Skip to main content
SpringerLink
Log in

Social Security Reform in Brazil: A Twitter Sentiment Analysis

  • Conference paper
  • First Online:
Electronic Government and the Information Systems Perspective (EGOVIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12926))

  • 526 Accesses

Abstract

Brazil’s social security reform, approved in 2019, moved several social networks, revealing the high polarity of this theme. In this paper, we perform an automatic investigation of this polarity using sentiment analysis techniques. We collected 980,577 tweets published between January and November of that year and compared four supervised machine learning algorithms: Multinomial Naïve Bayes, Logistic Regression, Support Vector Machines, and Random Forest. Due to the large volume of data collected, we employed a transfer learning approach to train these algorithms. As a result, the four algorithms predominantly classified the tweets as “neutral”, that is, these posts do not explicitly expose their users’ opinion about the topic. Of the tweets that showed polarity, there was a dominance of posts classified as “positive”, i.e., their authors have positive feelings (support, affinity) concerning the Social Security Reform proposal.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Araújo, M., Gonçalves, P., Benevenuto, F.: Measuring sentiments in online social networks. In: Proceedings of the 19th Brazilian Symposium on Multimedia and the Web - WebMedia 2013. ACM Press (2013)

    Google Scholar 

  2. Bhattacharya, S., Yang, C., Srinivasan, P., Boynton, B.: Perceptions of presidential candidates’ personalities in twitter. J. Assoc. Inf. Sci. Technol. 67(2), 249–267 (2016)

    Article  Google Scholar 

  3. Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 440–447. Association for Computational Linguistics, Prague, Czech Republic, June 2007

    Google Scholar 

  4. Bosch, T.: Twitter activism and youth in South Africa: the case of #RhodesMustFall. Inf. Commun. Soc. 20(2), 221–232 (2016)

    Article  Google Scholar 

  5. Bovet, A., Makse, H.A.: Influence of fake news in Twitter during the 2016 US presidential election. Nat. Commun. 10(1), 1–14 (2019)

    Article  Google Scholar 

  6. Budiharto, W., Meiliana, M.: Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis. J. Big Data 5(1), 1–10 (2018)

    Article  Google Scholar 

  7. Caetano, J.A., Lima, H.S., Santos, M.F., Marques-Neto, H.T.: Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election. J. Internet Serv. Appl. 9(18), 1–15 (2018)

    Google Scholar 

  8. Giachanou, A., Crestani, F.: Like it or not: a survey of Twitter sentiment analysis methods. ACM Comput. Surv. 49(2), 1–41 (2016)

    Article  Google Scholar 

  9. Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the First ACM Conference on Online Social Networks - COSN 2013, pp. 27–38. ACM Press, Boston (2013)

    Google Scholar 

  10. Guerra, P.H.C., Veloso, A., Meira, W., Almeida, V.: From bias to opinion: a transfer-learning approach to real-time sentiment analysis. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2011, pp. 150–158. KDD 2011. ACM Press, New York, NY, USA (2011)

    Google Scholar 

  11. Guimarães, E., Carvalho, J., Paes, A., Plastino, A.: Transfer learning for Twitter sentiment analysis: choosing an effective source dataset. In: Proceedings of 8th Symposium on Knowledge Discovery, Mining and Learning, pp. 161–168 (2020)

    Google Scholar 

  12. Hasan, A., Moin, S., Karim, A., Shamshirband, S.: Machine learning-based sentiment analysis for twitter accounts. Math. Comput. Appl. 23(1), 11 (2018)

    Google Scholar 

  13. Kralj Novak, P., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. PLoS One 10(12), e0144296 (2015)

    Article  Google Scholar 

  14. Kušen, E., Strembeck, M.: Politics, sentiments, and misinformation: an analysis of the Twitter discussion on the 2016 Austrian Presidential elections. Online Soc. Networks Media 5, 37–50 (2018)

    Article  Google Scholar 

  15. Liu, R., Shi, Y., Ji, C., Jia, M.: A survey of sentiment analysis based on transfer learning. IEEE Access 7, 85401–85412 (2019)

    Article  Google Scholar 

  16. Mottl, D.: GetOldTweets3: Project description. The Python Package Index, November 2019. https://pypi.org/project/GetOldTweets3/

  17. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  18. Pan, W., Zhong, E., Yang, Q.: Transfer learning for text mining. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 223–257. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_7

    Chapter  Google Scholar 

  19. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  20. Rajaraman, A., Ullman, J.D.: Data mining. In: Rajaraman, A., Ullman, J.D. (eds.) Mining of Massive Datasets, vol. 1, pp. 1–17. Cambridge University Press, Cambridge (2011)

    Chapter  Google Scholar 

  21. Resende, T.: Senado finaliza nesta quarta reforma da Previdência que pode afetar 72 milhões. Folha de São Paulo, October 2019. https://bit.ly/3qDU40e. (in Portuguese)

  22. Rossetto, G.P.N., Carreiro, R., Almada, M.P.: Twitter e comunicação política: limites e possibilidades. Compolítica 3(2), 189–216 (2013). (in Portuguese)

    Article  Google Scholar 

  23. Santana, F.: Análise de Sentimentos Utilizando Dados do Twitter. Minerando Dados (2019). https://minerandodados.com.br

  24. Stein, T.: Gírias e abreviações mais usadas do Whatsapp. Dicionário Polular (2019). https://www.dicionariopopular.com/girias-abreviacoes-siglas-mais-usadas-do-whatsapp/. (in Portuguese)

  25. Theocharis, Y., Lowe, W., van Deth, J.W., García-Albacete, G.: Using Twitter to mobilize protest action: online mobilization patterns and action repertoires in the Occupy Wall Street, Indignados, and Aganaktismenoi movements. Inf. Commun. Soc. 18(2), 202–220 (2014)

    Article  Google Scholar 

  26. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Cohen, W.W., Gosling, S. (eds.) Proceedings of the Fourth International Conference on Weblogs and Social Media. The AAAI Press (2010)

    Google Scholar 

  27. Yang, L., Zhang, J.: Automatic transfer learning for short text mining. EURASIP J. Wirel. Commun. Networking 2017(1), 1–18 (2017)

    Article  Google Scholar 

  28. Yaqub, U., Chun, S.A., Atluri, V., Vaidya, J.: Analysis of political discourse on twitter in the context of the 2016 US presidential elections. Gov. Inf. Q. 34(4), 613–626 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Federal University of Uberlândia, Av. João Naves de Ávila, 2.121, Uberlândia, MG, Brazil

    Rafael D. Ricci, Elaine R. Faria, Rodrigo S. Miani & Paulo H. R. Gabriel

Authors
  1. Rafael D. Ricci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elaine R. Faria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rodrigo S. Miani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paulo H. R. Gabriel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paulo H. R. Gabriel .

Editor information

Editors and Affiliations

  1. Corvinus University Budapest, Budapest, Hungary

    Andrea Kö

  2. National Research Council and European Parliament, Florence, Italy

    Enrico Francesconi

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ricci, R.D., Faria, E.R., Miani, R.S., Gabriel, P.H.R. (2021). Social Security Reform in Brazil: A Twitter Sentiment Analysis. In: Kö, A., Francesconi, E., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2021. Lecture Notes in Computer Science(), vol 12926. Springer, Cham. https://doi.org/10.1007/978-3-030-86611-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86611-2_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86610-5

  • Online ISBN: 978-3-030-86611-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation