Cross-Domain Error Correction in Personality Prediction

Abstract

In this paper, we analyze domain bias in automated textbased personality prediction, and proposes a novel method to correct domain bias. The proposed approach is very general since it requires neither retraining a personality prediction system using examples from a new domain, nor any knowledge of the original training data used to develop the system. We conduct several experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy.