Data mining approach to monitoring the requirements of the job market: A case study

Ioannis Karakatsanis, Wala AlKhader, Frank MacCrory, Armin Alibasic, Mohammad Atif Omar, Zeyar Aung, Wei Lee Woon

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

In challenging economic times, the ability to monitor trends and shifts in the job market would be hugely valuable to job-seekers, employers, policy makers and investors. To analyze the job market, researchers are increasingly turning to data science and related techniques which are able to extract underlying patterns from large collections of data. One database which is of particular relevance in the presence context is ONET, which is one of the most comprehensive publicly accessible databases of occupational requirements for skills, abilities and knowledge. However, by itself the information in ONET is not enough to characterize the distribution of occupations required in a given market or region. In this paper, we suggest a data mining based approach for identifying the most in-demand occupations in the modern job market. To achieve this, a Latent Semantic Indexing (LSI) model was developed that is capable of matching job advertisement extracted from the Web with occupation description data in the ONET database. The findings of this study demonstrate the general usefulness and applicability of the proposed method for highlighting job trends in different industries and geographical areas, identifying occupational clusters, studying the changes in jobs context over time and for various other research embodiments.

Original languageBritish English
Pages (from-to)1-6
Number of pages6
JournalInformation Systems
Volume65
DOIs
StatePublished - 1 Apr 2017

Keywords

  • Job market analysis
  • Latent semantic indexing
  • Text-mining
  • Web data extraction

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