Microblog users' Big-Five personality prediction based on multi-task learning

doi:10.7523/j.issn.2095-6134.2018.04.019

Abstract

Abstract: Most of traditional prediction methods of social network users' personality are based on single-task classification or regression machine learning. They ignore the potential related information between multiple tasks, and are very difficult to get admirable prediction results based on small scale training data. In this paper, a robust multi-task learning method (RMTL) is proposed to predict Big-Five personality of Microblog users, and it can not only share the task relations, but also identify irrelevant (outlier) tasks. The model is first decomposed into two components, i.e., a structure and an outlier, and then the nucleus norm and L₁/L₂ norm are used to constrain the regular term so as to solve the optimization problems. With Sina Microblog users' data, we validate the RMTL method, and the average correct rate, average precision rate, and average recall rate of the five dimensions are 67.3%, 71.5%, and 74.6%, respectively. The RMTL method outperforms the 4 single-task learning methods and the multi-task learning.

Key words: Sina microblog, personality prediction, multi-task learning, robust, prediction accuracy

CLC Number:

TN911.22

ZHENG Jinghua, GUO Shize, GAO Liang, ZHAO Nan. Microblog users' Big-Five personality prediction based on multi-task learning[J]. , 2018, 35(4): 550-560.

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