An embodied cognition classifier of human emotion and senses

Abstract

Drawing on the idea of embodied cognition, where the human body and its environment influence the way a person thinks and feels, we test if it is possible to classify suicide attackers from their writing by using words affected by the emotional and modality-specific systems in the brain. We use their final notes and manifestos compared with normal bloggers' posts to train a machine learning classifier. We compare two support vector machine classifier models using linear and radial base function kernels. In this exploratory study, receiver operating characteristic curves show encouraging separation accuracies. These models offer an 11-13% improvement over methods using only emotion or sense categories. This study supports the idea that an embodied cognition classifier better discriminates the way a person thinks and feels rather than treating the body and mind as separate entities and may help in reflecting behavior and applying influence in online social systems.