Abstract
Digital revolution, globalization and the re-organisation of production and service companies have heavily modified the demand for skills and specific occupations. Here, the problem of analyzing web job vacancies is attracting increasing research interest from public organizations, employment agencies, and private companies, as it could reveal how web labour market demand is evolving with time, by focusing on the skills required of applicants by employers. A study conducted on the Italian web labour market focused on three distinct professional areas, namely, Communications, Sales, and Administration & Finance.
Highlights
In the past few decades, significant forces and factors have dramatically changed the nature and characteristics of the labour market in both advanced and developing countries
The analysis of the web labour market allows an understanding of labour market dynamics by identifying trends, skills and occupations according to real market expectations
This paper focuses on how the knowledge base synthesized from big data can enable analysts and labour market specialists to analyse and understand the dynamics of the web labour market, by letting the data speak through a data driven approach
Summary
In the past few decades, significant forces and factors have dramatically changed the nature and characteristics of the labour market in both advanced and developing countries. The analysis of web job vacancies allows labour market analysts and specialists to make sense of the European labour market dynamics and trends, by performing text classification via machine-learning to classify web job vacancies in a standard well-established taxonomy This taxonomy acts like a lingua franca, enabling the observation and comparison of different web labour markets across national borders by overcoming the linguistic boundaries. For this reason, we classify job vacancies with respect to the European Skills, Competences, Qualifications and Occupations (ESCO)i taxonomy, performing text classification based on machine learning techniques. The analysis of web job vacancies reduces the ‘time-to-market’ of the analyses in comparison with that of classical survey-based analyses, facilitating prompt policy evaluation and decision tasks
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