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<p>Personality significantly influences our attitudes/reactions/choices towards various situations and social interactions, affecting task suitability and team compatibility. Personality trait identification and analysis are vital in personal development, job selection, and performance enhancement. The Myers-Briggs Type Indicator (MBTI) is a widely used framework in social computing and human–computer interaction for understanding individual personality traits. Traditionally, MBTI assessments relied on psychologists, thus introducing subjectivity and bias. After post-pandemic, with increased online interactions using social media platforms, identifying traits without direct intervention through natural interactions is becoming inevitable before formal testing. Recently, deep learning algorithms have shown promise in developing such automated techniques for Personality Recognition; however, they face challenges with existing imbalanced data. We propose an efficient oversampling strategy using the transformer model GPT-2 and Synthetic Minority Oversampling Technique (SMOTE) to address imbalanced data problems with existing MBTI datasets. We introduce a novel lightweight CNN architecture called Dilated Separable Residual Network (DSRNet) that employs depth-wise separable convolutions to minimize computational cost while achieving an average accuracy of 90.05%, with a macro-averaged F1-score of 0.9003 across the four MBTI dimensions, demonstrating balanced performance on imbalanced personality classes.</p>

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Dilated Separable Residual Network (DSRNet): Lightweight model for Personality Recognition using LLM Augmented Text Data

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Devraj Patel,
  • Sunita V. Dhavale,
  • Bhushan B. Mhetre

摘要

Personality significantly influences our attitudes/reactions/choices towards various situations and social interactions, affecting task suitability and team compatibility. Personality trait identification and analysis are vital in personal development, job selection, and performance enhancement. The Myers-Briggs Type Indicator (MBTI) is a widely used framework in social computing and human–computer interaction for understanding individual personality traits. Traditionally, MBTI assessments relied on psychologists, thus introducing subjectivity and bias. After post-pandemic, with increased online interactions using social media platforms, identifying traits without direct intervention through natural interactions is becoming inevitable before formal testing. Recently, deep learning algorithms have shown promise in developing such automated techniques for Personality Recognition; however, they face challenges with existing imbalanced data. We propose an efficient oversampling strategy using the transformer model GPT-2 and Synthetic Minority Oversampling Technique (SMOTE) to address imbalanced data problems with existing MBTI datasets. We introduce a novel lightweight CNN architecture called Dilated Separable Residual Network (DSRNet) that employs depth-wise separable convolutions to minimize computational cost while achieving an average accuracy of 90.05%, with a macro-averaged F1-score of 0.9003 across the four MBTI dimensions, demonstrating balanced performance on imbalanced personality classes.