And the Winner is ...: Bayesian Twitter-based Prediction on 2016 U.S. Presidential Election

Abstract

This paper describes a Naive-Bayesian predictive model for 2016 U.S. Presidential Election based on Twitter data. We use 33,708 tweets gathered since December 16, 2015 until February 29, 2016. We introduce a simpler data preprocessing method to label the data and train the model. The model achieves 95.8% accuracy on 10-fold cross validation and predicts Ted Cruz and Bernie Sanders as Republican and Democratic nominee respectively. It achieves a comparable result to those in its competitor methods.

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Cite this paper

@article{Tunggawan2016AndTW, title={And the Winner is ...: Bayesian Twitter-based Prediction on 2016 U.S. Presidential Election}, author={Elvyna Tunggawan and Yustinus Eko Soelistio}, journal={CoRR}, year={2016}, volume={abs/1611.00440} }