Aspect-based sentiment analysis model based on multi-dependency graph and knowledge fusion

The problems existing in aspect-based sentiment analysis include: a singular approach to syntactic dependency parsing, incomplete extraction and utilization of grammatical information; limited use of external knowledge bases, which failed to provide sufficient background knowledge and information for judging sentiment; and an excess of introduced knowledge, leading to biased conclusions. A new aspect-based sentiment analysis model was proposed, and two different syntactic parsing methods were utilized to construct two types of syntactic dependency graphs for sentences. Emotional dependency graphs were built based on external emotional knowledge, incorporating conceptual knowledge graphs to enhance aspect terms in sentences, constructing visible matrices corresponding to the sentences enhanced through conceptual knowledge graphs. A dual-channel graph convolutional neural network was employed to process the dependency graphs, the emotional dependency graphs and the visible matrices, integrating the dependency graphs with the emotional dependency graphs to perform semantic and syntactic dual interactions on specific aspect feature representations. Experimental results showed that the proposed model significantly outperformed the mainstream models in terms of accuracy and macro F1 score on multiple datasets. © 2024 Zhejiang University. All rights reserved.