Intelligent text analysis using natural language processing for competency-based recruitment

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The main focus of the project is the development of a text classification and natural language processing**framework for an innovative recruiting software platform developed by SkyHive Technologies Inc. (partner**company). This will involve machine-based analysis of text to automatically identify competencies from job**applications according to extracted keywords and the context of surrounding words. The partner company has**developed a skills-based platform to increase the efficiency and reliability of the recruiting process. This**platform can be used by workers and employers to find the best matches according to job advertisements and**candidate skills. It processes advertisements and decomposes them into the required skills and categorizes**candidate profiles according to their skills. Machine learning is then performed to provide matching results.**This allows candidates to identify opportunities in near real-time as opposed to days, weeks or even months**with conventional approaches, and makes the hiring process rewarding for platform users as it is fast and**cost-effective. The platform also recommends training programs to workers according to targeted skill**development. Although employers can use the platform to identify candidates based on skill matching, finding**competency-matching results is unmanageable due to the complexity of tasks that must be performed on the**applications. This reduces the platform utility, and negatively impacts the matching rate. This project will**enable the platform to provide competency-based recommendations to employers based on text classification**matches between job descriptions and user profiles created on the system. For example, an employer who seeks**core competencies such as relationships, innovation, leadership, risk-taking, and decision-making will**automatically be notified of all active related applicants. Further, the salary levels for particular positions will**be determined and matched with the salary expectations of candidates. This project will greatly improve the**quality of the skills-based recruitment and increase user engagement within the platform.