Akademik Veri Yönetim Sistemi
4th International Conference on Electronic Governance with Emerging Technologies, EGETC 2025, Melbourne, Avustralya, 25 - 26 Eylül 2025, cilt.2730 CCIS, ss.42-51, (Tam Metin Bildiri)
Analyzing job descriptions in today’s dynamic job market is crucial for understanding industry trends, skill demands, and workforce planning. This study uses web mining techniques to extract, process, and analyze job postings from various online sources. By leveraging web scraping, Natural Language Processing (NLP), and machine learning, we identify key skills, qualifications, and experience requirements across different industries. The study also investigates job-skill gaps by comparing demanded qualifications with workforce competencies. The findings provide valuable insights for employers and universities. The proposed methodology offers a scalable real-time labor market analysis approach, which can support career guidance systems and strategic workforce planning. Future work may focus on enhancing recommendation models for personalized job matching and integrating dynamic labor market forecasting techniques.