A new framework for tracking emotional states in conversations uses multimodal data to identify persistent emotional phases, enhancing understanding in clinical settings. This approach offers a more interpretable and cost-effective alternative to existing methods, potentially improving the quality of AI responses in emotionally unstable contexts.