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dc.creatorQuesada Grosso, Minor Eduardo
dc.creatorCasasola Murillo, Edgar
dc.creatorLeoni de León, Jorge Antonio
dc.date.accessioned2019-11-18T16:45:15Z
dc.date.available2019-11-18T16:45:15Z
dc.date.issued2017
dc.identifier.citationhttp://www2.clei.org/cleiej/paper.php?id=376es_ES
dc.identifier.issn0717- 5000
dc.identifier.urihttp://hdl.handle.net/10669/79877
dc.description.abstractMining and exploitation of data in social networks has been the focus of many efforts, but despite the resources and energy invested, still remains a lot for doing given its complexity, which requires the adoption of a multidisciplinary approach . Specifically, on what concerns to this research, the content of the texts published regularly, and at a very rapid pace, at sites of microblogs (eg Twitter.com) can be used to analyze global and local trends. These trends are marked by microblogs emerging topics that are distinguished from others by a sudden and accelerated rate of posts related to the same topic; in other words, by an increment of popularity in relatively short periods, a day or a few hours, for example Wanner et al. . The problem, then, is twofold, first to extract the topics, then to identify which of those topics are trending. A recent solution, known as Bursty Biterm Topic Model (BBTM) is an algorithm for identifying trending topics, with a good level of performance in Twitter, but it requires great amount of computer processing. Hence, this research aims to determine if it is possible to reduce the amount of processing required and getting equally good results. This reduction carry out by a discrimination of co-occurrences of words (biterms) used by BBTM to model trending topics. In contrast to our previous work, in this research, we carry on a more complete and exhaustive set of experiments.es_ES
dc.description.sponsorshipUniversidad de Costa Rica/[745-B4-048]UCR/Costa Ricaes_ES
dc.description.sponsorshipUniversidad de Costa Rica/[745-B6-175]UCR/Costa Ricaes_ES
dc.language.isoen_USes_ES
dc.sourceClei Electronic Journal, vol. 20(1), pp.1-16es_ES
dc.subjectTrending topicses_ES
dc.subjectTopic modelses_ES
dc.subjectShort textes_ES
dc.subjectNLPes_ES
dc.subjectTopic extractiones_ES
dc.subjectNatural language processinges_ES
dc.titleTrending Topic Extraction using Topic Models and Biterm Discriminationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.19153/cleiej.20.1.3
dc.description.procedenceUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e Informáticaes_ES
dc.description.procedenceUCR::Vicerrectoría de Docencia::Artes y Letras::Facultad de Letras::Escuela de Filología, Lingüística y Literaturaes_ES
dc.identifier.codproyecto745-B4-048
dc.identifier.codproyecto745-B6-175


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