Zeitschriftenartikel

Topic Modeling and Classification of Cyberspace Papers Using Text Mining

The global cyberspace networks provide individuals with platforms to can interact, exchange ideas, share information, provide social support, conduct business, create artistic media, play games, engage in political discussions, and many more. The term cyberspace has become a conventional means to describe anything associated with the Internet and the diverse Internet culture. In fact, cyberspace is an umbrella term that covers all issues occurring through the interaction of information systems and humans over these networks. Deep evaluation of the scientific articles on the cyberspace domain provides concentrated knowledge and insights about major trends of the field. Text mining tools and techniques enable the practitioners and scholars to discover significant trends in a large set of internationally validated papers. This study utilizes text mining algorithms to extract, validate, and analyze 1860 scientific articles on the cyberspace domain and provides insight over the future scientific directions or cyberspace studies.

Topic Modeling and Classification of Cyberspace Papers Using Text Mining

GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln

Namensnennung - Nicht kommerziell 4.0 International

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Anmerkungen
Status: Veröffentlichungsversion; begutachtet (peer reviewed)
Sprache
Deutsch
Umfang
Seite(n): 103-125
ISSN
2538-6255

Erschienen in
Journal of Cyberspace Studies, 2(1)

Bezug (was)
Publizistische Medien, Journalismus,Verlagswesen
Naturwissenschaften
cyberspace; text mining; trend discovery; topic modeling
interaktive, elektronische Medien
Naturwissenschaften, Technik(wissenschaften), angewandte Wissenschaften
Internet
interaktive Medien
elektronische Medien
virtuelle Realität
Trend
Algorithmus

Beteiligte Personen und Organisationen
Sohrabi, Babak
Vanani, Iman Raeesi
Shineh, Mohsen Baranizade
Erschienen
2018

Rechteinformation
GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
Letzte Aktualisierung
24.01.2023, 06:48 MEZ

Objekttyp


  • Zeitschriftenartikel

Beteiligte


  • Sohrabi, Babak
  • Vanani, Iman Raeesi
  • Shineh, Mohsen Baranizade

Entstanden


  • 2018

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