Post-Based Prediction of Users' Opinions Employing the Social Impact Model Improved by Emotion

Abstract

Opinion formation is a collective behavior, describing the dynamics of people's opinions due to their interactions. Nowadays, social media are broadly used and cause a lot of interactions among users who mainly know each other merely as a username, but significantly influence each others' opinions and emotions. Both emotions and opinions spread across users in social media via their exchanged posts. Furthermore, based on psychology research, emotion affects people's opinion. In this research, we implemented two binary classifiers to predict the users' next opinions considering previous posts sent in online community: an original classifier, a classifier based on the social impact model of opinion formation; and an emotion-integrated classifier, a classifier based on the social impact model of opinion formation integrated with an emotion model to achieve an improved model. To evaluate the improved classifier, we used a dataset containing some debates from the CreateDebate.com website and compared the performance of the original classifier with the performance of the emotion integrated classifier. The experiment results show that considering emotions improves the accuracy and precision of the social impact model of opinion formation in social media.