IDEAS home Printed from https://ideas.repec.org/a/anm/alpnmr/v10y2022i2p155-166.html
   My bibliography  Save this article

Classification of News Texts by Categories Using Machine Learning Methods

Author

Listed:
  • Mehmet Kayakuş
  • Fatma Yiğit Açıkgöz

Abstract

In parallel with the advances in technology, digital journalism is preferred more than printed journalism day by day. Due to the fast and up-to-date sense of journalism provided by digital journalism and its ubiquitous accessibility features, it is read more by users. In addition to these advantages provided by digital journalism, it also has some difficulties compared to printed journalism. The stage of preparation and delivery of the news to the user requires more technological knowledge and equipment compared to printed journalism. The processes of title selection, text creation, photo selection and determination of the appropriate news category in the preparation phase of the news are designed to be both faster and user-friendly compared to printed publishing. The news created to be presented to the target audience may belong to one or more of different categories such as economy, politics, sports, technology, and health. The inclusion of the news in the appropriate category provides convenience in terms of reaching the right audience and archiving the news correctly. In this study, news texts were classified according to their categories based on the machine learning methods. In the study, news of five newspapers in three different categories were used. Bayesian classifier and decision tree methods were used to classify the news in the dataset including a total of 10.500 news. In the results of the study, it was observed that the Bayesian classifier classified the news more successfully according to their categories.

Suggested Citation

  • Mehmet Kayakuş & Fatma Yiğit Açıkgöz, 2022. "Classification of News Texts by Categories Using Machine Learning Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(2), pages 155-166, December.
  • Handle: RePEc:anm:alpnmr:v:10:y:2022:i:2:p:155-166
    DOI: https://doi.org/10.17093/alphanumeric.1149753
    as

    Download full text from publisher

    File URL: https://www.alphanumericjournal.com/media/Issue/volume-10-issue-2-2022/classification-of-news-texts-by-categories-using-machine-le_Ozhtqsj.pdf
    Download Restriction: no

    File URL: https://alphanumericjournal.com/article/classification-of-news-texts-by-categories-using-machine-learning-methods
    Download Restriction: no

    File URL: https://libkey.io/https://doi.org/10.17093/alphanumeric.1149753?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fatma Yiğit Açikgöz & Mehmet Kayakuş & Bianca-Ștefania Zăbavă & Onder Kabas, 2024. "Brand Reputation and Trust: The Impact on Customer Satisfaction and Loyalty for the Hewlett-Packard Brand," Sustainability, MDPI, vol. 16(22), pages 1-17, November.
    2. Yonis Gulzar & Ceren Oral & Mehmet Kayakus & Dilsad Erdogan & Zeynep Unal & Nisa Eksili & Pınar Celik Caylak, 2024. "Predicting High Technology Exports of Countries for Sustainable Economic Growth by Using Machine Learning Techniques: The Case of Turkey," Sustainability, MDPI, vol. 16(13), pages 1-20, June.
    3. Mehmet Kayakuş & Fatma Yiğit Açikgöz & Mirela Nicoleta Dinca & Onder Kabas, 2024. "Sustainable Brand Reputation: Evaluation of iPhone Customer Reviews with Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 16(14), pages 1-17, July.

    More about this item

    Keywords

    Category; Classification; Machine Learning; News;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

    Statistics

    Access and download statistics

    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:anm:alpnmr:v:10:y:2022:i:2:p:155-166. 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: Bahadir Fatih Yildirim (email available below). General contact details of provider: https://www.alphanumericjournal.com/ .

    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.