SkillNER: Mining and mapping soft skills from any text. (1st December 2021)

• Record Type: Journal Article
• Authors: Fareri, Silvia; Melluso, Nicola; Chiarello, Filippo; Fantoni, Gualtiero
• Is Part Of: Expert systems with applications. Volume 184(2021)
• Publication Date: 2021-12-01
• Subjects: Soft Skill -- Skill Analysis -- Machine Learning -- Text Mining -- Named Entity Recognition; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115544
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A Text Mining Tool was developed to automatically extract soft skills from any sources. As an application of the results, a Cluster Analysis was performed on ESCO. We represent the relations among Job Profiles and among Soft Skills as Graphs. The Tool and the Graphs could bring multiple benefits to Firms, Institutions and Workers. The System is designed to be Cost-Effective and Easily Adaptable to other contexts. Abstract: In today's digital world, there is an increasing focus on soft skills. On the one hand, they facilitate innovation at companies, but on the other, they are unlikely to be automated soon. Researchers struggle with accurately approaching quantitatively the study of soft skills due to the lack of data-driven methods to retrieve them. This limits the possibility for psychologists and HR managers to understand the relation between humans and digitalisation. This paper presents SkillNER, a novel data-driven method for automatically extracting soft skills from text. It is a named entity recognition (NER) system trained with a support vector machine (SVM) on a corpus of more than 5000 scientific papers. We developed this system by measuring the performance of our approach against different training models and validating the results together with a team of psychologists. Finally, SkillNER was tested in a real-world case study using the job descriptions of ESCO (European Skill/Competence Qualification and Occupation) as textual source. The system enabled the detection of communities of job profiles based on their shared soft skills and communities of soft skills based on their shared job profiles. This case study demonstrates that the tool can automatically retrieve soft skills from a large corpus in an efficient way, proving useful for firms, institutions, and workers. The tool is open and available online to foster quantitative methods for the study of soft skills.