The rapid growth of online recruitment platforms has increased the number of job applications received by organizations. Manually reviewing resumes is often slow, labor-intensive, and may lead to biased hiring decisions. To address these challenges, this research presents a Resume Screening Application based on Natural Language Processing (NLP) and Machine Learning (ML). The proposed system automatically extracts important details from resumes and job descriptions, including technical skills, education, certifications, work experience, and soft skills. The application uses NLP techniques for text preprocessing and feature extraction, while machine learning algorithms such as Support Vector Classifier (SVC) and Random Forest are applied for resume classification and candidate-job matching. Semantic similarity methods are also used to compare resumes with job descriptions and identify the most suitable applicants for a particular role. In addition, the system provides resume analysis and personalized feedback by identifying missing skills, skill gaps, and improvement areas. It supports multiple resume formats and helps reduce recruitment time, manual screening effort, and hiring bias while improving the overall accuracy and fairness of candidate selection. Experimental results demonstrate that the NLP-based screening approach can improve recruitment efficiency and support intelligent hiring decisions.
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Conclusion
The Resume Screening Application developed using Natural Language Processing and Machine Learning techniques provides an effective solution for modern recruitment challenges. The system automates resume analysis by extracting candidate details such as skills, education, certifications, and work experience, and comparing them with job requirements. The application helps reduce manual screening effort, saves recruitment time, and improves fairness in candidate selection by minimizing human bias. It also supports candidates by providing recommendations related to missing skills, resume enhancement, and career development. Overall, the proposed system acts as an intelligent recruitment tool that improves hiring accuracy, efficiency, and reliability. Future improvements such as advanced machine learning models, multilingual support, and deeper semantic analysis can further enhance the performance of the system and make it more suitable for real-world recruitment environments.
References
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How to Cite This Paper
Shruti V. Nasale, Prof. Dr. R. A. Taley Mam (2026). Privacy-Resume Screening Application Using NLP “Simplifying the Shortlisting Process”. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
