Adaptive Multi-Feature Extraction Graph Convolutional Networks for Multimodal Target Sentiment Analysis

The multi-modal target-oriented sentiment analysis aims at predicting the sentiment polarities for target entities in a sentence by combining vision and language information. However, most existing deep learning approaches fail to extract valuable information from the visual modality and ignore the usability of syntactic dependency information embedded in the text modality. In this paper, we propose a two-stream adaptive multi-feature extraction graph convolutional networks (AME-GCN), which translates the image into a textual caption and dynamically fuses the semantic and syntactic feature from the given sentence and generated caption to model the inter/intra-modality dynamics. Extensive experiments on two multi-modal Twitter datasets show the effectiveness of the proposed model against popular textual and multi-modal approaches, demonstrating that AME-GCN is a best alternative for this task.