Multimodal Affective Analysis (MAA) has made significant progress, but current methods still lack an explicit mechanism to decide when modality-specific (private) features should be combined with modality-invariant (shared) representations and when they should be ignored. Addressing this limitation is necessary because such private cues can be redundant, conflicting, or noisy. We propose SPriG, a shared–private architecture that adds improvement-guided control over private information. Each modality is mapped to a common feature space and split into shared and private (modality-specific) components. A shared-only fusion path first produces a robust baseline prediction, and a gated private residual then allows each modality to contribute additional information only when it helps relative to this baseline. A set of auxiliary objectives guides the disentanglement and model training behaviour. We evaluate SPriG across five public benchmarks covering key areas of affective computing, including sentiment prediction, conversational emotion classification, and humor detection. Across these datasets, SPriG consistently outperforms recent state-of-the-art methods on both regression and classification metrics, demonstrating the effectiveness of our approach.
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