Personality Recognition on Social Media With Label Distribution Learning

被引:35
|
作者
Xue, Di [1 ]
Hong, Zheng [1 ]
Guo, Shize [2 ]
Gao, Liang [2 ]
Wu, Lifa [1 ]
Zheng, Jinghua [3 ]
Zhao, Nan [4 ]
机构
[1] PLA Univ Sci & Technol, Coll Command Informat Syst, Nanjing 210002, Jiangsu, Peoples R China
[2] Inst North Elect Equipment, Beijing 100083, Peoples R China
[3] Elect Engn Inst, Hefei 230037, Anhui, Peoples R China
[4] Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China
来源
IEEE ACCESS | 2017年 / 5卷
关键词
Personality recognition; label distribution learning; social media mining; big five personality; NETWORKS; BEHAVIOR; TRAITS;
D O I
10.1109/ACCESS.2017.2719018
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:13478 / 13488
页数:11
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