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@article{189071,
        author = {Abhay Bhadauria and Divyansh Mishra and Harshit Bansal},
        title = {Personality Prediction System through CV Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4819-4824},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189071},
        abstract = {In today’s competitive and rapidly evolving job market, recruiters and organizations face significant challenges in identifying candidates whose personalities align with desired work environments, roles, and team dynamics. Traditional hiring practices—including psychometric tests, interviews, and manual resume screenings—are labor-intensive and susceptible to human bias, inconsistency, and inefficiency. This has driven the need for intelligent, automated tools capable of evaluating both professional qualifications and psychological profiles at scale. The CV-based personality prediction website represents a major innovation in talent analytics by harnessing advances in machine learning, natural language processing (NLP), and data-driven psychological assessment. The central idea is to analyze resumes (CVs) submitted by candidates, extracting textual features and linguistic patterns to infer core personality traits such as Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism—collectively known as the Big Five.
The system’s architecture involves several crucial components: first, robust resume parsing modules process heterogeneous document formats and employ NLP algorithms to extract demographic information, educational background, professional experience, skillsets, and self-descriptive statements. Next, feature engineering methods identify words, phrases, and textual styles predictive of psychological traits, using psycholinguistic theories and computational models as guides. These features are mapped to personality indicators, which form the input to machine learning models trained on labeled datasets where CVs are paired with previously assessed personalities. Common techniques include supervised algorithms such as random forests, support vector machines, and logistic regression, as well as more advanced deep learning frameworks such as neural networks and transformer architectures (e.g., BERT and XLNet).},
        keywords = {},
        month = {December},
        }