Vertical Meets Horizontal: Disentangling Affective Geometry for Plug-and-Play Structured Prediction

Multimodal sentiment analysis has made substantial progress in learning expressive cross-modal representations, yet final prediction is still commonly performed by flat classifiers that treat sentiment categories as independent targets. Such representations implicitly form a task-specific affective space whose inter-class organization defines a learned affective geometry that may remain underexploited during prediction. To address this limitation, Vertical-Meets-Horizontal Prediction (VMH-P) is introduced as a plug-and-play structured prediction framework that exploits learned affective geometry from fully trained multimodal models without modifying their representation space. VMH-P aligns available multimodal representations into a shared evidence space and disentangles the resulting affective geometry along two complementary directions. Vertically, latent affective regions are induced from class-center geometry and embedded into a Lorentz hierarchy, where path-wise routing and leaf-level discrimination jointly produce hierarchical class evidence. Horizontally, centered class directions establish supportive and contrasting relations whose strengths are adaptively refined to enable inter-class evidence propagation. The resulting structured evidence is aggregated across sources and introduced into the host logits through a confidence-gated residual correction. Experiments on three multimodal affective datasets with three heterogeneous host models show consistent improvements across all nine host–dataset combinations, with average gains of 3.84% and 4.12% in ACC and F1, respectively. The code is publicly available at https://anonymous.4open.science/r/VMH-P-5692.