Using Multi-granular Fuzzy Linguistic Modelling Methods to Represent Social Networks Related Information in an Organized Way
MetadataShow full item record
AuthorMorente Molinera, Juan Antonio; Cabrerizo, F.J.; Alonso, S.; Martínez, M. A.; Herrera Viedma, Enrique
Multi-granular fuzzy linguistic modelling methodsFuzzy ontologiesSentiment analysis
Morente-Molinera, J. A., Cabrerizo, F. J., Alonso, S., Martínez, M. Á., & Herrera-Viedma, E. (2020). Using Multi-granular Fuzzy Linguistic Modelling Methods to Represent Social Networks Related Information in an Organized Way. International Journal of Computers Communications & Control, 15(2).
SponsorshipThis work has been supported by the ’Juan de la Cierva Incorporación’ grant from the Spanish Ministry of Economy and Competitiveness and by the Grant from the FEDER funds provided by the Spanish Ministry of Economy and Competitiveness (No. TIN2016-75850-R).
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.