Analysis of Sentiment of Tweets Related to Protests that occurred in Brazil between June and August 2013

• Tiago C. de França Federal University of Rio de Janeiro
• Jonice Oliveira Federal University of Rio de Janeiro

Abstract

The sentiment analysis of citizens is possible by using suitable techniques of analyzes applied to a massive database which is composed by messages provided by persons on Web. The goal of this paper is to analyze the opinion about protests that occurred in Brazil in 2013. For this, a database composed by tweets written in Brazilian Portuguese was used. This database was pre-processed for the corpus 'creation. We observed that polarity (agreement or disagreement with the protests) of these messages and the final results have shown that the majority of messages are agreement ones.

Keywords: Sentiment Analysis, Twitter, Protests

References

Basile, V.; Nissim, M. Sentiment analysis on Italian tweets, 2013. 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis 2013.

Baumgarten, M. Keyword-Based Sentiment Mining using Twitter, 2013. International Journal of Ambient Computing and Intelligence, 5 (2). pp. 56-69.

Bermingham, A.; Conway, M. Combining social network analysis and sentiment analysis to explore the potential for online radicalization. Disponivel em: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5231878. Acessado em 10 de Março de 2014.

Brew, A., Greene, D., Archambault, D., and Cunningham, P. (2011). “Deriving Insights from National Happiness Indices.”, 2011 IEEE 11th International Conference On Data Mining Workshops (ICDMW), pp. 53 –60.

Fantástico. Transporte e política são principais razões de manifestações, diz pesquisa. Disponível em: http://g1.globo.com/fantastico/noticia/2013/06/transporte-e-politica-sao-principais-razoes-de-manifestacoes-diz-pesquisa.html. Acessado em 10 de Fevereiro de 2014.

Ferreira, M. Classificação Hierárquica da Atividade Económica das Empresas a partir de Texto da Web, 2011. Disponível em [http://sigarra.up.pt/fep/pt/PUBLS_PESQUISA.FORMVIEW?p_id=13311]. Acessado em Setembro de 2013.

FIFA. Copa das Confederações, 2013. Disponível em: http://pt.fifa.com/confederationscup/matches/. Acessado em 30 de Março de 2014.

Freitas, L. A.; Vieira, R. Ontology based feature level opinion mining for portuguese reviews, 2013. Proceedings of the 22nd international conference on World Wide Web companion.

Gonçalves, P.; Dores, W.; e Benevenuto, F. PANAS-t: Uma Escala Psicometrica para Medição de ˜Sentimentos no Twitter, 2012. Disponível em [http://www.imago.ufpr.br/csbc2012/anais_csbc/eventos/brasnam/artigos/]. Acessado em Agosto de 2013.

Hu, X.; Tang, J.; Gao, H.; Liu, H. Unsupervised Sentiment Analysis with Emotional Signals, 2013. Disponível em [http://www.public.asu.edu/~xiahu/papers/www13.pdf]. Acessado em Agosto de 2013.

Jambhulkar, P.; e Nirkhi, S. A Survey Paper on Cross-Domain Sentiment Analysis. International Journal of Advanced Research in Computer and Communication EngineeringVol. 3, Issue 1, January 2014

JMJ. Jornada Mundial da Juventude, Rio 2013. Disponível em: http://www.rio2013.com/. Acessado em 30 de Março de 2014.

Jurafsky, D., and Martin, J. H. (2009). Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, Prentice Hall, 2nd Edition.

Karamibekr, M.; e Ghorbani, A. A. Sentiment analysis of social issues, 2012. Social Informatics (SocialInformatics).

Li, Y.-M., and Li, T.-Y. (2011). “Deriving Marketing Intelligence over Microblogs.”, Proceedings of 44th Hawaii International Conference On System Sciences (HICSS), pp. 1 –10.

Liu, B. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, Maio de 2012. Synthesis Lectures on Human Language Technologies.

