Speaker: Areej Alhothali, PhD Candidate
We propose an affect control theory-based (ACT) model to predict the temporal progression of the interactants' emotions and their optimal behavior over a sequence of interactions. ACT has a solid foundation in sociology to interpret, understand, and predict human social interactions. In this study, we extend the sociological mathematical model in ACT by learning and incorporating parameters from real data.
Our model analyzes and predicts the evolved sentiments and most emotionally aligned behavior after each interaction taking into account the previous events and emotions of the main characters' in event-based documents (stories, blogs, and news articles). We evaluated the proposed model and compared the estimated results against two types of event-based documents: fairy tales and political news articles. The results show that there is a reasonable agreement between the estimated emotions and behavior and their corresponding ground truth.