Title: User content categorisation model, a generic model that combines text mining and semantic models
Authors: Randa Benkhelifa; Ismaïl Biskri; Fatima Zohra Laallam; Esma Aïmeur
Addresses: Faculté des nouvelles technologies de l'information et de la communication, Laboratoire de l'intelligence artificielle et des technologies de l'information, Université Kasdi Merbah Ouargla, Ouargla 30-000, Algeria ' LAMIA, Département de Mathématiques et Informatique, Université du Québec à Trois-Rivières, Trois-Rivières, Canada ' Faculté des nouvelles technologies de l'information et de la communication, Laboratoire de l'intelligence artificielle et des technologies de l'information, Université Kasdi Merbah Ouargla, Ouargla 30-000, Algeria ' Department of Computer Science Operations Research, University of Montreal, Montreal, Canada
Abstract: Social networking websites are growing not only regarding the number of users but also in terms of the user-generated content. These data represent a valuable source of information for several applications, which require the meaning of that content associated with the personal data. However, the current structure of social networks does not allow extracting in a fast and straightforward way the hidden information sought by these applications. Major efforts have emerged from the semantic web community addressing this problem trying to represent the user as accurately as possible. They are not unable to give a sense to the user-generated content. For this, more sense-making needs to be done on the content, to enrich the user profile. In this paper, we introduce a generic model called user content categorisation (UCC). It incorporates the text mining approach into a semantic model to enrich the user profile by including information on user's posts classifications.
Keywords: semantic models; ontology; text mining; machine learning; user interests; user categorisation; text categorisation; profiling; ontology learning.
International Journal of Computational Science and Engineering, 2020 Vol.21 No.4, pp.536 - 555
Received: 03 May 2018
Accepted: 18 Mar 2019
Published online: 24 Apr 2020 *