JOB PORTAL RESUME EVALUATON SYSTEM USING TEXT MINING AND NATURAL LANGUAGE PROCESSING
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 4, Pages 2800-2807
AbstractIn this competitive era, getting right education and right job is always a challenge. Organization who are in need of skilled people in certain departments also find it difficult to identify right candidate with good talented skill set. In the proposed system, we propose 2 logins namely job seeker and employer. The proposed system tries to make the recruitment process simpler and efficient by integrating text mining and natural language processing techniques. The proposed architecture consists of unique and essential features like study materials, de-duplication process, resume analysis and weightage analysis. The employer can upload study materials while posting the job requirement so that the job seeker will have a fair knowledge of the exact job role. The recommendation of exact profile based on the skill required is processed using collaborative filtering algorithm. To optimize the cloud storage we have integrated de-duplication technique to eliminate saving same resume n number of times which would increase encryption cost and storage. The de-duplicaton process is performed using Proactive Replica Checking approach (PRCR). Also applying natural processing techniques in both job seeker and employer side provides efficient results saving much of time. In the employer side, we use web crawler to extract job description and requirements. In the job seeker side, once the resume is posted, stop word filtering and text segmentation is performed. After text segmentation, the scoring is provide based on the education, work experience, skills, personality traits and frequency of degree. Finally our proposed system provides a recommendation system for the upcoming generation in which degree of education major job requirements are coming.
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