Topic Modeling and Classification of Cyberspace Papers Using Text Mining

• Main Authors: Babak Sohrabi, Iman Raeesi Vanani, MMohsen Baranizade Shineh
• Format: Article
• Language: English
• Published: University of Tehran 2018-01-01
• Series: Cyberspace Studies
• Subjects: cyberspace; Text mining; trend discovery; topic modeling
• Online Access: https://jcss.ut.ac.ir/article_64635_925f4efbf8d90e012c7ccb0684f1ff8f.pdf

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.