Dimensional Emotion

Dimensional emotion research focuses on understanding and modeling emotions along continuous dimensions like valence (pleasantness), arousal (intensity), and dominance, rather than discrete categories. Current research heavily utilizes deep learning architectures, including convolutional and recurrent neural networks, often incorporating multimodal data (audio, video, text) and employing techniques like multi-task learning and late fusion to improve emotion recognition accuracy. This work is significant for advancing human-computer interaction, improving mental health assessment tools, and providing a more nuanced understanding of human affect in diverse contexts.