Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text
Personality assessments are at present nearly entirely dependent on self-reports, and machine learning methods have been rarely applied to this field. This study used machine learning to predict people’s self-reported proactive personalities. Based on a sample of 901 participants that use...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9425529/ |
| _version_ | 1811210743903682560 |
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| author | Peng Wang Meng Yan Xiangping Zhan Mei Tian Yingdong Si Yu Sun Longzhen Jiao Xiaojie Wu |
| author_facet | Peng Wang Meng Yan Xiangping Zhan Mei Tian Yingdong Si Yu Sun Longzhen Jiao Xiaojie Wu |
| author_sort | Peng Wang |
| collection | DOAJ |
| description | Personality assessments are at present nearly entirely dependent on self-reports, and machine learning methods have been rarely applied to this field. This study used machine learning to predict people’s self-reported proactive personalities. Based on a sample of 901 participants that used Weibo text and short answer text, the authors used five machine learning algorithms for classification: Support Vector Machine (SVM), XGboost, k-nearest neighbor (KNN), naïve Bayes, and logistic regression. Seven different indicators – Accuracy (ACC), F1-score(F1), Sensitivity(SEN), Specificity (SPE), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Area under Curve (AUC) – combined with hierarchical cross-validation were also used to make the comprehensive evaluation of models. Based on this, we proposed a method to classify people’s proactive personalities based on text mining technology. The results showed that the SVM and naïve Bayes outperformed the other methods on short texts (i.e., short answer texts) and mixed long texts (i.e., short answer & Weibo text). In the context of the texts used, mixed long text (i.e., short answer & Weibo text) improved and stabilized the indices, and this combination was the best choice of text for predicting proactive personality. In addition, the SVM was the most stable classifier in most situations, even on Weibo text that was not suitable for analysis as long text, and it also recorded good results in terms of accuracy, the F1-score, and the AUC. |
| first_indexed | 2024-04-12T05:00:50Z |
| format | Article |
| id | doaj.art-eaf05995655e4dca83a2850b85024bb8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T05:00:50Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eaf05995655e4dca83a2850b85024bb82022-12-22T03:47:00ZengIEEEIEEE Access2169-35362021-01-019772037721110.1109/ACCESS.2021.30780529425529Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer TextPeng Wang0https://orcid.org/0000-0001-9849-4093Meng Yan1Xiangping Zhan2Mei Tian3Yingdong Si4Yu Sun5Longzhen Jiao6Xiaojie Wu7https://orcid.org/0000-0002-7455-129XSchool of Psychology, Shandong Normal University, Jinan, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaLibrary, 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, ChinaSchool of Psychology, Shandong Normal University, Jinan, ChinaPersonality assessments are at present nearly entirely dependent on self-reports, and machine learning methods have been rarely applied to this field. This study used machine learning to predict people’s self-reported proactive personalities. Based on a sample of 901 participants that used Weibo text and short answer text, the authors used five machine learning algorithms for classification: Support Vector Machine (SVM), XGboost, k-nearest neighbor (KNN), naïve Bayes, and logistic regression. Seven different indicators – Accuracy (ACC), F1-score(F1), Sensitivity(SEN), Specificity (SPE), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Area under Curve (AUC) – combined with hierarchical cross-validation were also used to make the comprehensive evaluation of models. Based on this, we proposed a method to classify people’s proactive personalities based on text mining technology. The results showed that the SVM and naïve Bayes outperformed the other methods on short texts (i.e., short answer texts) and mixed long texts (i.e., short answer & Weibo text). In the context of the texts used, mixed long text (i.e., short answer & Weibo text) improved and stabilized the indices, and this combination was the best choice of text for predicting proactive personality. In addition, the SVM was the most stable classifier in most situations, even on Weibo text that was not suitable for analysis as long text, and it also recorded good results in terms of accuracy, the F1-score, and the AUC.https://ieeexplore.ieee.org/document/9425529/Machine learningproactive personalitytext mining |
| spellingShingle | Peng Wang Meng Yan Xiangping Zhan Mei Tian Yingdong Si Yu Sun Longzhen Jiao Xiaojie Wu Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text IEEE Access Machine learning proactive personality text mining |
| title | Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text |
| title_full | Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text |
| title_fullStr | Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text |
| title_full_unstemmed | Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text |
| title_short | Predicting Self-Reported Proactive Personality Classification With Weibo Text and Short Answer Text |
| title_sort | predicting self reported proactive personality classification with weibo text and short answer text |
| topic | Machine learning proactive personality text mining |
| url | https://ieeexplore.ieee.org/document/9425529/ |
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