AI-Powered Resume Analyzer with Intelligent Job Recommendation and Skill Gap Analysis: A Survey of NLP and Machine Learning Approaches

Abstract: The contemporary employment ecosystem presents a paradox wherein a large proportion of competent candidates fail to advance past automated screening stages, not due to skill deficiency but due to inadequate resume formulation. Applicant Tracking Systems (ATS) employed by recruiters systematically eliminate resumes that do not conform to keyword density thresholds and structural conventions, creating an opaque barrier for job seekers lacking professional guidance. This paper surveys six representative studies spanning 2021–2026 on NLP-based resume analysis, machine learning-driven job role prediction, semantic candidate-job matching, and automated skill gap identification. Each study is evaluated across methodology, algorithmic approach, dataset characteristics, and core limitations. Based on this survey, we propose a unified AI-Powered Resume Analyzer that integrates a multi-stage NLP extraction pipeline, a Scikit-learn classification model for job role prediction, a structured skill-gap benchmarking module, and a Flask-based web interface. The proposed system is designed to provide candidates with quantitative ATS compatibility scores, predicted suitable job roles, identified competency gaps, and prioritized improvement recommendations in a single candidate-facing platform—addressing critical shortcomings in existing recruiter-centric approaches.

Keywords: Natural Language Processing, Resume Analysis, Applicant Tracking System, Skill Gap Analysis, Job Role Prediction, Machine Learning, SpaCy, NLTK, Flask, TF-IDF, BERT, Scikit-learn, Career Guidance, Named Entity Recognition