JunKeon Park, YeongYeon Na, and Il-Chul Moon. Text Augmented Automatic Statistician for Predicting Approval Rates of Politicians, IEEE Conference on System, Man, and Cybernetics 2017 (SMC 2017), Banff, Canada, Oct 5-8, 2017

Abstract

Predicting an approval rate of politicians is a popular task. While a type of prediction is using a text mining from news articles, we introduce a text augmented Gaussian process to perform the prediction with contexts. We test our model with 2017 South Korea Presidential Election in 1) a quantitative evaluation, and 2) a qualitative analysis. The performance of the model with text input is better than the performance of the model without the text input, which has been a typical approach of applying the Gaussian process. Moreover, the model can capture keywords which provide behind rational of the prediction result, which was not provided with only temporal information.

@INPROCEEDINGS{8122733, 

author={Park, JunKeon and Na, YeongYeon and Moon, Il-Chul}, 

booktitle={2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, 

title={Text augmented automatic statistician for predicting approval rates of politicians}, 

year={2017}, 

pages={954-959}, 

keywords={Kernel;Predictive models;Gaussian processes;Adaptation models;Voting;Twitter;Market research}, 

doi={10.1109/SMC.2017.8122733}

} 

Source Website:

https://ieeexplore.ieee.org/document/8122733