Resume Analysis System Using Natural Language Processing
Prof. Shivani Karhale.
shivanikarhale12@gmail.com
Premchand Pawade, Pranav Sukale, Shivprasad Swami , Ahefaz Khan
erprempawade@gmail.com, sukalepranav@gmail.com, swamishivprasad9495@gmail.com, Khanahefaz01@gmail.com
Abstract :
In the era of digital recruitment, organizations face the challenge of processing thousands of resumes efficiently and accurately. Manual resume screening is not only time-consuming but also prone to human error and bias. To address these issues, this project presents a Resume Analysis System using Natural Language Processing (NLP), which automates the process of analyzing and filtering resumes based on job requirements.
The system leverages advanced NLP techniques such as tokenization, part-of-speech tagging, named entity recognition, and TF-IDF (Term Frequency-Inverse Document Frequency) to extract relevant information like skills, education, experience, and certifications from unstructured resume text. It then matches these extracted features against predefined job descriptions using similarity measures and machine learning models, helping recruiters identify the most suitable candidates.
This solution improves the efficiency and fairness of the hiring process by reducing manual effort, ensuring consistent evaluation, and speeding up candidate shortlisting. The system can be integrated into existing recruitment platforms to enhance their capability in intelligent resume parsing and analysis.The system utilizes a range of NLP techniques including tokenization, part-of-speech tagging, named entity recognition (NER), and TF-IDF (Term Frequency-Inverse Document Frequency) to process unstructured resume content and extract meaningful information such as skills, education, experience, and certifications. It then compares these extracted features against job requirements to calculate a matching score and rank candidates accordingly.
Keywords :
Resume Analysis, Natural Language Processing (NLP), Text Mining, Resume Parsing, Candidate Matching, Job Description Matching, TF-IDF, Machine Learning, Named Entity Recognition (NER), Recruitment Automation, Skill Extraction, Document Classification, Information Retrieval.
