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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60843

A Data-Driven Study on Emerging Skill Demand in Online Job Portals using NLP and Time-Series Forecasting

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-

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Paper ID:

IJSREM60843

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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RK

Rishav Kumar

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Abstract

This study explores the dynamic nature of skill demand in the online job market by leveraging real-time job posting data. A dataset was constructed through web scraping of job listings from Internshala, capturing key attributes such as job descriptions, required skills, and posting dates. Natural Language Processing (NLP) techniques were applied to extract relevant skill information from unstructured text data. The extracted data was analyzed to identify temporal trends in skill demand, followed by the application of time-series forecasting models to predict future demand. A comparative evaluation between

KEYWORDS

based extraction and TF-IDF methods was conducted to determine effectiveness

Cite This Article

Rishav Kumar, "A Data-Driven Study on Emerging Skill Demand in Online Job Portals using NLP and Time-Series Forecasting," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026.

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4/20/2026

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The authors declare no conflict of interest related to this research work.

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