International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 04 | Apr 2025
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
NLP-BASED RESUME ANALYSIS, SKILL ENHANCEMENT & JOB AUTOMATION G. Krishna Lohith¹, K. Varshith Reddy², P. Mounisha Reddy³, Viyyapu Lokeshwari Vinya⁴ ¹²³UG student, Dept. of Computer Science and Engineering, Vardhaman College of Engineering, Telangana, India ⁴Assistant Professor, Dept. of Computer Science and Engineering, Vardhaman College of Engineering, Telangana, India *** Abstract- NLP-Based Resume Analysis, Skill Enhancement & recruitment platforms utilize machine learning and Job Automation uses NLP to extract the text and is analyzed to Natural Language Processing (NLP) techniques for help users provide insights. It suggests users some skills to work efficient resume parsing and matching. By categorizing on and some course recommendations which helps him level up. resumes and job postings, these platforms reduce time Through resume matching with job descriptions, NLP is able complexities and improve accuracy using methods like to detect skill gaps and suggest tailored training programs to Content-based Recommendation, cosine similarity, and improve candidates’ qualifications and make them more KNN. However, effectiveness is not an issue as time hireable. In this paper, we examine a few contemporary methods for screening automated resumes. To increase the consumption is the main issue. One proposed solution precision and effectiveness of the screening process, these involves segmenting resumes based on sections and approaches employ a variety of methods including hybrid deep employing NLP for data extraction, improving efficiency. learning frameworks, transfer learning, genetic algorithms, and multisource data. Also, some research investigates the use of job descriptions to improve resume screening precision. This technology helps recruiters reduce the workload, it helps simplify the recruiters work and find the person that matches their criteria. NLP does more than just analyzing resumes. It helps the job automation with real-time suggestions and resume optimization assistance along with candidate matching to suitable job positions. AI-powered chatbots and virtual assistants can suggest resume improvement, suggest certificates, and help candidates in applying.
Resume Parser and Analyzer tools further streamline the process by structuring unstructured resumes, extracting essential fields, and suggesting improvements. This not only saves recruiters time but also provides applicants with insights into their resume’s standing and areas for enhancement. Moreover, by allowing only the recruiter to access matched results, confidentiality is maintained, and the most qualified candidates are efficiently identified. This intelligent-based approach aims to optimize the recruitment process, benefiting both recruiters and job seekers alike.
Keywords: Natural language processing, Real-Time Optimization, Personalized Upskilling, Virtual Career Assistants, Resume Recommendation, Skill Gap Analysis, Job Matching Algorithms, Resume Parsing Techniques, Recruitment Process Automation, Virtual Career Assistants
2. RELATED WORKS
1. INTRODUCTION
Kondapalli Sai Pranay wrote “Resume Screening using Natural Language Processing and Machine Learning” which appeared in International Journal of Current Technology and Engineering during 2020. The paper demonstrates an approach which utilizes NLP alongside machine learning to perform resume screening operations and job description matching.
In today’s rapidly evolving tech driven world, traditional methods are no longer followed. However, the current system often requires candidates to manually enter all resume details, leading to no match in between job requirements and candidate skills. Generally with thousands of resumes per job posting, manual analysis is not possible for recruiters, leading to no satisfaction among candidates and facing challenges in finding the right job. To address this, innovative
The 2019 publication by Shweta Agrawal and Sumit Gupta entitled “Automated Resume Screening System Using Machine Learning and Natural Language Processing” introduced an evaluation system for resumes based on their job requirement fit through the combination of these technologies within the International Journal of Innovative Technology and Exploratory Engineering. Aditi Kaushik and Shruti Jain released “A Comprehensive Analysis of Resume Screening Techniques” in the International Journal of Computer Science and Mobile Computing during
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 1768