Arousal Label

Arousal labeling, a key aspect of affective computing, focuses on quantifying the intensity of emotional states along a continuous scale, often in conjunction with valence (positive/negative affect). Current research emphasizes developing robust models for arousal prediction using diverse data sources like facial expressions, physiological signals (EEG, pupil dilation), text (lyrics, stories, social media posts), and audio-visual movie content, employing techniques such as deep neural networks (CNNs, Transformers), and machine learning algorithms (random forests, support vector machines). These advancements are improving the accuracy and efficiency of emotion recognition systems, with implications for human-robot interaction, mental health assessment, and the creation of more emotionally intelligent technologies.