Towards emotion recognition from contextual information using machine learning

Emotions influence cognitive processes that underlie human behavior. Whereas experiencing negative emotions may lead to develop psychological disorders, experiencing positive emotions may improve creative thinking and promote cooperative behavior. The importance of human emotions has led to the development of automatic emotion recognition systems based on analysis of speech waveforms, facial expressions, and physiological signals as well as text data mining. However, emotions are associated with a context (in which emotions are actually experienced), hence, this work focuses on emotion recognition from contextual information. In this paper, we present a study aimed to assess the feasibility of automatically recognizing emotions from individuals’ contexts. In this study, 32 participants provided information using a mobile application about their emotions and the context (e.g., companions, activities, and locations) in which these emotions were experienced. We used machine learning techniques to build individual models, general models, and gender-specific models to automatically recognize emotions of participants. The empirical results show that individuals’ emotions are highly related to their context and that automatic recognition of emotions in real-world situations is feasible by using contextual data.