Emotion Recognition in Conversation (ERC) aims to identify the emotional states of speakers in conversations. Existing ERC methods perform either fast thinking or slow thinking for emotion predictions. The former lacks interpretability of emotion predictions, and the latter focuses on emotion analysis at shallow semantics. Such insufficient reasoning chains fall short in capturing deep semantics within conversations. To address these limitations, we propose ERCThinker, a Fast-Slow thinking framework for the task of ERC. First, we design different thinking strategies with fine-grained emotion reasoning chains to capture deep semantics that contain topic, discourse structure, speaker characteristic, scene, and emotion shift. Second, we develop an adaptive thinking mechanism in both strategy-level and utterance-level, guiding the model to dynamically perform thinking switching across various scenarios. Furthermore, we utilize Agent-as-Judge to score reasoning chains as reward signals for more accurate emotion predictions. To support training, we construct EmotionCueCoT, the emotion reasoning dataset with supervision in both explanation and judgment. Extensive experiments on various ERC benchmark datasets demonstrate that ERCThinker achieves state-of-the-art performance in both explanation and judgment, making progress in the realm of ERC.

Emotion Recognition in Conversation (ERC), the task of identifying the emotion of each utterance in a conversation, is crucial for human-machine interaction. Existing LLM-based ERC methods focus on standard prompting and slow thinking for emotion analysis. However, they suffer from the lack of human-like emotion reasoning and discrimination between similar emotions, thus limiting accurate emotion predictions. To this end, we present JoPR, jointing perception-curriculum learning and emotional reasoning for conversational emotion recognition. Specifically, we devise a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning. We further design an emotion-specific reward function in a novel reinforcement learning framework, thereby enhancing the discernment between similar emotions. Our proposal is extensively evaluated over three widely used benchmark datasets, and experimental results confirm the superiority of JoPR. Furthermore, we provide an in-depth analysis to confirm the emotion perception and reasoning capabilities of JoPR.