Emotion Corpus

Emotion corpora are collections of annotated data used to train and evaluate models for emotion recognition in text and speech. Current research focuses on improving model performance through techniques like parameter-efficient fine-tuning of large pre-trained models and addressing biases stemming from non-representative data sampling in existing corpora. These efforts are crucial for advancing emotion recognition across languages and modalities, with applications ranging from mental health support to improved human-computer interaction. The development of more diverse and representative corpora remains a key challenge, impacting the fairness and generalizability of emotion recognition systems.