TL;DR
The proposed model, Crossbred Analogous Tree-Knowledge Deep (CAT-KD), is composed of five key components for comprehensive sentiment analysis and achieves an accuracy of 94.83% and an F1-score of 94.12%, outperforming baseline models with improvements of up to 0.62% in accuracy and 2.46% in F1-score on Twitter datasets.
Results highlight the value of integrating relational graph structures with complementary feature representations for misinformation detection, and provide a foundation for future extensions, including federated learning, explainability techniques, and cross-domain applications in multilingual contexts.
Results demonstrate that jointly incorporating structural, semantic, and sentiment information yields more coherent, semantically aligned communities than structure-only baselines while maintaining practical scalability.
Heterogeneous Graph Attention Network (HGAT) is proposed, where a pretrained BERT-Large encoder is coupled with a Heterogeneous Graph Attention Network (HGAT) to learn joint representations for textual, social network and external knowledge graph features.
The Syntactic-optimal Transport Graph Network (SOT-Graph) is proposed, a model that jointly integrates structural and distributional signals and outperforms existing baselines by a margin of 1.30% Macro-F1 on Laptop14 and 1.01% on Twitter.
An innovative model which combines transformer-based context embedding, BiLSTM for capturing of sentiment flows, and GAT for examining relational data is introduced which incorporates contextual, sequential and relational modelling of multilingual opinion mining.
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 th...
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