Predicting Personality Using Facebook Status Based on Semi-supervised Learning

Abstract

Personality analysis on social media is a research hotspot due to the importance of personality research in psychology as well as the rapid development of social media. Many studies have used social media status to analyze user's personality, but most of them are conducted on inadequate label data and linguistic features. In this paper, to explore the usage of unlabeled data on personality analysis, a personality analysis framework based on semi-supervised learning is introduced. Besides, for making full use of the language information in social media status, the well-known n-gram model is adopted to extract linguistic features. The experimental results demonstrate the semi-supervised learning can take advantage of unlabeled data and improve the accuracy of prediction model.