An AI-Based Resume Analyzer Using Natural Language Processing

Abstract

The recruitment process in modern organizations has become increasingly complex due to the ever-growing number of applications received for each job opening. Traditional manual resume screening methods are time-consuming, prone to human error, and often influenced by unconscious biases. These limitations have created a need for more efficient and unbiased systems. To overcome these challenges, we propose an AI-based Resume Analyzer leveraging Natural Language Processing (NLP) techniques to automate and optimize the resume screening process. This tool extracts structured information such as educational qualifications, technical and soft skills, and work experience from unstructured resume documents and compares them against job descriptions using advanced NLP approaches like TF-IDF and cosine similarity.

The system identifies relevant and missing keywords from resumes, categorizes them into actionable suggestions, and provides a real-time matching score. Unlike conventional Applicant Tracking Systems (ATS) that rely solely on keyword matching, our tool uses semantic analysis for a deeper understanding, resulting in improved relevance and fairness in candidate shortlisting. This lightweight, web-based application requires no pre-trained dataset, making it accessible and scalable for small to mid-sized enterprises and individual users.

It also includes a logging mechanism for maintaining historical data for multiple resume evaluations. The results of experimental evaluations indicate a high level of accuracy and usability for both job seekers and HR professionals. Overall, the proposed system not only accelerates the recruitment process but also enhances decision-making quality and candidate experience. By integrating artificial intelligence with resume evaluation, the platform ensures a more intelligent, transparent, and equitable hiring process.