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Research Article | Open Access |

Automated Analysis of Job Market Demands using Large Language Model

Author 1: Myo Thida
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 14, No. 8 · Published 2023

DOI: https://doi.org/10.14569/IJACSA.2023.01408103

Abstract

This paper presents a comprehensive analysis of labor market demands for Myanmar workers in Japan, and Thailand, focusing on opportunities for individuals without higher education degrees. Leveraging ChatGPT’s text classification and summarization capabilities, we extracted vital insights from extensive job advertisements and social media groups. The dataset comprises 152 job advertisements from Thailand and 30 from Japan, collected in 2023. Our research provides a valuable snapshot of skill demands and job opportunities, offering insights for informed decision-making by both job seekers and international non-governmental organizations. The innovative approach of using ChatGPT highlights its efficacy in understanding labor market dynamics. These findings serve as a foundation for tailored interventions to bridge employment challenges faced by marginalized Myanmar youths.

Keywords

How to Cite this Article

Thida, M. (2023). Automated Analysis of Job Market Demands using Large Language Model. International Journal of Advanced Computer Science and Applications, 14(8). https://doi.org/10.14569/IJACSA.2023.01408103

Thida, Myo. "Automated Analysis of Job Market Demands using Large Language Model." International Journal of Advanced Computer Science and Applications, vol. 14, no. 8, 2023, https://doi.org/10.14569/IJACSA.2023.01408103.

@article{Thida2023,
  title     = {Automated Analysis of Job Market Demands using Large Language Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Myo Thida},
  doi       = {10.14569/IJACSA.2023.01408103},
  url       = {https://doi.org/10.14569/IJACSA.2023.01408103}
}

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