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A novel approach to evaluate and rank candidates in a recruitment process by estimating emotional intelligence through social media data

In this work, we put forward a system that automates the eligibility check and aptitude evaluation of prospective candidates in a recruitment process. Implemented as a web application, the system lets employers post new job openings. Interested candidates could apply by filling an online resume and inputting their twitter handle. The system estimates their emotional aptitude by analyzing the tweets while professional eligibility is verified through the entries given in the online resume. Big Five Personality Model (also known as Five Factor Model) is used to predict the personality traits of the users and assess their emotional quotient. Machine learning techniques such as supervised classification is used to model the personality predictor. Meta-attributes from the tweets are considered for the process, while the actual contents are ignored, thus protecting the privacy of the users. Regression techniques are then employed to assign a compatibility score to each applicant. The system outputs a list of candidates ranked in the order of the compatibility score to the employer who posted the job offer.