Resume Analysis System Using Natural Language Processing
Premchand Pawade, Pranav Sukale, Shivprasad Swami , Ahefaz Khan Prof. Shivani Karhale
erprempawade@gmail.com, sukalepranav@gmail.com, swamishivprasad9495@gmail.com, Khanahefaz01@gmail.com
Abstract
This project develops a resume analysis system using Natural Language Processing (NLP) to streamline hiring. Companies often receive large volumes of resumes, making it challenging to identify the best candidates quickly. Manually screening resumes is time-consuming, can be inconsistent, and may overlook key details. By automating this process with NLP, our system reads and evaluates resumes efficiently. It extracts key information like skills, experience, and education to match candidates to job requirements. For example, if a job requires a specific skill, the system can highlight candidates with that qualification.
NLP algorithms can recognize relevant keywords, synonyms, and context, even if the phrasing varies across resumes. This means that someone who writes "managed team projects" or "project lead" can be identified as having similar experience. The system uses advanced NLP models, such as BERT or GPT, to capture subtle language details and improve accuracy. As it processes more resumes, the system "learns" and becomes better at identifying relevant skills and qualifications. Customization allows it to adapt to different industries by prioritizing specific keywords and competencies.
This adaptability makes the system valuable across fields like technology, finance, and healthcare. By focusing on qualifications objectively, it helps reduce bias in resume review. Recruiters save time and can focus on candidates who meet job requirements more closely. The system accelerates hiring, improves candidate-job matching, and supports data-driven decisions.
Keywords
Resume Analysis, Natural Language Processing (NLP), Recruitment Automation, Text Analysis, Skill Extraction, Information Retrieval, Automated Resume Parsing, Unstructured Data Processing.
