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Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example

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  • Lina Cao
  • Jian Zhang
  • Xinquan Ge
  • Jindong Chen

Abstract

The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the circumstances of big data, this paper showed the feasibility of vocational portraits driven by job advertisements with data analysis and processing engineering technicians (DAPET) as an example. First, according to the description of occupation in the Chinese Occupation Classification Grand Dictionary, a text similarity algorithm was used to preliminarily choose recruitment data with high similarity. Second, Convolutional Neural Networks for Sentence Classification (TextCNN) was used to further classify the preliminary corpus to obtain a precise occupational dataset. Third, the specialty and skill were taken as named entities that were automatically extracted by the named entity recognition technology. Finally, putting the extracted entities into the occupational dataset, the occupation characteristics of multiple dimensions were depicted to form a profile of the vocation.

Suggested Citation

  • Lina Cao & Jian Zhang & Xinquan Ge & Jindong Chen, 2021. "Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
  • Handle: RePEc:plo:pone00:0253308
    DOI: 10.1371/journal.pone.0253308
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    References listed on IDEAS

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    1. Panggih Kusuma Ningrum & Tatdow Pansombut & Attachai Ueranantasun, 2020. "Text mining of online job advertisements to identify direct discrimination during job hunting process: A case study in Indonesia," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-29, June.
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    Cited by:

    1. Fareri, Silvia & Apreda, Riccardo & Mulas, Valentina & Alonso, Ruben, 2023. "The worker profiler: Assessing the digital skill gaps for enhancing energy efficiency in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

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