Emotion Annotation

Emotion annotation focuses on automatically labeling data (text, speech, images, video) with emotional content, aiming to improve the accuracy and efficiency of emotion recognition systems. Current research emphasizes leveraging large language models (LLMs) to automate or improve annotation processes, exploring novel architectures like transformers and contrastive learning for enhanced performance, and developing new datasets with richer, more nuanced emotional labels. This work is crucial for advancing affective computing, enabling more accurate and robust emotion recognition in applications ranging from mental health care to personalized user interfaces.