Web Scraping for Job Listings Using Python and BeautifulSoup
DOI:
https://doi.org/10.63345/Keywords:
Web scraping, job listings, data mining, HTML parsing, employment trends, recruitment analyticsAbstract
The rapid evolution of the digital job market has resulted in a massive volume of employment opportunities being posted on online platforms daily, ranging from global recruitment portals to specialized niche boards. Accessing, structuring, and analyzing this data efficiently has become a crucial requirement for researchers, recruiters, and policymakers. Manual collection of job listing data is inherently slow, inconsistent, and prone to human error, which significantly limits the potential for large-scale, real-time labor market analysis. This research investigates the application of Python and the BeautifulSoup library for automated web scraping of job listings, providing a scalable, accurate, and efficient approach to recruitment data extraction.
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