SkillSift: Automated Resume Classification and Skill Extraction Platform Using NLP
Author : V Harini and R Nithya
Abstract :
The rapid growth of job applications has made manual resume screening time-consuming and inefficient for recruiters. To address this challenge, SkillSift is proposed as an automated resume classification and skill extraction platform powered by Natural Language Processing (NLP). The system analyzes resumes submitted in digital formats and automatically identifies key information such as technical skills, educational background, work experience, and certifications. Using NLP techniques such as text preprocessing, tokenization, named entity recognition, and semantic analysis, the platform extracts relevant skill sets and categorizes resumes based on job roles or domains. SkillSift enhances the recruitment workflow by reducing manual effort and enabling faster candidate shortlisting. The system also improves accuracy in identifying candidate competencies by analyzing contextual information within resumes. Recruiters can efficiently filter and rank applicants based on required skills, improving the quality of hiring decisions. Additionally, the platform supports scalable resume processing, making it suitable for organizations handling large volumes of applications. Overall, SkillSift demonstrates how NLP-driven automation can modernize recruitment systems by transforming unstructured resume data into meaningful insights. This approach not only saves time for recruiters but also ensures a fair and consistent evaluation of candidates.
Keywords :
Natural Language Processing, Resume Classification, Skill Extraction, Automated Recruitment, Text Mining.
