Appraisal Theory

Appraisal theory explores how individuals' cognitive evaluations of events—considering factors like novelty, controllability, and goal relevance—shape their emotional responses. Current research focuses on applying this theory to improve the understanding and prediction of human emotions in various contexts, including analyzing social media sentiment, predicting user behavior, and enhancing the emotional intelligence of large language models (LLMs). This work leverages machine learning techniques, often employing multi-task learning frameworks and supervised models trained on corpora annotated with appraisal dimensions, to achieve more nuanced and accurate emotion recognition and generation. The insights gained are valuable for advancing computational psychology, improving human-computer interaction, and developing more empathetic AI systems.