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Web news mining in an evolving framework

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Control, Aprendizaje y Optimización de Sistemas (CAOS)es
dc.contributor.authorIglesias Martínez, José Antonio
dc.contributor.authorTiemblo, Alexandra
dc.contributor.authorLedezma Espino, Agapito Ismael
dc.contributor.authorSanchis de Miguel, María Araceli
dc.date.accessioned2022-10-19T14:38:06Z
dc.date.available2022-10-19T14:38:06Z
dc.date.issued2016-03-01
dc.description.abstractOnline news has become one of the major channels for Internet users to get news. News websites are daily overwhelmed with plenty of news articles. Huge amounts of online news articles are generated and updated everyday, and the processing and analysis of this large corpus of data is an important challenge. This challenge needs to be tackled by using big data techniques which process large volume of data within limited run times. Also, since we are heading into a social-media data explosion, techniques such as text mining or social network analysis need to be seriously taken into consideration. In this work we focus on one of the most common daily activities: web news reading. News websites produce thousands of articles covering a wide spectrum of topics or categories which can be considered as a big data problem. In order to extract useful information, these news articles need to be processed by using big data techniques. In this context, we present an approach for classifying huge amounts of different news articles into various categories (topic areas) based on the text content of the articles. Since these categories are constantly updated with new articles, our approach is based on Evolving Fuzzy Systems (EFS). The EFS can update in real time the model that describes a category according to the changes in the content of the corresponding articles. The novelty of the proposed system relies in the treatment of the web news articles to be used by these systems and the implementation and adjustment of them for this task. Our proposal not only classifies news articles, but it also creates human interpretable models of the different categories. This approach has been successfully tested using real on-line news. (C) 2015 Elsevier B.V. All rights reserved.en
dc.description.sponsorshipThis work has been supported by the Spanish Government under i-Support (Intelligent Agent Based Driver Decision Support) Project (TRA2011-29454-C03-03).en
dc.identifier.bibliographicCitationIglesias, J.A., Tiemblo, A. Ledezma, A. Sanchís, A. (2016). Web news mining in an evolving framework. Information Fusion, 28, pp. 90-98. https://doi.org/10.1016/j.inffus.2015.07.004en
dc.identifier.doihttps://doi.org/10.1016/j.inffus.2015.07.004
dc.identifier.issn1566-2535
dc.identifier.publicationfirstpage90
dc.identifier.publicationlastpage98
dc.identifier.publicationtitleInformation Fusionen
dc.identifier.publicationvolume28
dc.identifier.urihttps://hdl.handle.net/10016/35904
dc.identifier.uxxiAR/0000017408
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TRA-2011-29454-C03-03es
dc.rights© 2015 Elsevier B.V.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.otherbig dataen
dc.subject.othertexten
dc.subject.otheridentificationen
dc.subject.otherextractionen
dc.subject.otherstreamsen
dc.titleWeb news mining in an evolving frameworken
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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