Advertisement

Sentiment and Behavior Analysis of One Controversial American Individual on Twitter

  • J. Eliakin M. de Oliveira
  • Moshe Cotacallapa
  • Wilson Seron
  • Rafael D. C. dos Santos
  • Marcos G. Quiles
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9948)

Abstract

Social media is a convenient tool for expressing ideas and a powerful means for opinion formation. In this paper, we apply sentiment analysis and machine learning techniques to study a controversial American individual on Twitter., aiming to grasp temporal patterns of opinion changes and the geographical distribution of sentiments (positive, neutral or negative), in the American territory. Specifically, we choose the American TV presenter and candidate for the Republican party nomination, Donald J. Trump. The results acquired aim to elucidate some interesting points about the data, such as: what is the distribution of users considering a match between their sentiment and their relevance? Which clusters can we get from the temporal data of each state? How is the distribution of sentiments, before and after, the first two Republican party debates?

Keywords

Sentiment Analysis Presidential Candidate Twitter User Republican Party Positive Sentiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank the CNPq, CAPES, and FAPESP (Proc. 2011/18496-7), for financial support.

References

  1. 1.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment in short strength detection informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  2. 2.
    Prabowo, R., Thelwall, M.: Sentiment analysis: a combined approach. J. Informetrics 3(2), 143–157 (2009)CrossRefGoogle Scholar
  3. 3.
    Mishra, N., Jha, C.K.: Article: classification of opinion mining techniques. Int. J. Comput. Appl. 56(13), 1–6 (2012)Google Scholar
  4. 4.
    Stieglitz, S., Dang-Xuan, L.: Social media and political communication: a social media analytics framework. Soc. Netw. Anal. Min. 3, 1277–1291 (2012)CrossRefGoogle Scholar
  5. 5.
    Jungherr, A., Jürgens, P., Schoen, H.: Why the pirate party won the german election of 2009 or the trouble with predictions. Soc. Sci. Comput. Rev. 30(2), 229–234 (2012)CrossRefGoogle Scholar
  6. 6.
    Ringsquandl, M., Petkovic, D.: Analyzing political sentiment on Twitter. In: AAAI Spring Symposium: Analyzing Microtext (2013)Google Scholar
  7. 7.
    Seron, W., Zorzal, E., Quiles, M.G., Basgalupp, M.P., Breve, F.A.: #Worldcup2014 on Twitter. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9155, pp. 447–458. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  8. 8.
    Soelistio, Y.E., Surendra, M.R.S.: Simple text mining for sentiment analysis of political figure using naive bayes classifier method. CoRR abs/1508.05163 (2015)Google Scholar
  9. 9.
    Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time Twitter sentiment analysis of 2012 U.S. presidential election cycle. In: ACL 2012 System Demonstrations, ACL 2012, pp. 115–120 (2012)Google Scholar
  10. 10.
    Mejova, Y., Srinivasan, P., Boynton, B.: Gop primary season on Twitter: “popular” political sentiment in social media. In: Sixth ACM International Conference on Web Search and Data Mining, WSDM 2013, pp. 517–526 (2013)Google Scholar
  11. 11.
    Taheri, S., Mammadov, M., Bagirov, A.M.: Improving naive bayes classifier using conditional probabilities. In: Ninth Australasian Data Mining Conference, AusDM 2011, vol. 121, pp. 63–68 (2011)Google Scholar
  12. 12.
    Morstatter, F., Pfeffer, J., Liu, H., Carley, K.: Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose. In: International AAAI Conference on Weblogs and Social Media (2013)Google Scholar
  13. 13.
    Das, S., Chen, M.: Yahoo! for Amazon: extracting market sentiment from stock message boards. In: Asia Pacific Finance Association Annual Conference (APFA) (2001)Google Scholar
  14. 14.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: ACL-02 Conference on Empirical Methods in Natural Language Processing EMNLP, pp. 79–86 (2002)Google Scholar
  15. 15.
  16. 16.
    Jiang, L., Wang, D., Cai, Z., Yan, X.: Survey of improving naive bayes for classification. In: Alhajj, R., Gao, H., Li, X., Li, J., Zaïane, O.R. (eds.) ADMA 2007. LNCS (LNAI), vol. 4632, pp. 134–145. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • J. Eliakin M. de Oliveira
    • 1
  • Moshe Cotacallapa
    • 1
  • Wilson Seron
    • 2
  • Rafael D. C. dos Santos
    • 1
  • Marcos G. Quiles
    • 2
  1. 1.Instituto Nacional de Pesquisas Espaciais (INPE)São José dos CamposBrazil
  2. 2.Federal University of São Paulo (UNIFESP)São José dos CamposBrazil

Personalised recommendations