Automated Domain Bias Correction and Its Application in Text-Based Personality Analysis

Personality prediction based on textual data is one topic gaining attention recently for its potential in large-scale personalized applications such as social media-based marketing. However, when applying this technology in real-world applications, users often encounter situations in which the personality traits derived from different sources (e.g., social media posts versus emails) are inconsistent. Varying results for the same individual renders the technology ineffective and untrustworthy. In this paper, we demonstrate the impact of domain differences in automated text-based personality prediction. We also propose different approaches for domain error correction to meet different needs: (a) single or multi-domain correction and (b) outcome-based or input feature-based error correction. We conduct comprehensive experiments to evaluate the effectiveness of these methods. Our findings demonstrate a significant improvement of prediction accuracy with the proposed methods. (e.g., 20–30% relative error reduction using outcome-based error correction or 48% increase of F1 score using feature-based error correction).