Enhancing MBTI Personality Prediction from Text Data with Advance Word Embedding Technique.
DOI:
https://doi.org/10.21015/vtse.v12i3.1864Abstract
Understanding human personality traits is crucial for various domains, including psychology, education, and human resources. The Myers-Briggs Type Indicator (MBTI) is a widely recognized psychological assessment tool, categorizing individuals into one of sixteen distinct personality types. The existing methodologies, which primarily relied on Word2Vec embeddings and traditional machine learning models, showed promise but left room for improvement. To address this problem, this research focused on enhancing Myers-Briggs Type Indicator (MBTI) personality prediction from text data through advanced word-embedding techniques, specifically GloVe and BERT. The research investigates the effectiveness of various Machine Learning Classifiers, including Random Forest, XGBoost, LinearSVC, SGD, Logistic Regression, and CatBoost, in predicting MBTI personality types. Additionally, the impact of preprocessing techniques such as text cleaning, tokenization, TF-IDF vectorization, GloVe, and BERT embeddings on classification performance is examined. Furthermore, the research explores strategies for addressing class imbalance through upsampling techniques. Results indicate high accuracy and performance across multiple classifiers, with XGBoost achieving the highest accuracy of 97.33%. The analysis of MBTI dimensions reveals nuanced insights into the classifiers' ability to capture specific personality traits.
References
Abidin, N. H. Z., Remli, M. A., Mohd Ali, N., Eh Phon, D. N., Yusoff, N., Adli, H. K., & Busalim, A. H. (2020). Improving Intelligent Personality Prediction using Myers-Briggs Type Indicator and Random Forest Classifier. International Journal of Advanced Computer Science and Applications, 11(11), 192–199. https://doi.org/10.14569/IJACSA.2020.0111125
Amirhosseini, M. H., & Kazemian, H. (2020). Machine learning approach to personality type prediction based on the Myers–Briggs type indicator®. Multimodal Technologies and Interaction, 4(1). https://doi.org/10.3390/mti4010009
Basto, C. (2021). Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing. http://arxiv.org/abs/2105.11798
Choi, S. (2021). The Interdependency of the Diction and MBTI Personality Type of Online Users. American Journal of Applied Psychology, 10(1), 21. https://doi.org/10.11648/j.ajap.20211001.14
Dos Santos, V. G., & Paraboni, I. (2022). Myers-Briggs personality classification from social media text using pre-trained language models. Journal of Universal Computer Science, 28(4), 378–395. https://doi.org/10.3897/jucs.70941
Hegde, R., Kumar Hegde, S., Kotian, S., & Shetty, S. C. (2019). Personality classification using Data mining approach. IJRAR19H1202 International Journal of Research and Analytical Reviews (IJRAR) Www.Ijrar.Org, 354(1), 354–359. www.ijrar.org
Johnson, S. J., & Murty, M. R. (2023). Machine Learning Approach to Improve Data Connectivity in Text-based Personality Prediction using Multiple Data Sources Mapping. Journal of Scientific and Industrial Research, 82(1), 109–119. https://doi.org/10.56042/jsir.v82i1.70218
Justindhas, Y., Mohanraj, S. M., & Shivani, R. (2022). A Synoptic Survey on Personality Prediction System using MBTI. 3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022 - Proceedings, Icesc, 950–955. https://doi.org/10.1109/ICESC54411.2022.9885634
Keh, S. S., & Cheng, I.-T. (2019). Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models. http://arxiv.org/abs/1907.06333
Khan, A. S., Ahmad, H., Asghar, M. Z., Saddozai, F. K., Arif, A., & Khalid, H. A. (2020). Personality classification from online text using machine learning approach. International Journal of Advanced Computer Science and Applications, 11(3), 460–476. https://doi.org/10.14569/ijacsa.2020.0110358
Kovacevic, N., Holz, C., Gunther, T., Gross, M., & Wampfler, R. (2023). Personality Trait Recognition Based on Smartphone Typing Characteristics in the Wild. IEEE Transactions on Affective Computing, 14(4), 3207–3217. https://doi.org/10.1109/TAFFC.2023.3253202
N, P. K. K., Member, S., Gavrilova, M. L., & Member, S. (2022). Latent Personality Traits Assessment From Social Network Activity Using Contextual Language Embedding. IEEE Transactions on Computational Social Systems, 9(2), 638–649.
