Personality Recognition on Social Media With Label Distribution Learning

• Published in: IEEE access Vol. 5; pp. 13478 - 13488
• Main Authors: Xue, Di, Hong, Zheng, Guo, Shize, Gao, Liang, Wu, Lifa, Zheng, Jinghua, Zhao, Nan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; big five personality; Blogs; Digital media; Feature extraction; label distribution learning; Machine learning; Machine learning algorithms; Personality; Personality recognition; Prediction algorithms; Prediction models; Psychology; Recognition; social media mining; Social network services; Social networks; Support vector machines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2719018

Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual's personality is a worthwhile goal; however, the traditional ways of personality assessment through self-report inventories or interviews conducted by psychologists are costly and less practical in social media domains, since they need the subjects to take active actions to cooperate. This paper proposes a method of big five personality recognition (PR) from microblog in Chinese language environments with a new machine learning paradigm named label distribution learning (LDL), which has never been previously reported to be used in PR. One hundred and thirteen features are extracted from 994 active Sina Weibo users' profiles and micro-blogs. Eight LDL algorithms and nine non-trivial conventional machine learning algorithms are adopted to train the big five personality traits prediction models. Experimental results show that two of the proposed LDL approaches outperform the others in predictive ability, and the most predictive one also achieves relatively higher running efficiency among all the algorithms.