Abstract
The fourth industrial revolution emerges from a demanding need for reskilling and upskilling every active working person. Furthermore, the European Commission included key policy instruments for resilience, social fairness, and sustainable competitiveness in the European Skills Agenda. Distance training and education programs are key factors to succeed in the targets mentioned above. Due to the o COVID-19 pandemic, already 30% of the total education in European countries has further expanded. As a result, online evaluation approaches are more than necessary. Various methodologies have been applied to evaluate the online training sessions, from traditional statistics to context analysis and, the newly introduced text mining and sentiment analysis. This work used conventional descriptive statistical methods and advanced text mining methods to analyse data collected by private sector online training seminars—a total of 50 trainees in 5 seminars conducted by the private sector during COVID-19 pandemic training activities. A typical text mining analysis performed on a low is some open questions and a small number of texts.
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