Affective Datasets

Affective datasets are collections of data representing human emotions, encompassing various modalities like text, audio, and video, and employing diverse labeling schemes (e.g., dimensional, categorical). Current research focuses on developing robust models, often leveraging self-supervised learning and techniques like contrastive language-image pre-training, to address the heterogeneity of existing datasets and improve emotion classification accuracy across different media. This work is crucial for advancing affective computing, enabling applications such as improved human-computer interaction, mental health monitoring, and the design of more effective remote work environments. The creation of large, diverse, and well-annotated datasets remains a key challenge and driver of progress in the field.