Artículo:
Fuzzy Queries of Social Networks involving Sentiment Analysis and Topic Detection

Fecha

2018-09-17

Editor

Sistedes

Publicado en

Actas de las XVIII Jornadas de Programación y Lenguajes (PROLE 2018)

Licencia

CC BY 4.0

Resumen

Social networks have become a source of data which are of interest in all areas, and their querying and analysis is a hot topic in computer science. Our research group has developed a fuzzy extension of the Semantic Web query language SPARQL, called FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL). This extension provides mechanisms to express fuzzy queries against RDF data. FSA-SPARQL works with social networks. With this aim, FSA-SPARQL enables the transformation and fuzzification of social network API data. Fuzzification of social networks data is automatic and user-defined enabling a wide range of mechanisms for ranking and categorization, including sentiment analysis and topic detection. As case study, FSA-SPARQL has been used to query three well-known social networks: Twitter, Foursquare and TMDb.

Descripción

Acerca de Almendros-Jiménez, Jesus M.

Palabras clave

Fuzzy Logic, Semantic Web, Social Networks, SPARQL
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