Conectando Opiniões a Opinadores: Um estudo de caso sobre protestos políticos no Brasil
Resumo
Análise de Sentimento (AS) sobre conteúdo de Mídias Sociais, bem como a Identificação de Usuários Influentes (IUI), também chamados de opinadores, provêm informações valiosas atualmente. Apesar da intrínseca relação entre opiniões e opinadores, muitos dos trabalhos recentes focam exclusivamente em uma das duas tarefas. Por meio de avaliações empíricas em uma amostra de dados de tweets relacionada à presidente do Brasil, nesse trabalho apresentamos os potenciais benefícios de se combinar métodos de AS com os de IUI. Em nossas análises, identificamos comportamentos distintos de propagação de opiniões e demonstramos que a opinião coletiva pode ser estimada com precisão utilizando tweets relacionados a poucos opinadores.
Referências
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Bigonha, C., Cardoso, T. N., Moro, M. M., Almeida, V. A., and Gonc¸alves, M. A. (2010). Detecting evangelists and detractors on twitter. In 18th Brazilian Symposium on Multimedia and the Web, pages 107–114.
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Lee, C., Kwak, H., Park, H., and Moon, S. (2010). Finding influentials based on the temporal order of information adoption in twitter. In Proc. of WWW. ACM.
Mourão, F., Rocha, L., Araújo, R., Couto, T., Gonçalves, M., and Meira Jr., W. (2008). Understanding temporal aspects in document classification. In WSDM 2008, USA.
Neves, A., Vieira, R., Mour˜ao, F., and Rocha, L. (2015). Quantifying complementarity among strategies for influeners´ detection on twitter. ICCS.
O’Connor, B., Balasubramanyan, R., Routledge, B. R., and Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proc. of the AAAI Conference on Weblogs and Social Media.
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Rocha, L., Agostinho, R., Sá, G., Silveira, T., Teixeira, F., Gomes, R., Ferreira, R., and Mourão, F. (2015). Saci: Sentiment analysis by collective inspection on social media content. Journal of Web Semantics.
Silva, A., Guimarães, S., Meira, Jr., W., and Zaki, M. (2013). Profilerank: Finding relevant content and influential users based on information diffusion. In Proc. of the ACM Workshop on Social Network Mining and Analysis.
Zhao, J., Dong, L., Wu, J., and Xu, K. (2012). Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In Proc. of KDD 2012, pages 1528–1531, China.
Bigonha, C., Cardoso, T. N., Moro, M. M., Almeida, V. A., and Gonc¸alves, M. A. (2010). Detecting evangelists and detractors on twitter. In 18th Brazilian Symposium on Multimedia and the Web, pages 107–114.
Brody, S. and Diakopoulos, N. (2011). Cooooooooooooollllllllllll!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In Proc. of EMNLP 2011, UK.
Edelman, D. (2010). Branding in the digital age. Harvard Business Review, 88:14–18.
Hu, X., Tang, J., Gao, H., and Liu, H. (2013). Unsupervised sentiment analysis with emotional signals. In Proc. of WWW ’13, pages 607–618, Geneva, Switzerland.
Ilyas, M. U. and Radha, H. (2011). Identifying influential nodes in online social networks using principal component centrality. In IEEE Int. Communications Conference.
Lee, C., Kwak, H., Park, H., and Moon, S. (2010). Finding influentials based on the temporal order of information adoption in twitter. In Proc. of WWW. ACM.
Mourão, F., Rocha, L., Araújo, R., Couto, T., Gonçalves, M., and Meira Jr., W. (2008). Understanding temporal aspects in document classification. In WSDM 2008, USA.
Neves, A., Vieira, R., Mour˜ao, F., and Rocha, L. (2015). Quantifying complementarity among strategies for influeners´ detection on twitter. ICCS.
O’Connor, B., Balasubramanyan, R., Routledge, B. R., and Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proc. of the AAAI Conference on Weblogs and Social Media.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report.
Rocha, L., Agostinho, R., Sá, G., Silveira, T., Teixeira, F., Gomes, R., Ferreira, R., and Mourão, F. (2015). Saci: Sentiment analysis by collective inspection on social media content. Journal of Web Semantics.
Silva, A., Guimarães, S., Meira, Jr., W., and Zaki, M. (2013). Profilerank: Finding relevant content and influential users based on information diffusion. In Proc. of the ACM Workshop on Social Network Mining and Analysis.
Zhao, J., Dong, L., Wu, J., and Xu, K. (2012). Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In Proc. of KDD 2012, pages 1528–1531, China.
Publicado
04/07/2016
Como Citar
VIEIRA, Ramon; NEVES, Alan; MOURÃO, Fernando; ROCHA, Leonardo.
Conectando Opiniões a Opinadores: Um estudo de caso sobre protestos políticos no Brasil. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA DA SBC (CTIC-SBC), 35. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 11-20.
