Emotion Prediction

Emotion prediction research aims to computationally model and predict human emotional states from various input modalities, such as text, speech, facial expressions, and physiological signals. Current research emphasizes multimodal approaches, leveraging deep learning architectures like graph neural networks, transformers, and recurrent neural networks to capture complex temporal dependencies and contextual information within conversations or other interactions. This field is significant for advancing human-computer interaction, improving mental health monitoring, and informing the development of more empathetic and socially intelligent AI systems. Furthermore, ongoing work addresses challenges like data scarcity, handling ambiguity and missing modalities, and improving model interpretability and robustness.