@misc{10481/61377,
year = {2020},
month = {4},
url = {http://hdl.handle.net/10481/61377},
abstract = {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.},
organization = {This 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).},
publisher = {Universitatea Agora},
keywords = {Multi-granular fuzzy linguistic modelling methods},
keywords = {Fuzzy ontologies},
keywords = {Sentiment analysis},
title = {Using Multi-granular Fuzzy Linguistic Modelling Methods to Represent Social Networks Related Information in an Organized Way},
doi = {10.15837/ijccc.2020.2.3851},
author = {Morente Molinera, Juan Antonio and Cabrerizo, F.J. and Alonso, S. and Martínez, M. A. and Herrera Viedma, Enrique},
}