Emotion Dynamic

Emotion dynamics research focuses on understanding how emotions change over time, aiming to model and predict these shifts across various modalities like speech, text, and music. Current research employs diverse approaches, including deep learning architectures like convolutional and recurrent neural networks, state space models, and hybrid methods combining text analysis with emoji sentiment, to analyze emotional trajectories in different contexts. This field is significant for its potential applications in improving human-computer interaction, mental health assessment, and enhancing user experience in areas such as advertising and customer service. The development of robust and generalizable models for emotion recognition and prediction is a key focus, particularly addressing challenges like cross-corpus bias and the need for more nuanced emotional representations.