SHAP-Weighted Cross-Modal Expert Fusion for Emotion and Sentiment Recognition: Evidence and Limits
arXiv:2607.08573v1 Announce Type: new Abstract: Multimodal emotion and sentiment recognition is commonly addressed by early fusion, which concatenates modalities before classification, or late fusion, which combines independently trained unimodal predictors. Early fusion can be accurate but monolithic, while late fusion is modular but may lose cross-modal interactions. This paper revisits XAI-guided adaptive fusion (\xgaf), a tree-based mixture of unimodal and cross-modal experts whose sample-level weights are derived from TreeSHAP attribution magnitudes. We focus on the effect of SHAP attribu
This paper leverages explainable AI (XAI) techniques, specifically SHAP, which has matured as a method for understanding model decisions, to improve multimodal AI systems.
Improving multimodal AI's ability to interpret emotion and sentiment has direct implications for human-computer interaction, advanced AI agents, and content analysis.
The proposed method offers a more modular and potentially more accurate approach to integrating diverse data streams for complex sentiment and emotion tasks, enhancing AI's interpretive capabilities.
- · AI developers
- · Customer service industries
- · Social media analytics
- · Human-computer interaction researchers
- · Monolithic early fusion approaches
More robust and psychologically nuanced AI applications become possible.
Improved AI systems could lead to more effective adaptive interfaces and personalized digital experiences.
The enhanced understanding of human intent and feeling could accelerate the development of truly autonomous and socially aware AI agents.
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Read at arXiv cs.AI