Image Recognition Cropping and Multimodal Aspect-Level Sentiment Analysis Using Knowledge Graph Enhancement
E. Liang, Guozhe Jin · 2025
Multimodal Aspect-Level Sentiment Analysis, as an intersection of natural language processing and computer vision, aims to combine textual and image modalities to accurately predict aspect-specific sentiment categories. Existing methods mainly achieve modal information integration through simple feature-level or decision-level fusion, which is difficult to fully capture the complex semantic associations among multimodal features. In this study, an innovative framework combining Mask R-CNN, Graph Neural Network (GNN) and Transformer is proposed for image feature extraction, emotion enhancement and multimodal feature fusion, respectively. By introducing the sentiment knowledge graph, this method effectively enhances the sentiment inference ability; the filtering module based on the attention mechanism is used to strengthen the inter-modal feature interaction. Experimental results show that the performance of this model on both Twitter2015 and Twitter2017 datasets outperforms the existing baseline methods, validating its effectiveness in multimodal sentiment analysis tasks.