Extracting collective trends from Twitter using social-based data mining

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dc.contributor.author Bello Orgaz, Gema
dc.contributor.author Menéndez, Héctor D.
dc.contributor.author Okazaki, Shintaro
dc.contributor.author Camacho, David
dc.contributor.other UAM. Departamento de Ingeniería Informática es_ES
dc.date.accessioned 2015-03-25T18:21:38Z
dc.date.available 2015-03-25T18:21:38Z
dc.date.issued 2013
dc.identifier.citation Computational Collective Intelligence. Technologies and Applications: 5th International Conference, ICCCI 2013, Craiova, Romania, September 11-13, 2013, Proceedings. Lecture Notes in Computer Science, Volumen 8083. Springer, 2013. 622-630. en_US
dc.identifier.isbn 978-3-642-40494-8 (print) en_US
dc.identifier.isbn 978-3-642-40495-5 (online) en_US
dc.identifier.issn 0302-9743 (print) en_US
dc.identifier.issn 1611-3349 (online) en_US
dc.identifier.uri http://hdl.handle.net/10486/664807
dc.description The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40495-5_62 en_US
dc.description Proceedings 5th International Conference, ICCCI 2013, Craiova, Romania, September 11-13, 2013, en_US
dc.description.abstract Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Network is Twitter. This Social Network was created to share comments and opinions. The information provided by users is specially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining and Text Mining techniques (such as classification and clustering) might be used for knowledge extraction trying to distinguish the meaning of the opinions. This work is focused on the analysis about how these techniques can interpret these opinions within the Social Network using information related to IKEA® company. en_US
dc.description.sponsorship The preparation of this manuscript has been supported by the Spanish Ministry of Science and Innovation under the following projects: TIN2010-19872, ECO2011-30105 (National Plan for Research, Development and Innovation) and the Multidisciplinary Project of Universidad Aut´onoma de Madrid (CEMU-2012-034) en_US
dc.format.extent 11 pág. es_ES
dc.format.mimetype application/pdf en
dc.language.iso eng en
dc.publisher Springer Berlin Heidelberg
dc.relation.ispartof Lecture Notes in Computer Science en_US
dc.rights © Springer-Verlag Berlin Heidelberg 2011
dc.subject.other Classification en_US
dc.subject.other Clustering en_US
dc.subject.other Collective trends en_US
dc.subject.other Data mining en_US
dc.subject.other Social network en_US
dc.subject.other Twitter en_US
dc.title Extracting collective trends from Twitter using social-based data mining en_US
dc.type conferenceObject en
dc.type bookPart en
dc.subject.eciencia Informática es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/978-3-642-40495-5_62
dc.identifier.doi 10.1007/978-3-642-40495-5_62
dc.identifier.publicationfirstpage 622
dc.identifier.publicationlastpage 630
dc.identifier.publicationvolume 8083
dc.relation.eventdate September 11-13, 2013 en_US
dc.relation.eventnumber 5
dc.relation.eventplace Craiova (Romania) en_US
dc.relation.eventtitle 5th International Conference on Computational Collective Intelligence, ICCCI 2013 en_US
dc.type.version info:eu-repo/semantics/acceptedVersion en
dc.contributor.group Análisis de Datos e Inteligencia Aplicada (ING EPS-012) es_ES
dc.rights.accessRights openAccess en
dc.authorUAM Camacho Fernández, David (261274)
dc.authorUAM Bello Orgaz, Gema (262523)
dc.authorUAM Okazaki Ono, Shintaro (271225)

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