Resume Analysis Using NLP and ATS Algorithm

Abstract

In today’s highly competitive job market, organizations receive thousands of resumes for a single job opening, making manual screening time-consuming, inconsistent, and prone to bias. Applicant Tracking Systems (ATS) have become a critical component in modern recruitment pipelines, enabling automated filtering of resumes based on predefined criteria. However, traditional ATS solutions often rely on keyword matching, which may overlook qualified candidates whose resumes are structured differently or use varied terminology. This project proposes a Resume Analyzer using Natural Language Processing (NLP) integrated with an ATS scoring mechanism to enhance resume screening accuracy, efficiency, and fairness. The system automatically parses resumes, extracts relevant information such as skills, experience, education, and certifications, and compares them with job descriptions to generate a comprehensive ATS compatibility score. NLP techniques such as tokenization, lemmatization, named entity recognition, and semantic similarity analysis are employed to understand the contextual meaning of resume content rather than relying solely on exact keyword matches. The proposed system aims to assist recruiters by reducing manual effort, minimizing unconscious bias, and ensuring consistent evaluation standards across applicants. Additionally, job seekers can use the system to optimize their resumes by identifying gaps and improving alignment with job requirements. By leveraging NLP and machine learning techniques, the resume analyzer provides an intelligent, scalable, and adaptable solution that improves recruitment decision-making while maintaining transparency and interpretability. The system demonstrates how AI-driven text analytics can transform traditional hiring processes into datadriven, efficient, and objective workflows.