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

2016/11/02 by Tunggawan, Elvyna, Soelistio, Yustinus Eko
#Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Social and Information Networks (cs.SI)

paper · doi:10.48550/arxiv.1611.00440

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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