CV SORTER: A TEXT MINING BASED APPROACH TO SELECT A CV

Abstract

Human resource department of organizations receive infinite CVs on regular basis, making it more complex for the HR person to find the best job candidates. In fact, an employer receives an average of 144 CVs per job opening. Usually different people follow the different style therefore required information about the candidates is sometimes missed or takes time to search for. It is usually observed that many people add lot ofunnecessary details in their CV. Upon visiting the market, it has been noticed that industries heavily depends on evaluation tools to select the most suitable applicant for the job. In fact, many companies are turning to systems that saves time and rapidly finds the best candidates. HR process of finding the best candidates is challenging because ofboth sequence ofidentifying, and quality ofdetermining the candidates. Purpose ofthis project is to provide an efficient CV extraction tool using text mining algorithm (K-Nearest Neighbours), and also using the features of Natural Language Processing (NLP).The main concept ofthis research based project is to create a best way to choose the appropriate CV accurately and in speedy process by extracting the best matching resumes. Which will allow HR department to choose the right candidate with minimal standard process.