Resume Analyzer Using Natural Language Processing (NLP)
TF-IDF, Cosine Similarity NLP techniques were employed by the authors to match strings in the suggested system, and output results are obtained instantly in real time.
Abstract
Finding the best candidates for a position and informing users of their resume score and areas for growth are the two main objectives of Resume Screening After a thorough analysis of the literature on current methods, it was found that while traditional systems like manual screening may lead to incorrect assumptions and the wastage of human potential, they are not robust in terms of processing, accuracy, or efficiency. Software must score the resumes of the candidates in real-time and match and rate them using machine learning and natural language processing techniques in order to obtain accuracy. Applications’ resumes would be the input, and suggestions from the user side and an admin-side rated candidate resume list would be the output. By utilizing natural language processing techniques, output results are obtained instantly in real time. TF-IDF, Cosine Similarity NLP techniques were employed by the authors to match strings in the suggested system. It could be used in administrative agencies, government agencies, and multinational corporations where a large number of resumes need to be checked every day for multiple positions.