sentiment Classification of Chinese Microblogs Combining Dual Weight Mechanismand Graph Convolutional Neural Network

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Using deep learning models and attention mechanisms to classify fine-grained emotions of Chinese microblogs has become a research hotspot.However,the existing attention mechanisms consider the impact of words on words,and lack effective integration of the various dimensional characteristics of the words themselves (such as word meaning,part of speech,semantics and other characteristic information).
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.In order to solve this problem,the paper proposes a dual weight mechanism WDWM (word and dimension weight mechanism),and combines it with the GCN model based on the analytical dependency tree,so that it can not only select the words that contain key information in each microblog,but also extract the important dimensional characteristics of the word and effectively integrate multiple dimensional characteristics of words,so as to capture more rich feature information.The F measure of fine-grained sentiment classification of Chinese microblogs combining dual weight mechanism and graph convolutional neural network(WDWM-GCN) reaches 84.02%,which is 1.

7% higher than the latest algorithm proposed by WWW in 2020,which further proves that WDWM-GCN can effectively integrate the multi-dimensional characteristics of words and capture rich feature information.In the experiment on the classification of Sogou news data set,after the BERT model is addedto the WDWM mechanism,the classification effect is further improved,which fully provs that the WDWM has a significant improvement on the text classification model