Emotion Classification

Emotion classification is an important task in natural language processing, aiming to identify and categorize the emotional states expressed in text. This task involves analyzing multimodal information such as facial expressions and speech of the subject, and classifying their emotions into predefined categories, such as neutral or no emotion, anger, joy, etc. Emotion classification has significant application value in fields like human-computer interaction, sentiment analysis, and mental health assessment. Common evaluation metrics include the Concordance Correlation Coefficient (CCC) and Mean Squared Error (MSE).