Mood Prediction

Mood prediction research aims to automatically infer an individual's emotional state using diverse data sources, including physiological signals (EEG, ECG, PPG), social media posts, and even song lyrics. Current approaches leverage various machine learning models, such as deep neural networks (including transformers and convolutional recurrent networks), and ensemble methods like LightGBM, often incorporating multimodal data fusion and pre-trained models for improved accuracy and efficiency. This field holds significant promise for early detection and intervention in mental health conditions, personalized mental healthcare, and enhancing human-computer interaction through more emotionally intelligent systems.