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Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering

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  • 1School of Economics and Management, Beijing Information Science and Technology University, Beijing, China.

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This study demonstrates how job advertisements can create dynamic occupational profiles, overcoming limitations of traditional surveys. This data-driven approach offers a more efficient and up-to-date method for vocational analysis.

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Area of Science:

  • Data Science
  • Computational Linguistics
  • Occupational Science

Background:

  • Traditional occupational profiling relies on surveys, which are slow to update and labor-intensive.
  • There is a need for dynamic occupational information to improve existing profiling systems.

Purpose of the Study:

  • To demonstrate the feasibility of creating vocational portraits using job advertisements.
  • To present a data-driven methodology for occupational profiling in the big data era.

Main Methods:

  • Utilized a text similarity algorithm to filter relevant job advertisement data based on occupation descriptions.
  • Employed Convolutional Neural Networks for Sentence Classification (TextCNN) for precise corpus classification.
  • Applied Named Entity Recognition (NER) to extract key specialties and skills.

Main Results:

  • Successfully generated a precise occupational dataset from job advertisements.
  • Extracted and integrated specialties and skills as named entities.
  • Depicted multi-dimensional occupational characteristics to form detailed vocational profiles.

Conclusions:

  • Job advertisement data, when analyzed with advanced algorithms, provides a feasible and dynamic alternative to traditional occupational profiling.
  • This methodology enhances the accuracy and timeliness of vocational portraits.