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Nowadays, with the explosive growth of multimodal reviews on social mediaplatforms, multimodal sentiment analysis has recently gained popularity becauseof its high relevance to these social media posts. Although most previousstudies design various fusion frameworks for learning an interactiverepresentation of multiple modalities, they fail to incorporate sentimentalknowledge into inter-modality learning. This paper proposes a Multi-channelAttentive Graph Convolutional Network (MAGCN), consisting of two maincomponents: cross-modality interactive learning and sentimental feature fusion.For cross-modality interactive learning, we exploit the self-attentionmechanism combined with densely connected graph convolutional networks to learninter-modality dynamics. For sentimental feature fusion, we utilize multi-headself-attention to merge sentimental knowledge into inter-modality featurerepresentations. Extensive experiments are conducted on three widely-useddatasets. The experimental results demonstrate that the proposed model achievescompetitive performance on accuracy and F1 scores compared to severalstate-of-the-art approaches.

Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis