Intelligent Management of Human Capital

Intelligent Management of Human Capital

Modeling and Psychological Analysis of Job Applicants Using Natural Language Processing and Facial Analysis

Document Type : Original Article

Authors
1 Department of Computer Engineering, Shamsipour Technical and Vocational College, Tehran, Iran.
2 Department of Computer Engineering, Shamsipour Technical and Vocational College, Tehran, Iran
Abstract
Background and Objective: Traditional hiring processes are faced with challenges such as human biases and a lack of precision. With the aim of increasing accuracy and fairness in selection, this research introduces an intelligent multimodal system that assesses applicants' personalities by simultaneously analyzing verbal and non-verbal signals based on the six-factor psychological model.
Methodology: With its multi-stage architecture, the system first analyzes verbal content using advanced speech-to-text models. Concurrently, it extracts facial states and movements by leveraging facial analysis libraries and the Facial Action Coding System. Finally, a deep learning model with a cross-modal attention mechanism combines these two facets of information to predict personality traits.
Findings: Evaluations showed that the multimodal model performs with significantly more accuracy than unimodal models. The system's validity was confirmed by comparing its results with the evaluations of expert psychologists. Additionally, an interpretable analytical dashboard was designed to provide evidence (quotes and clips) to human resources managers.
Conclusion: This research provides an innovative, precise, and evidence-based framework for psychological analysis within the hiring process. By relying on the six-factor psychological model and a multimodal architecture, this system has the potential to transform traditional selection processes towards fairer, more objective, and more efficient procedures.
Keywords
Subjects

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