Emotion Category

Emotion categorization in various modalities (text, speech, video) aims to identify and classify emotional states, focusing on both discrete categories (e.g., happiness, anger) and continuous dimensions (valence, arousal). Current research emphasizes the development of robust models, often employing deep learning architectures like transformers and recurrent neural networks, to handle diverse datasets, including spontaneous and naturalistic data, and address challenges like imbalanced class distributions and cross-lingual variations. This work is crucial for advancing human-computer interaction, mental health applications, and a deeper understanding of human emotion itself.