Conectando Opiniões a Opinadores: Um estudo de caso sobre protestos políticos no Brasil
Abstract
Sentiment analysis (SA) on Social Media content, as well as the Influential Users Detection (IUD), also called opinion-leaders, provide valuable information for many applications. Despite the intrinsic relation between opinions and opinion-leaders, most of the recent works focus exclusively on one of these two tasks. By empirical assessments on a data sample of tweets about the Brazilian president, this work demonstrates the potential benefits of combining SA Methods with IUD ones. In our analysis, we identified distinct behaviors of opinion propagation and demonstrated that the collective opinion may be accurately estimated by using a few opinion-leaders.
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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.
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.
Published
2016-07-04
How to Cite
VIEIRA, Ramon; NEVES, Alan; MOURÃO, Fernando; ROCHA, Leonardo.
Conectando Opiniões a Opinadores: Um estudo de caso sobre protestos políticos no Brasil. In: SBC UNDERGRADUATE RESEARCH CONTEST (CTIC-SBC), 35. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 11-20.
