The rapid growth of job applications in modern recruitment has made manual resume screening a time-consuming and inefficient process for human resource professionals. To address this challenge, the proposed system, Automated Resume Screening System Using Natural Language Processing (NLP), aims to streamline and improve the candidate selection process through intelligent automation. The system analyzes and evaluates resumes by extracting relevant information such as skills, qualifications, experience, and keywords related to job requirements. Using Natural Language Processing techniques and machine learning algorithms, the system processes unstructured resume data and converts it into structured information that can be easily compared with job descriptions. The model then ranks and filters candidates based on their relevance to the specified job profile, helping recruiters quickly identify the most suitable applicants. This approach reduces manual effort, minimizes human bias, and significantly improves the efficiency and accuracy of recruitment processes. The system typically involves stages such as resume data collection, text preprocessing, feature extraction, skill matching, and candidate ranking. By applying techniques such as tokenization, stop-word removal, and vectorization, the system understands the semantic meaning of resume content and evaluates the compatibility between candidate profiles and job requirements. The proposed solution can be integrated into recruitment platforms to assist organizations in handling large volumes of applications while ensuring fair and objective candidate evaluation. Overall, the system demonstrates how artificial intelligence and natural language processing can transform traditional hiring practices into a faster, more reliable, and data-driven process, ultimately helping organizations select qualified candidates efficiently.
JSETMS (Sat,) studied this question.