Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network  

作　　者：Yufei ZENG Zhixin LI Zhenbin CHEN Huifang MA 

机构地区：[1]Guangxi Key Lab of Multi-source Information Mining and Security,Guangxi Normal University,Guilin 541004,China [2]College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China

出　　处：《Frontiers of Computer Science》2023年第6期87-99,共13页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62276073,61966004);Guangxi Natural Science Foundation(No.2019GXNSFDA245018);Innovation Project of Guangxi Graduate Education(No.YCSW2022155);Guangxi“Bagui Scholar”Teams for Innovation and Research Project;Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.

摘　　要：The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis(ABSA).However,the accuracy of the dependency parser cannot be determined,which may keep aspect words away from its related opinion words in a dependency tree.Moreover,few models incorporate external affective knowledge for ABSA.Based on this,we propose a novel architecture to tackle the above two limitations,while fills up the gap in applying heterogeneous graphs convolution network to ABSA.Specially,we employ affective knowledge as an sentiment node to augment the representation of words.Then,linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency tree.Finally,we design a multi-level semantic heterogeneous graph convolution network(Semantic-HGCN)to encode the heterogeneous graph for sentiment prediction.Extensive experiments are conducted on the datasets SemEval 2014 Task 4,SemEval 2015 task 12,SemEval 2016 task 5 and ACL 14 Twitter.The experimental results show that our method achieves the state-of-the-art performance.

关 键 词：heterogeneous graph convolution network multi-head attention network aspect-based sentiment analysis deep learning affective knowledge 

分 类 号：O15[理学—数学]