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Topic Modeling and Classification of Cyberspace Papers Using Text Mining
- Source :
- Cyberspace Studies, Vol 2, Iss 1, Pp 103-125 (2018), Journal of Cyberspace Studies
- Publication Year :
- 2018
- Publisher :
- University of Tehran, 2018.
-
Abstract
- 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.
- Subjects :
- cyberspace
Text mining
Science
topic modeling
interaktive Medien
Naturwissenschaften
virtuelle Realität
lcsh:Technology
ddc:070
lcsh:Telecommunication
Interactive, electronic Media
lcsh:TK5101-6720
interaktive, elektronische Medien
News media, journalism, publishing
Natural Science and Engineering, Applied Sciences
Internet
algorithm
lcsh:T
electronic media
interactive media
Naturwissenschaften, Technik(wissenschaften), angewandte Wissenschaften
Algorithmus
trend
trend discovery
text mining
virtual reality
ddc:500
Publizistische Medien, Journalismus,Verlagswesen
elektronische Medien
Subjects
Details
- Language :
- English
- ISSN :
- 25885502 and 25885499
- Volume :
- 2
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Cyberspace Studies
- Accession number :
- edsair.dedup.wf.001..d43b28e6c1379982a0b544c42c950027