Understanding Explicit and Implicit Affect through Neuro-Symbolic Reasoning

Implicit affect recognition seeks to understand what people feel beyond what they literally say. This requires distinguishing surface affect from underlying attitudes and understanding how the two relate. However, recent LLM-based methods that train on final labels or overall rewards can overlook errors in either layer and fail to teach their relationship. We propose PAIR (Polarity-Aware Implicit Affect Recognition), a neuro-symbolic framework that represents both explicit and implicit layers and uses symbolic rules to connect them, making their individual correctness and joint consistency available for learning. Specifically, neural models interpret explicit expression and implicit attitude through two reasoning paths, while symbolic rules compose their polarity judgments into affective stance, preserving both agreement and divergence. During supervised fine-tuning, symbolic verification selects trajectories with correct judgments and valid composition; during reinforcement learning, local rewards refine individual judgments, while global rewards evaluate the composed stance alongside pragmatic intent and fine-grained emotion. On CueBench, PAIR achieves three-task average accuracies of 64.25% and 53.93% on Qwen3-4B and Qwen3-1.7B, outperforming DAPO by 5.88 and 3.37 percentage points, respectively. Further analyses show complementary gains from structured supervision and semantically aligned local feedback, including improved joint explicit–implicit correctness.