Methodology for Job Advertisements Analysis in the Labor Market in Metropolitan Cities: The Case Study of the Capital of Russia

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Abstract

The article presents a methodology for analyzing perspective occupations in some region by means of job advertisements gathering and processing. We offer an approach to building ratings of popular specialties in the labor market in megacities. The object of the study is Moscow. The prerequisite for this study is the rapid development of labor markets in the capital cities, which must be monitored and analyzed due to some changes. The vacancies offered by employers form the professional qualification structure of demand in the labor market. Characteristics of vacancies (salaries, labor functions) are a guideline for regional labor markets. Also, the labor markets of large cities are distinguished by the large volume and diversity of the supply of work (demand for labor). Due to the large amount of information, to analyze all the labor offers on the market and summarize the results for an ordinary citizen seems to be a time-consuming task. Our approach illustrates the possibility of using programming technologies (the parsing and API of the sites of recruiting and recruitment agencies) for automatic collection of information about the proposed vacancies and their subsequent analysis. Using technologies of statistical data processing and Data Mining allows for filtering the source data, exclude emissions, group vacancies, evaluate key characteristics and build their private rankings. The obtained mass and job security ratings can be useful for career guidance agencies, as well as for schoolchildren, students and people planning retraining and retraining in adulthood, to assess the relevance and rational choice of a future profession #CSOC1120.

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APA

Pitukhin, E., Astafyeva, M., & Astafyeva, I. (2020). Methodology for Job Advertisements Analysis in the Labor Market in Metropolitan Cities: The Case Study of the Capital of Russia. In Advances in Intelligent Systems and Computing (Vol. 1224 AISC, pp. 413–429). Springer. https://doi.org/10.1007/978-3-030-51965-0_37

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