Enhancing Accuracy of MBTI Personality Prediction Using Deep Ensemble Models and Data Augmentation Techniques
Devraj Patel1, Sunita V Dhavale2, Bhushan B Mhetre3
1Devraj Patel, Department of Computer Science & Engineering, Defence Institute of Advanced Technology (DU), Girinagar, Pune (MH.), India.
2Dr. Sunita Vikrant Dhavale, Associate Professor, Department of Computer Science & Engineering, Defence Institute of Advanced Technology (DU), Girinagar, Pune (M.H.), India.
3Dr. Bhushan B. Mhetre, Department of Psychiatry, Smt. Kashibai Navale Medical College and General Hospital, Narhe, Pune (MH.), India.
Manuscript received on 30 October 2025 | First Revised Manuscript received on 14 November 2025 | Second Revised Manuscript received on 17 December 2025 | Manuscript Accepted on 15 January 2026 | Manuscript published on 30 January 2026. | PP: 8-18 | Volume-14 Issue-5, January 2026 | Retrieval Number: 100.1/ijrte.D831514041125 | DOI: 10.35940/ijrte.D8315.14050126
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Personality traits prediction from text has broad applications in various fields such as recruitment, job performance analysis, adaptive learning and personalised systems. Although traditional psychological assessments are widely used today, they may be subjective and impractical for large-scale deployment because they require the physical presence of a psychologist. This study presents an automated personality prediction model utilising text data. To address class imbalance, a significant factor that degrades model performance on the personality text dataset, a two-tier oversampling strategy has been implemented. The primary contribution of this study is to systematically evaluate the efficacy of various Deep Learning Architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTMs, for MBTI prediction. Additionally, we have explored various ensemble learning approaches by combining separable CNNs, LeNet-5, and LSTM and BiLSTM models, thereby further improving prediction accuracy and generalisation. The experimental results show that integrating the proposed oversampling technique ensemble with the ensemble learning framework achieves higher accuracy, exceeding 87%, and outperforms previous models based solely on a single architecture or machine learning methods. The proposed method enables large-scale personality assessments to be deployed anywhere, at any time, reducing the need for the physical presence of psychologists.
Keywords: Deep Learning Ensemble, MBTI Classification, Oversampling Strategy, Personality Prediction, Text-based Assessment.
Scope of the Article: Computer Engineering