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 aspec...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.展开更多
The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions,emotions,evaluations and attitudes that people hold about the entities and their attributes from the text.The word...The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions,emotions,evaluations and attitudes that people hold about the entities and their attributes from the text.The word level affective cognition becomes an important topic in sentiment analysis.Extracting the(attribute,opinion word)binary relationship by word segmentation and dependency parsing,and labeling those by existing emotional dictionary combined with webpage information and manual annotation,this paper constitutes a binary relationship knowledge base.By using knowledge embedding method,embedding each element in(attribute,opinion,opinion word)as a word vector into the Knowledge Graph by TransG,and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector.Compared with traditional method,this engine has the advantages of high processing speed and low occupancy,which makes up the time-costing and high calculating complexity in the former methods.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62276073,61966004)Guangxi Natural Science Foundation(No.2019GXNSFDA245018)+2 种基金Innovation Project of Guangxi Graduate Education(No.YCSW2022155)Guangxi“Bagui Scholar”Teams for Innovation and Research ProjectGuangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.
文摘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.
基金This research is supported by the Key Program of National Natural Science Foundation of China(Grant Nos.U1536201 and U1405254)the National Natural Science Foundation of China(Grant No.61472092).
文摘The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions,emotions,evaluations and attitudes that people hold about the entities and their attributes from the text.The word level affective cognition becomes an important topic in sentiment analysis.Extracting the(attribute,opinion word)binary relationship by word segmentation and dependency parsing,and labeling those by existing emotional dictionary combined with webpage information and manual annotation,this paper constitutes a binary relationship knowledge base.By using knowledge embedding method,embedding each element in(attribute,opinion,opinion word)as a word vector into the Knowledge Graph by TransG,and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector.Compared with traditional method,this engine has the advantages of high processing speed and low occupancy,which makes up the time-costing and high calculating complexity in the former methods.