Lucca, G.; Pereira, I. A.; Prisco, A.; Borges, E. N. Uma implementação do algoritmo Naïve Bayes para classificação de texto, 2013. Disponivel em [http://www.lbd.dcc.ufmg.br/colecoes/erbd/2013/0019.pdf]. Acessado em Setembro de 2013.

Naaman, C.-H. L. Mor., and Boase, J. (2010). “Is it all About Me? User Content in Social Awareness Streams”, Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, 2010.

Nagy, A.; Valley, C.M.S.; Stamberger, J. Crowd sentiment detection during disasters and crises. Proceedings of the 9th International ISCRAM Conference – Vancouver, Canada, April 2012.

Nascimento, P.; Aguas, R.; Lima, D.; Kong, X.; Osiek, B.; Xexéo, G.; e Souza, J. Análise de sentimentos de tweets com foco em notícias, 2012. Disponível em [http://www.imago.ufpr.br/csbc2012/anais_csbc/eventos/brasnam/artigos] Acessado em Setembro de 2013.

Neethu, M. S.; e Rajasree, R. Sentiment analysis in twitter using machine learning techniques. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

Neri, F.; Aliprandi, C.; Capeci, F.; e Cuadros, M. Sentiment Analysis on Social Media, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

Pak, A., and Paroubek, P. (2010). “Twitter as a corpus for sentiment analysis and opinion mining.”, Proceedings of the 7th Conference on International Language Resources and Evaluation (LREC’10).

Protestos no Brasil em 2013. Manifestações no Brasil em 2013. Disponível em: http://pt.wikipedia.org/wiki/Manifesta%C3%A7%C3%B5es_no_Brasil_em_2013. Acessado em 30 de Março de 2014.

Shahheidari, S. Twitter Sentiment Mining: A Multi Domain Analysis. Complex, Intelligent, and Software Intensive Systems (CISIS), 2013 Seventh International Conference.

Silva, N. R.; Lima, D.; e Barros, F. SAPair: Um Processo de Análise de Sentimento no Nível de Característica, 2012. Disponível em [http://www.ppgia.pucpr.br/~enia/anais/wti/artigos.html] Acessado em Setembro de 2013.

Tumitan, D.; Becker, K. Tracking sentiment evolution on user-generated content: A case study in the brazilian political scene. Disponível em: http://sbbd2013.cin.ufpe.br/Proceedings/application/06.html. Acessado em 20 de Fevereiro de 2014.

Varela, P.N. B. Sentiment Analysis, 2012. Disponível em [http://goo.gl/PN3Xwg]. Acessado em Setembro de 2013.

Wiebe, J., Wilson, T., Cardie, C. (2006). “Annotating Expressions of Opinions andEmotions in Language”, Language Resources and Evaluation, v. 39, n. 2-3, pp. 165 –210.

Wiebe, J., Wilson, T., Cardie, C. (2006). “Annotating Expressions of Opinions andEmotions in Language”, Language Resources and Evaluation, v. 39, n. 2-3, pp. 165 –210.

Zhang, K., Cheng, Y., Xie, Y., Honbo, D., Agrawal, A., Palsetia, D., Lee, K., Liao, W., Choudhary, A. (2011). “SES: Sentiment Elicitation System for Social Media Data.”, Proceedings of 11th International Conference on Data Mining Workhops (ICDMW), pp. 129 – 136.

Zhou, X.; Tao, X.; Yong, J.; Yang, Z. Sentiment analysis on tweets for social events. Computer Supported Cooperative Work in Design (CSCWD), 2013 IEEE 17th International Conference. Duda, R. O.; Hart, P. E.; e Stork, D. G. Patter Classification, Second Edtion, 2000. Editora TextBook.

Tsoumakas, G.; Katakis, I.; Vlahavas, I. P. (2010). Mining multi-label data. In O. Maimon, L. Rokach (Eds.) Data Mining and Knowledge Discovery handbook, (pp. 667-685). Heidelberg, Germany: Springer-Verlag, 2nd Ed.