Causality Aware Multimodal Reasoning Network in Human Emotion Identification and Sentiment Understanding

Sentiment and emotion recognition in dynamic English communication require intelligent systems capable of reasoning beyond surface correlations among linguistic, acoustic, and visual cues. Traditional multimodal approaches exhibit limited interpretability, weak contextual adaptability, and lack causal understanding of emotional expressions, resulting in inconsistent predictions under ambiguous conditions. To address these challenges, a Context-Adaptive Knowledge-Guided Causal Reasoning Network (CKCR-Net) is introduced, integrating external semantic and affective knowledge with multimodal fusion to ensure transparency and contextual sensitivity. The proposed framework employs a Dynamic Multimodal Knowledge Graph (DMKG), hierarchical cross-modal attention, and a dual-stage causal reasoning module to infer cause–effect dependencies among modalities. The model was implemented in Python (PyTorch) using the CMU-MOSEI benchmark dataset and optimized through Adam optimizer and consistency-based loss regularization. CKCR-Net achieved an accuracy of 97.5%, precision of 96.4%, recall of 97.2%, and F1-score of 97.3%, significantly outperforming models such as CM-BERT (89.4%), RoBERTa (71%), and TFIDF-based fusion (96.9%). The causal reasoning mechanism improved recognition of subtle emotions like sarcasm and empathy, enhancing interpretability through attention heatmaps and counterfactual analysis. Overall, CKCR-Net provides an explainable, context-sensitive, and high-performing framework for multimodal sentiment analysis, offering a reliable pathway toward transparent affective computing and human–machine communication.

@article{Thakre2025,
  title     = {Causality Aware Multimodal Reasoning Network in Human Emotion Identification and Sentiment Understanding},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {N. K. Thakre and Yazan Shaker Almahammed and G. Indra Navaroj and Mohammed Fahad Almohazie and Abdullah Albalawi and Marran Al Qwaid and G. Sanjiv Rao},
  doi       = {10.14569/IJACSA.2025.0161166},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161166}
}