Topic Modeling for Skill Extraction from Job Postings
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Tarih
2023
Yazarlar
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Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
With an increase in the number of online job posts, it is becoming increasingly challenging for both job searchers and employers to navigate this large quantity of information. Therefore, it is crucial to use natural language processing techniques to analyze and draw inferences from these job postings. This study focuses on the most recent job postings in Turkiye for Computer Engineering and Management Information Systems. The objective is to extract skills for the job postings for job seekers wanting to apply for a new position. LSA is a statistical method for figuring out the underlying characteristics and meanings of sentences and words in natural language. Frequency analysis was also performed in addition to the LSA analyses with the goal of conducting a thorough examination of job postings. This study was conducted to determine and evaluate the skills that the sector actually needs. Thus, job seekers will have the chance to develop themselves in a more planned way. The findings indicate that the job postings for the two departments reflect various characteristics in terms of social and technical abilities. A higher requirement for social skills is thought to exist in the field of Management Information Systems rather than Computer Engineering. It has been discovered that employment involving data have been highly popular in recent years, and both departments often list opportunities involving data analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Açıklama
5th Iberoamerican and the 4th Indo-American Knowledge Graphs and Semantic Web Conference, KGSWC 2023 -- 13 November 2023 through 15 November 2023 -- -- 304229
Anahtar Kelimeler
job postings; latent semantic analysis; natural language processing; social web, Information management; Information use; Modeling languages; Natural language processing systems; Semantic Web; Computer engineering; Job postings; Job seekers; Language processing; Latent Semantic Analysis; Natural language processing; Natural languages; Skill extractions; Social webs; Topic Modeling; Semantics
