Characterization of messages in Portuguese language with traces of racism on Twitter
Abstract
This article provides the characterization of textual data with traces of racism in Portuguese obtained from Twitter. It discusses racism in ethical and legal terms for conceptual identification. It explains Sentiment Analysis and some approaches and possible levels of analysis. At the end, the facts perceived during the data collection process are discussed.
Keywords:
Textile Data, Racism, RSO Twitter, Identification, Sentiment Analysis.
References
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Benevenuto, F. (2015). Métodos para análise de Sentimentos em mídias sociais.
Bruce, R. (2001). A Bayesian Approach to Semi-Supervised Learning.
Chu, Z., Gianvecchio, S., Wang, H., and Jajodia, S. (2012). Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Transactions on Dependable and Secure Computing.
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Greevy, E. and Smeaton, A. F. (2004). Classifying racist texts using a support vector machine. In Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR ’04.
Hirst, G. and Liu, B. (2012). SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES Sentiment Analysis and Opinion Mining Sentiment Analysis and Opinion Mining.
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José De Alencar, R. (2012). ESTUDO DA OCORRENCIAˆ DE CYBERBULLYING CONTRA PROFESSORES NA REDE SOCIAL TWITTER POR MEIO DE UM AL-GORITMO DE CLASSIFICAção BAYESIANO. pages 5–1.
Liu, B. (2012). Sentiment Analysis and Opinion Mining.
Martins, I. C. (2014). O racismo nas redes sociais : O mundo virtual é feito por pessoas de carne e osso!
Pang, B. and Lee, L. (2006). Opinion Mining and Sentiment Analysis. Foundations and Trends R in InformatioPang, B., & LeFoundations and Trends R in Information Retrieval, 1(2), 91–231., 1(2):91–231.
Vicente, B., De Lima, A., Machado, V. P., De Melo, R., and Veras, S. Abordagem Semi-supervisionada para Rotula ao de Dados.
Waseem, Z. and Hovy, D. (2016). Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. pages 88–93.
Benevenuto, F. (2015). Métodos para análise de Sentimentos em mídias sociais.
Bruce, R. (2001). A Bayesian Approach to Semi-Supervised Learning.
Chu, Z., Gianvecchio, S., Wang, H., and Jajodia, S. (2012). Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Transactions on Dependable and Secure Computing.
Decker, K. M. and Focardi, S. (1995). Technology Overview: A Report on Data Mining.
Greevy, E. and Smeaton, A. F. (2004). Classifying racist texts using a support vector machine. In Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR ’04.
Hirst, G. and Liu, B. (2012). SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES Sentiment Analysis and Opinion Mining Sentiment Analysis and Opinion Mining.
ISTOÉ Independente (2015). O criminoso da internet.
José De Alencar, R. (2012). ESTUDO DA OCORRENCIAˆ DE CYBERBULLYING CONTRA PROFESSORES NA REDE SOCIAL TWITTER POR MEIO DE UM AL-GORITMO DE CLASSIFICAção BAYESIANO. pages 5–1.
Liu, B. (2012). Sentiment Analysis and Opinion Mining.
Martins, I. C. (2014). O racismo nas redes sociais : O mundo virtual é feito por pessoas de carne e osso!
Pang, B. and Lee, L. (2006). Opinion Mining and Sentiment Analysis. Foundations and Trends R in InformatioPang, B., & LeFoundations and Trends R in Information Retrieval, 1(2), 91–231., 1(2):91–231.
Vicente, B., De Lima, A., Machado, V. P., De Melo, R., and Veras, S. Abordagem Semi-supervisionada para Rotula ao de Dados.
Waseem, Z. and Hovy, D. (2016). Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. pages 88–93.
Published
2018-08-08
How to Cite
SILVA, Rodolvo Consta Cezar da; FERNANDES, Deborah Silva Alves; FERNANDES, Márcio Giovane Cunha.
Characterization of messages in Portuguese language with traces of racism on Twitter. In: REGIONAL SCHOOL ON INFORMATICS OF GOIÁS (ERI-GO), 2018. , 2018, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 205-214.
