dc.creatorColetta, Luiz Fernando Sommaggio
dc.creatorSilva, Nádia Félix Felipe da
dc.creatorHruschka, Eduardo Raul
dc.creatorHruschka Junior, Estevam R.
dc.date.accessioned2015-03-20T19:09:25Z
dc.date.accessioned2018-07-04T16:59:56Z
dc.date.available2015-03-20T19:09:25Z
dc.date.available2018-07-04T16:59:56Z
dc.date.created2015-03-20T19:09:25Z
dc.date.issued2014-10
dc.identifierBrazilian Conference on Intelligent Systems, 3th, 2014, São Carlos.
dc.identifier9781479956180
dc.identifierhttp://www.producao.usp.br/handle/BDPI/48602
dc.identifierhttp://dx.doi.org/10.1109/BRACIS.2014.46
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1643261
dc.description.abstractThe goal of sentiment analysis is to determine opinions, emotions, and attitudes presented in source material. In tweet sentiment analysis, opinions in messages can be typically categorized as positive or negative. To classify them, researchers have been using traditional classifiers like Naive Bayes, Maximum Entropy, and Support Vector Machines (SVM). In this paper, we show that a SVM classifier combined with a cluster ensemble can offer better classification accuracies than a stand-alone SVM. In our study, we employed an algorithm, named 'C POT.3'E-SL, capable to combine classifier and cluster ensembles. This algorithm can refine tweet classifications from additional information provided by clusterers, assuming that similar instances from the same clusters are more likely to share the same class label. The resulting classifier has shown to be competitive with the best results found so far in the literature, thereby suggesting that the studied approach is promising for tweet sentiment classification.
dc.languageeng
dc.publisherUniversidade de São Paulo - USP
dc.publisherUniversidade Federal de São Carlos - UFSCar
dc.publisherCentro de Robótica de São Carlos - CROB
dc.publisherSociedade Brasileira de Computação - SBC
dc.publisherSociedade Brasileira de Automática - SBA
dc.publisherSão Carlos
dc.relationBrazilian Conference on Intelligent Systems, 3th
dc.rightsCopyright IEEE
dc.rightsclosedAccess
dc.subjectTweet Sentiment Analysis
dc.subjectClassification
dc.subjectSupport Vector Machines
dc.subjectClustering
dc.subjectCluster Ensemble
dc.titleCombining classification and clustering for tweet sentiment analysis
dc.typeActas de congresos


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