Using Multi-granular Fuzzy Linguistic Modelling Methods to Represent Social Networks Related Information in an Organized Way

  • Juan Antonio Morente-Molinera University of Granada
  • Francisco Javier Cabrerizo University of Granada
  • Sergio Alonso University of Granada
  • María Ángeles Martínez University of Granada
  • Enrique Herrera-Viedma Andalusian Research Institute on Datascience and Computational Intelligence, University of Granada, Spain 18071, Granada, Spain.


Social networks are the preferred mean for experts to share their knowledge and provide information. Therefore, it is one of the best sources that can be used for obtaining data that can be used for a high amount of purposes. For instance, determining social needs, identifying problems, getting opinions about certain topics, ... Nevertheless, this kind of information is difficult for a computational system to interpret due to the fact that the text is presented in free form and that the information that represents is imprecise. In this paper, a novel method for extracting information from social networks and represent it in a fuzzy ontology is presented. Sentiment analysis procedures are used in order to extract information from free text. Moreover, multi-granular fuzzy linguistic modelling methods are used for converting the information into the most suitable representation mean.


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How to Cite
MORENTE-MOLINERA, Juan Antonio et al. Using Multi-granular Fuzzy Linguistic Modelling Methods to Represent Social Networks Related Information in an Organized Way. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 2, mar. 2020. ISSN 1841-9844. Available at: <>. Date accessed: 13 apr. 2021. doi:


Multi-granular fuzzy linguistic modelling methods, fuzzy ontologies, sentiment analysis.