Abstract
In this paper, we analyze weather social sentiments can be utilized for the prediction of election results. In particular, we analyze Twitter sentiments about Brexit and United Kingdom (UK) politicians. Twitter is the essential social network for sentiments analyzing and it provides useful information for mining data. Through periods, we collected Twitter data about Brexit and UK politicians using Twitter Application Program interface (API). First, we cleaned and pre-processed Tweet data for sentiment analysis. Then, we create a Twitter search and sentiment visualization interface using python. Python provides useful libraries for sentiment analysis and graphical presentations. Finally, we analyze the changing opinions about Brexit and UK politicians using sentiments. In particular, in advance, we were able to correctly predict the UK parliament voting results in January 2019. In this paper, we discuss Twitter data collection, Twitter sentiment search/visualization interface and detailed sentiment analysis results about Brexit and UK politicians.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
The Statistical portal. https://www.statista.com/statistics/274564/monthly-active-twitter-users-in-the-united-states
Twitter via SMS FAQ. Archived 6 April 2012, at the Wayback Machine. Accessed 13 Apr 2012
Kuhlman, D.: A Python Book: Beginning Python, Advanced Python, and Python Exercises. Archived from the original on 23 June 2012
Textblob. https://media.readthedocs.org/pdf/textblob/dev/textblob.pdf
The Matplotlib development team. https://matplotlib.org/tutorials/index.html#introductory
Dhar, V.: Data science and prediction. Commun. ACM 56(12), 64 (2013). https://doi.org/10.1145/2500499
Tansley, S., Tolle, K.M.: The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research (2009). ISBN 978-0-9825442-0-4
Mak, W.: https://data.world/wwymak/uk-election-tweets-2017-may-3/workspace/file?filename=2017-5-31.csv
Obar, J.A., Wildman, S.: Social media definition and the governance challenge: an introduction to the special issue. Telecommun. Policy 39(9), 745–750 (2015). https://doi.org/10.1016/j.telpol.2015.07.014.SSRN2647377
The definitive history of social media. The Daily Dot, 11 September 2016. Accessed 5 Feb 2018
Gilbertson, S.: Twitter Vulnerability: Spoof Caller ID to Take over Any Account. Wired, 11 June 2007. Accessed 5 Feb 2011
Twitter/OpenSource. Twitter.com. Archived from the original on 15 April 2013. Accessed 18 Apr 2013
Xi, H., Scott, D.: Dependent types in practical programming. In: Proceedings of ACM SIGPLAN Symposium on Principles of Programming Languages, pp. 214–227. ACM Press (1998). CiteSeerX 10.1.1.41.548
Moujahid, A., 21 April 2014. http://adilmoujahid.com/posts/2014/07/twitter-analytics/
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media Inc., Sebastopol (2009)
Jaidka, K., Ahmed, S., Skoric, M., Hilbert, M.: Predicting elections from social media: a three-country, three-method comparative study. Asian J. Commun. 29(3), 252–273 (2018). https://doi.org/10.1080/01292986.2018.1453849
Bermingham, A., Smeaton, A.F.: On using Twitter to monitor political sentiment and predict election results. In: Sentiment Analysis Where AI Meets Psychology (SAAIP), pp. 2–10. ACL, Chiang Mai (2011)
Livne, A., Simmons, M.P., Adar, E., Adamic, L.A.: The party is over here: structure and content in the 2010 election. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, pp. 17–21. Association for the Advancement of Artificial Intelligence, Barcelona (2011)
Boutet, A., Kim, H., Yoneki, E.: What’s in your tweets? I know who you supported in the UK 2010 general election. In: Proceedings of the International AAAI Conference on We Blogs and Social Media, pp. 411–414. Association for the Advancement of Artificial Intelligence, Dublin (2012)
Bovet, A., Morone, F., Makse, H.A.: Predicting election trends with Twitter: Hillary Clinton versus Donald Trump (2016). arXiv preprint arXiv:1610.01587
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chandio, M.M., Sah, M. (2020). Brexit Twitter Sentiment Analysis: Changing Opinions About Brexit and UK Politicians. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-38501-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38500-2
Online ISBN: 978-3-030-38501-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)