Predicting big-five personality for micro-blog based on robust multi-task learning

Abstract

Personality prediction on social network has become a hot topic. At present, most studies are using single-task classification/regression machine learning. However, this method ignores the potential association between multiple tasks. Also an ideal prediction result is difficult to achieve based on the small scale training data, since it is not easy to get a lot of social network data with personality label samples. In this paper, a robust multi-task learning method (RMTL) is proposed to predict Big-Five personality on Micro-blog. We aim to learn five tasks simultaneously by extracting and utilizing appropriate shared information among multiple tasks as well as identifying irrelevant tasks. For a set of Sina Micro-blog users’ information and personality labeled data retrieved by questionnaire, we validate the RMTL method by comparing it with 4 single-task learning methods and the mere multi-task learning. Our experiment demonstrates that the proposed RMTL can improve the precision rate, recall rate of the prediction and F value.