Emotion Probability

Emotion probability research focuses on accurately quantifying and predicting the likelihood of different emotional states in various contexts, such as from text, audio-visual data, or brain activity. Current research employs diverse approaches, including large language models (LLMs) for text analysis, multimodal fusion techniques combining data from different sources (e.g., audio, video, EEG), and novel architectures like normalizing flows to model uncertainty in predictions. This field is significant for advancing human-computer interaction, improving mental health diagnostics, and creating more emotionally intelligent artificial systems.