IDEAS home Printed from https://ideas.repec.org/a/igg/jaci00/v10y2019i3p17-32.html
   My bibliography  Save this article

Web Text Categorization Based on Statistical Merging Algorithm in Big Data Environment

Author

Listed:
  • Rujuan Wang

    (College of Humanities & Sciences of Northeast Normal University, Changchun, China)

  • Gang Wang

    (Northeast Normal University, Changchun, China)

Abstract

In the field of modern information technology, how to find information quickly, accurately and comprehensively that users really needed has become the focus of research in this field. In this article, a feature selection method based on a complex network is proposed for the structure and content characteristics of large-scale web text information. The preprocessed web text is converted into a complex network. The nodes in the network correspond to the entries in the text. The edges of the network correspond to the links between the entries in the text, and the degree of nodes and the aggregation system are used. Second, the text classification method is studied from the point of view of data sampling, and a text classification method based on density statistics is proposed. This method uses not only the density information of the text feature set in the classification process, but also the use of statistical merging criteria to get the text. The difference information of each feature has a better classification effect for large text collections.

Suggested Citation

  • Rujuan Wang & Gang Wang, 2019. "Web Text Categorization Based on Statistical Merging Algorithm in Big Data Environment," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 10(3), pages 17-32, July.
    • Handle: RePEc:igg:jaci00:v:10:y:2019:i:3:p:17-32
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJACI.2019070102
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Timmy H. Tseng & Shao-Hsun Chang & Yu-Min Wang & Yi-Shun Wang & Shin-jeng Lin, 2020. "An Empirical Investigation of the Longitudinal Effect of Online Consumer Reviews on Hotel Accommodation Performance," Sustainability, MDPI, vol. 13(1), pages 1-16, December.
    2. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.

    More about this item

    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:igg:jaci00:v:10:y:2019:i:3:p:17-32. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.