Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining

Frontiers in Psychology

article

Gancheng Zhu¹  Yuci Zhou²  Fengfeng Zhou¹  Min Wu²  Xiangping Zhan³  Yingdong Si³  Peng Wang³  Jun Wang³ 

[1] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University;Normal College, Weifang Institute of Technology;School of Psychology, Shandong Normal University

关键词: measurement;    proactive personality;    item response theory;    text mining;    machine learning;   

DOI  :  10.3389/fpsyg.2021.705005

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

This prospective study was designed to propose a novel method of assessing proactive personality by combining text mining technology and Item Response Theory (IRT) to measure proactive personality more efficiently. We got freely expressed texts (essay question text dataset and social media text dataset) and item response data on the topic of proactive personality from 901 college students. To enhance validity and reliability, three different approaches were employed in the study. In Method 1, we used item response data to develop a proactive personality evaluation model based on IRT. In Method 2, we used freely expressed texts to develop a proactive personality evaluation model based on text mining. In Method 3, we utilized the text mining results as the prior information for the IRT estimation and built a proactive personality evaluation model combining text mining and IRT. Finally, we evaluated those three approaches via the confusion matrix indicators. The major result revealed that (1) the combined method based on essay question text, micro-blog text with pre-estimated IRT parameters performed the highest accuracy of 0.849; (2) the combined method using essay question text and pre-estimated IRT parameters performed the highest sensitivity of 0.821; (3) the text classification method based on essay question text had the best performance on the specificity of 0.959; and (4) if the models were considered comprehensively, the combined method using essay question text, micro-blog text, and pre-estimated IRT parameters achieved the best performance. Thus, we concluded that the novel combined method was significantly better than the other two traditional methods based on IRT and text mining.

【 授权许可】

CC BY