Integrated Syntactic and Semantic Tree for Targeted Sentiment Classification Using Dual-Channel Graph Convolutional Network
Puning Zhang, Rongjian Zhao, Boran Yang, Yuexian Li, Zhigang Yang · IEEE/ACM Transactions on Audio Speech and Language Processing · 2024
Targeted sentiment analysis aims to identify the sentiment polarity of specific target mentions in a sentence. Existing methods employ neural networks to extract the relations between target mentions and their contexts. Recent approaches based on graph convolutional networks can model the syntactic relations extracted by an external parser into adjacency matrices. However, online reviews are informal and complex, the syntactic structures provided by the parser can be incorrect in these syntax-insensitive scenarios. To remedy this defect, we design a novel integrated syntactic and semantic tree (IS2tree) by labeling semantic relations between the target mention and contexts in a syntactic dependency tree. Furthermore, a dual-channel graph convolutional network (DCGCN) is proposed to encode the contextual information associated with the target mention by dynamic semantic pruning mechanisms and to also retain the syntactic relations. Experimental results demonstrate that the IS2tree has a favorable generalization capability comparing to the state-of-the-art baselines on four public datasets.