  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.
