Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACCESS.2017.2719018 | IEEE ACCESS |
Keywords | Field | DocType |
Personality recognition,label distribution learning,social media mining,big five personality | Big Five personality traits,Social media,Computer science,Social media mining,Personality Assessment Inventory,Support vector machine,Microblogging,Feature extraction,Artificial intelligence,Machine learning,Personality | Journal |
Volume | ISSN | Citations |
5 | 2169-3536 | 2 |
PageRank | References | Authors |
0.36 | 35 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Xue | 1 | 8 | 1.88 |
| Zheng Hong | 2 | 14 | 3.78 |
| Shize Guo | 3 | 145 | 23.21 |
| Liang Gao | 4 | 3 | 0.71 |
| Lifa Wu | 5 | 47 | 15.85 |
| Jinghua Zheng | 6 | 2 | 0.70 |
| nan zhao | 7 | 5 | 2.07 |