Classificação de gênero em dados do Twitter baseada na extração de meta-atributos textuais

• Ano de defesa: 2016
• Autor(a) principal: Lopes Filho, José Ahirton Batista
• Orientador(a): Castro, Leandro Nunes de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Presbiteriana Mackenzie
• Palavras-chave em Português: aprendizado de máquina; classificação; gênero; mídias sociais; Twitter; extração; meta-atributos; português
• Palavras-chave em Inglês: machine-learning; classification; gender; social media;Twitter; extraction; meta-attributes; portuguese language
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://dspace.mackenzie.br/handle/10899/24437

With the growth of social media in recent years, there has been an increase on the 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,and 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 work focuses on the task of gender classification by using 65 textual meta-attributes, commonly used in text attribution tasks, for the extraction of gender expression linguistic cues in tweets written in Portuguese.The work takes into account characters, syntax, words, structure and morphology, as well as selected psycolinguistic cues of short length, multi-genre, content free texts posted on Twitter to classify author's gender via four different machine-learning algorithms. The proposed meta-attributes in this process are also evaluated.