Gender classification of twitter data based on textual meta-attributes extraction

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dc.contributor.authorFilho J.A.B.L.
dc.contributor.authorPasti R.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-13T00:55:34Z
dc.date.available2024-03-13T00:55:34Z
dc.date.issued2016
dc.description.abstract© Springer International Publishing Switzerland 2016.With the growth of social media in recent years, there has been an increasing interest in the automatic characterization of users based on the informal content they generate. In this context, the labeling of users in demographic categories, such as age, ethnicity, origin and race, among the investigation of other attributes inherent to users, such as political preferences, personality and gender expression, has received a great deal of attention, especially based on Twitter data. The present paper focuses on the task of gender classification by using 60 textual meta-attributes, commonly used on text attribution tasks, for the extraction of gender expression linguistic cues in tweets written in Portuguese. Therefore, taking into account characters, syntax, words, structure and morphology of short length, multi-genre, content free texts posted on Twitter to classify author's gender via three different machine-learning algorithms as well as evaluate the influence of the proposed meta-attributes in this process.
dc.description.firstpage1025
dc.description.lastpage1034
dc.description.volume444
dc.identifier.doi10.1007/978-3-319-31232-3_97
dc.identifier.issn2194-5357
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36106
dc.relation.ispartofAdvances in Intelligent Systems and Computing
dc.rightsAcesso Restrito
dc.subject.otherlanguageClassification
dc.subject.otherlanguageExtraction
dc.subject.otherlanguageGender
dc.subject.otherlanguageMachine-learning
dc.subject.otherlanguageMeta-attributes
dc.subject.otherlanguagePortuguese language
dc.subject.otherlanguageSocial media
dc.subject.otherlanguageTwitter
dc.titleGender classification of twitter data based on textual meta-attributes extraction
dc.typeArtigo de evento
local.scopus.citations21
local.scopus.eid2-s2.0-84961629208
local.scopus.subjectGender
local.scopus.subjectMeta-attributes
local.scopus.subjectPortuguese languages
local.scopus.subjectSocial media
local.scopus.subjectTwitter
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84961629208&origin=inward
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