Abstract: In the task of multimodal aspect-based sentiment analysis,traditional methods primarily focus on deep-level cross-modal interactions between images and texts while paying less attention to the aspect-related shallow information within images and texts.This oversight leads to the introduction of aspect-irrelevant noise,thereby limiting the model’s ability to capture the complex relationships between aspects and sentiments.To address this issue,a dual-channel graph convolutional network (DCGCN) model is proposed.Based on the architecture of the BART model,the proposed approach employs an attention mechanism to enhance aspect semantics,leverages graph convolutional networks (GCN) to extract aspect-enhanced multimodal features,and aggregates syntactic dependencies,aspect-based positional dependencies,and aspect-augmented imagetext correlation information into the GCN adjacency weight matrix to obtain multi-information-aware multimodal features.Experiments demonstrate that the proposed model achieves F1 scores of 67.4% and 67.9% on two Twitter datasets,respectively,and can improve the performance of multimodal aspect-based sentiment analysis.

Key words: aspect-based sentiment analysis, multimodal, graph convolutional network, syntactic dependency, attention mechanism