Naik, H., Dedhia, S., Dubbewar, A., Joshi, M., & Patil, V. (2022). Myers Briggs Type Indicator (MBTI) - Personality Prediction using Deep Learning. 2022 2nd Asian Conference on Innovation in Technology, ASIANCON 2022, Dl, 1–6. https://doi.org/10.1109/ASIANCON55314.2022.9909077
Ontoum, S., & Chan, J. H. (2022). Personality Type Based on Myers-Briggs Type Indicator with Text Posting Style by using Traditional and Deep Learning. http://arxiv.org/abs/2201.08717
Patel, S., Nimje, M., Shetty, A., & Kulkarni, S. (2020). Personality Analysis using Social Media. International Journal of Engineering Research & Technology (IJERT), 9(3), 306–309. www.ijert.org
Pessoa, M., Lima, M., Pires, F., Haydar, G., Melo, R., Rodrigues, L., Oliveira, D., Oliveira, E., Galvao, L., Gadelha, B., Isotani, S., Gasparini, I., & Conte, T. (2023). A Journey to Identify Classification Strategies to Customize Game-Based and Gamified Learning Environments. IEEE Transactions on Learning Technologies, 17, 527–541. https://doi.org/10.1109/TLT.2023.3317396
Praphulla, G. L., Kishore, I. B., Venkatesh, B., Praveen, B., & Rao, P. S. (2023). Myers Briggs Personality Personality Prediction Using Machine Learning Techniques. AIP Conference Proceedings, 2794(1). https://doi.org/10.1063/5.0174316
Qamar, N., & Malik, A. A. (2022). A Quantitative Assessment of the Impact of Homogeneity in Personality Traits on Software Quality and Team Productivity. IEEE Access, 10(November), 122092–122111. https://doi.org/10.1109/ACCESS.2022.3222845
Radisavljevic, D., Batalo, B., Rzepka, R., & Araki, K. (2022). Myers-Briggs Type Indicator and the Big Five Model-How Our Personality Affects Language Use. Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022. https://doi.org/10.1109/CSDE56538.2022.10089309
Ramachandran, V., Loya, A., Shah, K. P., Goyal, S., Hansoti, E. A., & Caruso, A. C. (2020). Myers-Briggs Type Indicator in Medical Education: A Narrative Review and Analysis. Health Professions Education, 6(1), 31–46. https://doi.org/10.1016/j.hpe.2019.03.002
Russo, D., & Stol, K. J. (2022). Gender Differences in Personality Traits of Software Engineers. IEEE Transactions on Software Engineering, 48(3), 819–834. https://doi.org/10.1109/TSE.2020.3003413
Ryan, G., Katarina, P., & Suhartono, D. (2023). MBTI Personality Prediction Using Machine Learning and SMOTE for Balancing Data Based on Statement Sentences. Information (Switzerland), 14(4). https://doi.org/10.3390/info14040217
Sahono, M. N., Sidiastahta, F. U., Shidik, G. F., Fanani, A. Z., Muljono, Nuraisha, S., & Lutfina, E. (2020). Extrovert and Introvert Classification based on Myers-Briggs Type Indicator(MBTI) using Support Vector Machine (SVM). Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, ISemantic 2020, 572–577. https://doi.org/10.1109/iSemantic50169.2020.9234288
Shafi, H., Sikander, A., & Jamal, I. M. (2021). A Machine Learning Approach for Personality Type Identification using MBTI Framework. Journal of Independent Studies and Research Computing, 19(2), 6–10. https://doi.org/10.31645/jisrc.43.19.2.2
Sharma, M., Joshi, S., Sharma, S., Singh, A., & Gupta, R. (2021). Data Mining Classification Techniques to Assign Individual Personality Type and Predict Job Profile. 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021, 1–5. https://doi.org/10.1109/ICRITO51393.2021.9596511
Sonmezoz, K., Ugur, O., & Diri, B. (2020). MBTI Personality Prediction with Machine Learning. 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. https://doi.org/10.1109/SIU49456.2020.9302239
Soonpipatskul, N., Pal, D., Watanapa, B., & Charoenkitkarn, N. (2023). Personality Perceptions of Conversational Agents: A Task-Based Analysis Using Thai as the Conversational Language. IEEE Access, 11(September), 94545–94562. https://doi.org/10.1109/ACCESS.2023.3311137
Talasbek, A., Serek, A., Zhaparov, M., Moo-Yoo, S., Kim, Y. K., & Jeong, G. H. (2020). Personality classification experiment by applying k-means clustering. International Journal of Emerging Technologies in Learning, 15(16), 162–177. https://doi.org/10.3991/ijet.v15i16.15049
Vásquez, R. L., & Ochoa-Luna, J. (2021). Transformer-based Approaches for Personality Detection using the MBTI Model. Proceedings - 2021 47th Latin American Computing Conference, CLEI 2021, 17–23. https://doi.org/10.1109/CLEI53233.2021.9640012
Vasundhara, D., Sowmya, K., Mahathi, M., Keerthi, N., Swapnika, T., & Professor, A. (2023). MBTI (Myers Briggs Type Indicator) Based Personality Classification. International Research Journal of Modernization in Engineering Technology and Science, 06, 4652–4657. https://www.doi.org/10.56726/IRJMETS42086
Wang, T., Ye, P., Lv, H., Gong, W., Lu, H., & Wang, F. Y. (2023). Modeling Digital Personality: A Fuzzy-Logic-Based Myers Briggs Type Indicator for Fine-Grained Analytics of Digital Human. IEEE Transactions on Computational Social Systems, 11(1), 1096–1107. https://doi.org/10.1109/TCSS.2023.3245127
Wang, Z., Wu, C., Zheng, K., Niu, X., & Wang, X. (2019). SMOTETomek-Based Resampling for Personality Recognition. IEEE Access, 7, 129678–129689. https://doi.org/10.1109/ACCESS.2019.2940061
Zhao, C., Wang, J., Feng, X., & Shen, H. (2020). Relationship Between Personality Types in MBTI and Dream Structure Variables. Frontiers in Psychology, 11(August), 1–8. https://doi.org/10.3389/fpsyg.2020.01589
Zhao, Y., Miao, D., & Cai, Z. (2022). Reading Personality Preferences From Motion Patterns in Computer Mouse Operations. IEEE Transactions on Affective Computing, 13(3), 1619–1636. https://doi.org/10.1109/TAFFC.2020.3023296
Zhou, Y., Shi, J., & Yu, Q. (2022). MBTI Personality Analysis and Prediction. Yutao-Zhou.Github.Io. https://yutao-zhou.github.io/CV/files/EECS6893
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