emotiondetection
Emotion detection, also referred to as affect detection or emotion recognition, refers to computational methods for identifying or inferring human emotional states from data such as facial expressions, voice, text, or physiological signals. Emotions can be represented as discrete categories (for example happiness, sadness, anger) or as continuous dimensions such as valence and arousal. Systems may operate on a single modality or multiple modalities to improve accuracy.
Common data sources include facial expression analysis, speech prosody and voice tone, physiological signals (such as
Methodologically, emotion detection uses machine learning, including traditional classifiers and deep learning models such as convolutional
Applications span customer service, user experience research, driver monitoring, education, marketing, and mental health support. Proponents
Key challenges include the subjectivity of emotion, variability across individuals and contexts, labeling difficulties, and reliability