Computer Science ›› 2022, Vol. 49 ›› Issue (3): 246-254.doi: 10.11896/jsjkx.201200073

• Artificial Intelligence • Previous Articles     Next Articles

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

LI Hao, ZHANG Lan, YANG Bing, YANG Hai-xiao, KOU Yong-qi, WANG Fei, KANG Yan   

  1. School of Software,Yunnan University,Kunming 650504,China
  • Received:2020-12-07 Revised:2021-06-08 Online:2022-03-15 Published:2022-03-15
  • About author:LI Hao,born in 1970,professor,Ph.D,His main research interests include distributed computing,grid and cloud computing research.
    KANG Yan,born in 1972,Ph.D,asso-ciate professor.Her main research inte-rests include software engineering,system optimization,big data processing and mining.
  • Supported by:
    National Natural Science Foundation of China(61762092),Open Fund Project of Key Laboratory of Software Engineering in Yunnan Province(2020SE303),Major Science and Technology Projects in Yunnan Province(202002AB080001),Material Genetic Engineering-Calculation Software Development of Integrated Calculation Function Module Based on Metcloud(2019CLJY06) and Gene Enginee-ring of Rare and Precious Metal Materialsin Yunnan Province-R & D and Demonstration Application of High-throughput Integrated Computing and Data Analysis Technology for Rare and Precious Metal Materials(2019ZE001-1,202002AB080001).

Abstract: 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).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.

Key words: Attention mechanism, Dual weight mechanism, Fine-grained emotion classification, Graph convolutional neural network

CLC Number: 

  • TP391
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