Attention-Enhanced Multimodal Sentiment Analysis Using Resnet50-Cbam, BERT, And Graph Neural Networks

Abstract

The present study proposes such a multimodal sentiment analysis framework with attention-enhanced properties, a combination of ResNet50 and Convolutional Block Attention Module (CBAM), a textual encoder with BERT, and refinement of relational features via Graph Neural Networks (GNN). The model is designed to address the vulnerability of uni-modal sentiment analysis and integrate related visual and textual evidence. CBAM enhances the visual feature representation with the assistance of channel and spatial attention, but BERT proposes text embeddings in their context. Another model similar to multimodal representations and similarity-based sampling relationships is a Graph Neural Network. It is experimentally demonstrated that the proposed framework is characterized by a total classification accuracy of 0.7676 compared to baseline and conventional attention-based models. The additional outcomes of Precision show that enhanced retrieval performance was obtained, which highlights the fact that multimodal fusion that is strengthened by mental attention can be effective in the sentiment classification task.