MBTI personality prediction using GPT-2 LLM augmentation and ensemble machine learning approaches.
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| Title: | MBTI personality prediction using GPT-2 LLM augmentation and ensemble machine learning approaches. |
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| Authors: | Patel, Devraj1 (AUTHOR) devraj.patel23@gmail.com, Dhavale, Sunita V.1 (AUTHOR) sunitadhavale@diat.ac.in |
| Source: | Multimedia Tools & Applications. Feb2026, Vol. 85 Issue 2, p1-34. 34p. |
| Abstract: | Personality influences how individuals respond to situations and interact within teams, making its prediction valuable in domains such as recruitment, counselling, and military operations. The Myers-Briggs Type Indicator (MBTI) is widely used to assess personality, but traditional assessments require expert supervision and are time-consuming. With the growing presence of social media, this study explores automatic MBTI personality prediction using text data from online interactions. We address key challenges in MBTI classification, including data imbalance and low classification accuracy, by introducing a dual-tier oversampling strategy that combines GPT–2–based contextual data generation with the Synthetic Minority Oversampling Technique (SMOTE). Various word embedding methods and machine learning classifiers were evaluated, with ensemble learning techniques (voting, stacking, and blending) yielding the best performance. The proposed approach achieved an average accuracy of 90.39% and an F1-score of 0.9037, outperforming existing models. This study demonstrates the effectiveness of combining contextual augmentation with ensemble learning, offering a robust framework for scalable and reliable MBTI personality prediction from online text data. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191366899 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MBTI personality prediction using GPT-2 LLM augmentation and ensemble machine learning approaches. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Patel%2C+Devraj%22">Patel, Devraj</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> devraj.patel23@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Dhavale%2C+Sunita+V%2E%22">Dhavale, Sunita V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sunitadhavale@diat.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Feb2026, Vol. 85 Issue 2, p1-34. 34p. – Name: Abstract Label: Abstract Group: Ab Data: Personality influences how individuals respond to situations and interact within teams, making its prediction valuable in domains such as recruitment, counselling, and military operations. The Myers-Briggs Type Indicator (MBTI) is widely used to assess personality, but traditional assessments require expert supervision and are time-consuming. With the growing presence of social media, this study explores automatic MBTI personality prediction using text data from online interactions. We address key challenges in MBTI classification, including data imbalance and low classification accuracy, by introducing a dual-tier oversampling strategy that combines GPT–2–based contextual data generation with the Synthetic Minority Oversampling Technique (SMOTE). Various word embedding methods and machine learning classifiers were evaluated, with ensemble learning techniques (voting, stacking, and blending) yielding the best performance. The proposed approach achieved an average accuracy of 90.39% and an F1-score of 0.9037, outperforming existing models. This study demonstrates the effectiveness of combining contextual augmentation with ensemble learning, offering a robust framework for scalable and reliable MBTI personality prediction from online text data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-026-21265-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 1 Titles: – TitleFull: MBTI personality prediction using GPT-2 LLM augmentation and ensemble machine learning approaches. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Patel, Devraj – PersonEntity: Name: NameFull: Dhavale, Sunita V. IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 85 – Type: issue Value: 2 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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