Paper Info
Title
Using Multi-Granular Fuzzy Linguistic Modelling Methods To Represent Social Networks Related Information In An Organized Way
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.
Year
DOI
Venue
2020
10.15837/ijccc.2020.2.3851
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
Keywords
DocType
Volume
Multi-granular fuzzy linguistic modelling methods, fuzzy ontologies, sentiment analysis
Journal
15
Issue
ISSN
Citations 
2
1841-9836
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Juan Antonio Morente-Molinera116216.00
Francisco Javier Cabrerizo2165559.39
Sergio Alonso3166953.28
María Ángeles Martínez400.34
Enrique Herrera-Viedma513105642.24