Exploiting social graph networks for emotion prediction.

ABSTRACT: Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person's physiology, we also incorporate the environment's impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur a

SUBMITTER: Khalid M 

PROVIDER: S-EPMC10100636 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature