A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction

• URL: http://arxiv.org/abs/2603.03327v1
• Date: Tue, 10 Feb 2026 09:37:56 GMT
• Title: A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction
• Abstract summary: Monitoring and understanding user emotions during interactions helps predict and improve satisfaction.<n> relevant Chinese datasets are limited, and user emotions are dynamic.<n>We constructed a multi-task, multi-label Chinese dialogue dataset that supports satisfaction recognition, as well as emotion recognition and emotional state transition prediction.
• Score: 18.462328613441453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User satisfaction is closely related to enterprises, as it not only directly reflects users' subjective evaluation of service quality or products, but also affects customer loyalty and long-term business revenue. Monitoring and understanding user emotions during interactions helps predict and improve satisfaction. However, relevant Chinese datasets are limited, and user emotions are dynamic; relying on single-turn dialogue cannot fully track emotional changes across multiple turns, which may affect satisfaction prediction. To address this, we constructed a multi-task, multi-label Chinese dialogue dataset that supports satisfaction recognition, as well as emotion recognition and emotional state transition prediction, providing new resources for studying emotion and satisfaction in dialogue systems.

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