Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining
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 data...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.705005/full |
| _version_ | 1818455539296763904 |
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| author | Gancheng Zhu Yuci Zhou Yuci Zhou Fengfeng Zhou Min Wu Xiangping Zhan Yingdong Si Peng Wang Jun Wang |
| author_facet | Gancheng Zhu Yuci Zhou Yuci Zhou Fengfeng Zhou Min Wu Xiangping Zhan Yingdong Si Peng Wang Jun Wang |
| author_sort | Gancheng Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T22:12:23Z |
| format | Article |
| id | doaj.art-04a8a96dbec94cf4aa33eafdba1c87e5 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-14T22:12:23Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-04a8a96dbec94cf4aa33eafdba1c87e52022-12-21T22:45:42ZengFrontiers Media S.A.Frontiers in Psychology1664-10782021-07-011210.3389/fpsyg.2021.705005705005Proactive Personality Measurement Using Item Response Theory and Social Media Text MiningGancheng Zhu0Yuci Zhou1Yuci Zhou2Fengfeng Zhou3Min Wu4Xiangping Zhan5Yingdong Si6Peng Wang7Jun Wang8Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaNormal College, Weifang Institute of Technology, Weifang, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, ChinaNormal College, Weifang Institute of Technology, Weifang, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaThis 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2021.705005/fullmeasurementproactive personalityitem response theorytext miningmachine learning |
| spellingShingle | Gancheng Zhu Yuci Zhou Yuci Zhou Fengfeng Zhou Min Wu Xiangping Zhan Yingdong Si Peng Wang Jun Wang Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining Frontiers in Psychology measurement proactive personality item response theory text mining machine learning |
| title | Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining |
| title_full | Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining |
| title_fullStr | Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining |
| title_full_unstemmed | Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining |
| title_short | Proactive Personality Measurement Using Item Response Theory and Social Media Text Mining |
| title_sort | proactive personality measurement using item response theory and social media text mining |
| topic | measurement proactive personality item response theory text mining machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.705005/full |
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