Emotion Representation

Emotion representation in computational models focuses on accurately capturing and classifying emotional states from various data modalities, such as text, speech, and images, aiming to bridge the gap between human emotional experience and machine understanding. Current research emphasizes developing robust models, often employing deep learning architectures like transformers and masked autoencoders, to address challenges like data noise, label ambiguity, and the inherent subjectivity of emotion. This field is significant for advancing artificial intelligence's ability to interact naturally with humans and has practical applications in areas such as mental health monitoring, personalized recommendations, and ethical AI development.