Curriculum Vitae Sorting: A Novel Framework for Personality-based Automatic CV Sorting using Deep Learning

Both skill and personality play impactful roles in professional performance. The Human Resource Management (HRM) identifies and verifies the skill set and academic background while recruiting new employees. However, analyzing the personalities of the applicants is challenging. Because humans have intrinsic characteristics that allow them to express fabricated personalities in different settings. Nevertheless, people frequently express their true sentiments on social media. This paper presents an innovative and effective framework, the Curriculum Vitae Sorting (CVS) framework, that uses Bidirectional Long Short-Term Memory (BiLSTM) and the Myers-Briggs Type Indicator (MBTI) dataset to identify the personalities of job applicants using their social media posts. The CVS framework achieves a remarkable 92.88% classification accuracy with a 4.55% False Positive Rate (FPR). The practical application of this framework demonstrates an 11.67% improvement in the Key Performance Indicator (KPI) among newly recruited employees. The 0.9311% precision, 0.9294% recall, and 0.9403% F1-score of the CVS framework demonstrate its outstanding and reliable performance in personality classification. This unique application of Deep Learning (DL) in HRM unearths a new dimension of Artificial Intelligence (AI) in business, helping organizations recruit employees with the required personalities and qualities.