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?
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
CP in WashingtonCP means City Press.
- 4.
chrislhayes and CGasparino are journalists; mckaycoppins is a political writer.
- 5.
- 6.
- 7.
- 8.
via NBC News: http://nbcnews.to/1GXruPb.
- 9.
References
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)
Prabowo, R., Thelwall, M.: Sentiment analysis: a combined approach. J. Informetrics 3(2), 143–157 (2009)
Mishra, N., Jha, C.K.: Article: classification of opinion mining techniques. Int. J. Comput. Appl. 56(13), 1–6 (2012)
Stieglitz, S., Dang-Xuan, L.: Social media and political communication: a social media analytics framework. Soc. Netw. Anal. Min. 3, 1277–1291 (2012)
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)
Ringsquandl, M., Petkovic, D.: Analyzing political sentiment on Twitter. In: AAAI Spring Symposium: Analyzing Microtext (2013)
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)
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)
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)
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)
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)
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)
Das, S., Chen, M.: Yahoo! for Amazon: extracting market sentiment from stock message boards. In: Asia Pacific Finance Association Annual Conference (APFA) (2001)
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)
Potts, C.: http://sentiment.christopherpotts.net/lingstruc.html. Accessed 09 Dec 2015
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)
Acknowledgments
The authors would like to thank the CNPq, CAPES, and FAPESP (Proc. 2011/18496-7), for financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
de Oliveira, J.E.M., Cotacallapa, M., Seron, W., dos Santos, R.D.C., Quiles, M.G. (2016). Sentiment and Behavior Analysis of One Controversial American Individual on Twitter. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-46672-9_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46671-2
Online ISBN: 978-3-319-46672-9
eBook Packages: Computer ScienceComputer Science (R0)