Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/28590
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Campo DCValorIdioma
dc.contributor.authorVicente, M.-
dc.contributor.authorCarvalho, J. P.-
dc.contributor.authorBatista, F.-
dc.contributor.editorJosé-Luis Sierra-Rodríguez-
dc.contributor.editorJosé-Paulo Leal-
dc.contributor.editorAlberto Simões-
dc.date.accessioned2023-05-15T08:45:09Z-
dc.date.available2023-05-15T08:45:09Z-
dc.date.issued2015-
dc.identifier.citationVicente, M., Carvalho, J. P., & Batista, F. (2015). Using unstructured profile information for gender classification of Portuguese and English. EM J. L. Sierra-Rodríguez, J. P. Leal, & A. Simões (Eds.). SLATE 2015: 4th International Symposium on Languages, Applications and Technologies: Languages, Applications and Technologies (pp. 57-64). Springer. https://doi.org/10.1007/978-3-319-27653-3_6-
dc.identifier.isbn978-3-319-27653-3-
dc.identifier.urihttp://hdl.handle.net/10071/28590-
dc.description.abstractThis paper reports experiments on automatically detecting the gender of Twitter users, based on unstructured information found on their Twitter profile. A set of features previously proposed is evaluated on two datasets of English and Portuguese users, and their performance is assessed using several supervised and unsupervised approaches, including Naive Bayes variants, Logistic Regression, Support Vector Machines, Fuzzy c-Means clustering, and k-means. Results show that features perform well in both languages separately, but even best results were achieved when combining both languages. Supervised approaches reached 97.9 % accuracy, but Fuzzy c-Means also proved suitable for this task achieving 96.4 % accuracy.eng
dc.language.isoeng-
dc.publisherSpringer-
dc.relation.ispartofSLATE 2015: 4th International Symposium on Languages, Applications and Technologies: Languages, Applications and Technologies-
dc.rightsopenAccess-
dc.subjectTwitter userseng
dc.subjectGender detectioneng
dc.subjectFuzzy c-Meanseng
dc.subjectSupervised methodseng
dc.subjectUnsupervised methodseng
dc.titleUsing unstructured profile information for gender classification of Portuguese and Englisheng
dc.typeconferenceObject-
dc.event.typeConferênciapt
dc.event.date2015-
dc.pagination57 - 64-
dc.peerreviewedyes-
dc.volume563-
dc.date.updated2023-05-15T09:44:47Z-
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.1007/978-3-319-27653-3_6-
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-26082-
iscte.alternateIdentifiers.wosWOS:000370191100006-
iscte.alternateIdentifiers.scopus2-s2.0-84952685276-
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