A Data-Driven Approach to MBTI Personality Classification: Insights from Machine Learning Models

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dc.contributor.authorDael, Fares A.
dc.contributor.authorMaidanova, Symbat
dc.contributor.authorShayea, Ibraheem
dc.contributor.authorAbitova, Gulnara
dc.contributor.authorSeraly, Aigul
dc.date.accessioned2025-03-20T09:44:58Z
dc.date.available2025-03-20T09:44:58Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.descriptionIEEE MP Section; Institution of Electronics and Telecommunications Engineers (IETE)
dc.description16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024 -- 22 December 2024 through 23 December 2024 -- Indore -- 206392
dc.description.abstractThe Myers-Briggs Type Indicator (MBTI) is one of the most widely recognized psychological tools for categorizing personality types, often used in various professional and personal development contexts. This study presents a data-driven approach to MBTI personality classification using a range of machine learning models. By leveraging a dataset comprising user responses and linguistic patterns, we aim to predict the MBTI personality types with greater accuracy and reliability. Various models, including Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines, were evaluated to determine their effectiveness in classifying the 16 MBTI types. Our findings reveal that machine learning models can significantly enhance the predictive accuracy of MBTI classification compared to traditional methods. The Random Forest model, in particular, demonstrated superior performance, achieving an accuracy of [insert specific accuracy here] across the dataset. We also explore the importance of feature selection and data preprocessing in improving model outcomes, highlighting key features that contribute to personality type prediction. The results of this study suggest that a data-driven approach, combined with machine learning techniques, provides a promising avenue for more nuanced and accurate MBTI personality assessments. This approach not only enhances our understanding of personality prediction but also offers practical implications for applications in psychology, human resources, and personal development. © 2024 IEEE.
dc.identifier.doi10.1109/CICN63059.2024.10847430
dc.identifier.endpage1303
dc.identifier.isbn979-833150526-4
dc.identifier.scopus2-s2.0-85218035477
dc.identifier.scopusqualityN/A
dc.identifier.startpage1297
dc.identifier.urihttps://doi.org/10.1109/CICN63059.2024.10847430
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2099
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 2024 IEEE 16th International Conference on Communication Systems and Network Technologies, CICN 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250319
dc.subjectCatBoost
dc.subjectDecision Tree
dc.subjectMachine learning
dc.subjectnatural language processing
dc.subjectSVM
dc.titleA Data-Driven Approach to MBTI Personality Classification: Insights from Machine Learning Models
dc.typeConference Object

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