  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.
