Twitter user behavior understanding with mood transition prediction

Abstract

Human moods continuously change over time. Tracking moods can provide important information about psychological and health behavior of an individual. Also, history of mood information can be used to predict the future moods of individuals. In this paper, we try to predict the mood transition of a Twitter user by regression analysis on the tweets posted over twitter time line. Initially, user tweets are automatically labeled with mood labels from time 0 to t-1. It is then used to predict user mood transition information at time t. Experiments show that SVM regression attained less root-mean-square error compared to other regression approaches for mood transition prediction.