Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/28590
Autoria: | Vicente, M. Carvalho, J. P. Batista, F. |
Editor: | José-Luis Sierra-Rodríguez José-Paulo Leal Alberto Simões |
Data: | 2015 |
Título próprio: | Using unstructured profile information for gender classification of Portuguese and English |
Volume: | 563 |
Título e volume do livro: | SLATE 2015: 4th International Symposium on Languages, Applications and Technologies: Languages, Applications and Technologies |
Paginação: | 57 - 64 |
Referência bibliográfica: | Vicente, 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 |
ISBN: | 978-3-319-27653-3 |
DOI (Digital Object Identifier): | 10.1007/978-3-319-27653-3_6 |
Palavras-chave: | Twitter users Gender detection Fuzzy c-Means Supervised methods Unsupervised methods |
Resumo: | This 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. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | IT-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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conferenceObject_26082.pdf | 291,58 kB | Adobe PDF | Ver/Abrir |
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