Text Augmented Automatic Statistician for Predicting Approval Rates of Politicians

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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.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2017-10-05
Language
English
Citation

2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, pp.954 - 959

DOI
10.1109/SMC.2017.8122733
URI
http://hdl.handle.net/10203/273658
Appears in Collection
IE-Conference Papers(학술회의논문)
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