IDEAS home Printed from https://ideas.repec.org/a/cmj/seapas/y2020i24p287-298.html
   My bibliography  Save this article

Insights From It Jobs Market With Text Mining Approach

Author

Listed:
  • Ionela MANIU

    (Department of Mathematics and Informatics, Research Center in Informatics and Information Technology, Faculty of Sciences, Lucian Blaga University of Sibiu, Romania)

  • Emilia-Loredana POP

    (Department of Computer Science Faculty of Mathematics and Computer Science, Babes Bolyai University Cluj-Napoca, Romania)

  • Augusta RATIU

    (Department of Mathematics and Informatics Faculty of Sciences, Lucian Blaga University of Sibiu, Romania)

  • Eduard Traian STEFANESCU

    (Faculty of Sciences, Lucian Blaga University of Sibiu, Romania)

Abstract

On the labor market, IT Jobs represent one of the most important domains. This paper analyzed the IT Jobs from a large collection of job listings from a Romanian website. The Web Crawling techniques were used to extract the data from the website, the Text Mining, World Cloud and statistic techniques to analyze and present the results. Insights that are required for an IT Job were extracted. The results highlight the following: the most required IT Job Type was the Full Time one, the Career Level was the Mid Level and the Study Level was the Graduated one. The Text Mining Approach revealed that the most frequent words for the IT Jobs offers were: team, work, development, project, experience, environment, service, skill, customer, knowledge and software. A comparison between terms in IT Jobs in English language versus IT Jobs in Romanian language was also performed. As conclusion, in this paper, by combining different techniques to extract and analyze the textual data set of vacancy offers knowledge from the IT Jobs domain was determined and discovered. The extracted information presented insights in current skills, personal needs, career paths of the Romanian current IT labor market needs. The results of this research could be valuable information for public bodies, employers, higher education policy makers, researchers, students and parents.

Suggested Citation

  • Ionela MANIU & Emilia-Loredana POP & Augusta RATIU & Eduard Traian STEFANESCU, 2020. "Insights From It Jobs Market With Text Mining Approach," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 24, pages 287-298, December.
    • Handle: RePEc:cmj:seapas:y:2020:i:24:p:287-298
    as

    Download full text from publisher

    File URL: http://seaopenresearch.eu/Journals/articles/SPAS_24_4.pdf
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cmj:seapas:y:2020:i:24:p:287-298. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Serghie Dan (email available below). General contact details of provider: https://seaopenresearch.eu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.