AN INTERNSHIP CAMPAIGN CASE STUDY SHOWING RESULTS OF ENHANCED RECRUITMENT PROCESSES USING NLP

Abstract

The use of AI presents numerous benefits for numerous industry verticals. AI technology facilitates the analysis of unstructured data from heterogeneous sources. The added value from AI technologies relies on the insight gained over the data, helping in the automation of specific activities, or tasks, or enhancing the human factor in taking better decisions. According to recent market studies, business intelligence and analytics showed to be the essential area in which AI can deliver results. Natural Language Processing (NLP) is the AI subdomain which deals with human language and speech. NLP sits at the crossroads between a diverse number of disciplines, from linguistics to computer science and engineering, and of course, AI. Opinion mining (or sentiment analysis) is a natural language processing technique applied to determine whether data is positive, negative, or neutral. NLP can be the perfect solution to solve the inefficiencies in the traditional recruiting model for both recruiters and candidates when it comes to candidate screening and profiling. Our case study presents a recruiting platform (SoMeDi) for internship campaigns that applies Sentiment Analysis (SA) techniques to improve the hiring processes aiming to increase the efficiency of internship campaigns by ensuring a better match between the candidates' professional skills and the hiring company fields of activity. The SoMeDi performs text analytics (sentiment analysis) over the candidates' input data, once it is collected when they register for various internship applications. The paper presents the SoMeDi recruiting platform architecture and the SA microservice, together with the results achieved after validating the platform in a real-world scenario.