Emotional Feature

Emotional feature extraction and analysis are crucial for understanding and modeling human affect across various modalities, including text, audio, and video. Current research focuses on developing robust methods for extracting these features, often employing deep learning architectures like transformers (BERT, WavLM) and recurrent neural networks (GRU, LSTM), and integrating them into multimodal fusion models for improved accuracy in emotion recognition tasks. This work has significant implications for applications ranging from mental health interventions and personalized recommendations to improved human-computer interaction and the detection of misinformation.