An empirical study on Natural Language Processing in identifying industry demands to recommend required qualifications in the UAE
Suseelan Unnithan, Kannan
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A skillful and competent workforce is one of the deciding factors of any countries economic and social growth. Developed and undeveloped countries have issues related to the availability of skilled laborers. Also, the existing workforce is struggling to upgrade their skills based on the fast-changing technologies and industry environments. These factors are boosting the unemployment rate of countries. The solution for this issue is Vocational Education and Training, which will equip the workforce with the right skills. The vocational qualifications are developed based on the industry requirements. Regularly identifying and updating the industry requirements is a manual process and is expensive and time-consuming. By extracting and processing the employment advertisements published on the web, one can analyze the skills demands in each industry and the organization’s operational effectiveness. This research oversees the application of web data extraction in the education sector. This study proves that by extracting the jobs published on the web and processing the same using the NLP technique, the industry's skill requirements can be identified and propose new qualifications to be developed.