Emotion Determination

Emotion determination research focuses on accurately identifying and classifying emotions from various data sources, aiming to improve the performance and explainability of emotion recognition systems. Current research employs machine learning models, including convolutional neural networks (CNNs), tree ensembles, and attention-based models like BERT, often incorporating techniques like Shapley values for feature importance analysis and addressing data imbalance issues. This field has significant implications for diverse applications such as mental health monitoring, human-computer interaction, and assistive technologies, with ongoing efforts to improve accuracy, robustness, and the interpretability of these models.