CogSent: Cognition-Driven Multimodal Sentiment Analysis Through Fast–Slow Thinking

Abstract

Sentiment analysis of multimodal social media data is of great importance, not only for recognizing objective information but also for capturing subjective emotional states. While single-modal sentiment analysis has achieved notable progress, existing multimodal approaches still face two key challenges: (1) inadequate modeling of subjective emotional characteristics and (2) insufficient handling of cross-modal inconsistencies. To address these limitations, we propose an image-text multimodal sentiment analysis framework (CogSent) grounded in the psychological Dual-System Theory. First, inspired by human fast and slow thinking, we develop a Hierarchical Dual-Channel Cognition (HDCC) architecture to extract intuitive and rational affective features, respectively. Second, we introduce an Intuition-Guided Cognition Refinement (IGCR) module that uses System-I-inspired intuitive representations as affective priors to retrieve and refine System-II-inspired contextual representations via cross-attention. Third, we propose a Dynamic Cross-Modal Cognition Fusion (DC2F) network that predicts sample-adaptive thresholds from modality discrepancy, agreement, and attention statistics, dynamically regulating directional image–text interaction to mitigate interference from conflicting cross-modal sentiment signals. Extensive experiments on public benchmarks demonstrate that CogSent achieves improved performance compared to existing methods and yields competitive results on multiple evaluation settings.