Resume content scoring and improvement suggestions using nlp and rule-based techniques

Abstract

Having a proper resume is very important for undergraduates or fresh graduates to find their dream job. But most of them find it difficult to prepare their resume properly by themselves. It often needs a third party to review the resume to identify missing parts and content improvements of the resume because most of the time candidates make some mistakes. When it comes to resume review systems, most of the systems are based on the recruiter perspective which does not provide any insights for the candidate to improve their resumes. Hence, it is helpful if a proper resume content reviewer is there for candidates to analyze their resumes. This study is focused on developing a model to resume content scoring and suggest missing content based on NLP and rule-based techniques. Two separate approaches were developed and tested for the proposed system and then the comparison of those approaches were carried out through this study.