A knowledge-augmented heterogeneous graph convolutional network for aspect-level multimodal sentiment analysis

Aspect-level multimodal sentiment analysis has also become a new challenge in the field of sentiment analysis. Although there has been significant progress in the task based on image- text data, existing works do not fully deal with the implicit sentiment expression in data. In addition, they do not fully exploit the important information from external knowledge and image tags. To address these problems, we propose a knowledge-augmented heterogeneous graph convolutional network (KAHGCN). First, we propose a dynamic knowledge selection algorithm to select the most relevant external knowledge, thereby enhancing KAHGCN's ability of understanding the implicit sentiment expression in review texts. Second, we propose a graph construction strategy to construct a heterogeneous graph that aggregates review text, image tags and external knowledge. Third, we propose a multilayer heterogeneous graph convolutional network to strengthen the interaction between information from external knowledge, review texts and image tags. Experimental results on two datasets demonstrate the effectiveness of the KAHGCN.