Valence Arousal

Valence-arousal (VA) modeling aims to quantify emotional states along two dimensions: valence (pleasantness) and arousal (intensity). Current research focuses on improving the accuracy of VA estimation using multimodal data (audio, visual, text, EEG) and advanced machine learning techniques, including transformer networks, variational autoencoders, and attention mechanisms, often within the context of competitions and benchmark datasets. These advancements have implications for various fields, such as human-computer interaction, affective computing, and even literary analysis, by enabling more nuanced understanding and modeling of human emotions. The development of robust and accurate VA models contributes to the creation of more empathetic and responsive technologies.