Can Natural Language Processing Enhance CV Screening? Development of tool to analyse and screen CVs

Employee recruitment is an important process in a company's human resource management. In many businesses, the majority of the recruitment process is still done manually. This manual process can be time-consuming and lead to the hiring of unsuitable candidates. This can result in the company losing time, money, and efficiency. This thesis aims to develop a tool to automatically analyse and screen the CVs of candidates at the Blue Reply company and extract useful information for recruitment. Through the use of the IBM Watson Natural Language Understanding, the CVs analysis was automated. For each CV, the most important keywords ,as well as the main concepts, were extracted, providing obtain useful information for recruiters. The developed tool proves to be useful as it allows the recruitment process of new employees to be optimized in terms of time and suitability of candidates. The proposed solution can save time, reduce human error, and ensure that qualified candidates are not missed, leading to a more efficient and effective recruitment process. The thesis uses a methodology that involves collecting and processing data, running algorithms, and analyzing the results and it is organized into several chapters covering the introduction, related work, proposed methodology, analysis of tools used and data description, results, conclusions, as well as further developments of the work.