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http://dx.doi.org/10.9708/jksci.2018.23.01.009

Political Opinion Mining from Article Comments using Deep Learning  

Sung, Dae-Kyung (Dept. of Computer Science and Engineering, Kyungpook National University)
Jeong, Young-Seob (Dept. of Big Data Engineering, Soonchunhyang University)
Publication Information
Journal of the Korea Society of Computer and Information / v.23, no.1, 2018 , pp. 9-15 More about this Journal
Abstract
Policy polls, which investigate the degree of support that the policy has for policy implementation, play an important role in making decisions. As the number of Internet users increases, the public is actively commenting on their policy news stories. Current policy polls tend to rely heavily on phone and offline surveys. Collecting and analyzing policy articles is useful in policy surveys. In this study, we propose a method of analyzing comments using deep learning technology showing outstanding performance in various fields. In particular, we designed various models based on the recurrent neural network (RNN) which is suitable for sequential data and compared the performance with the support vector machine (SVM), which is a traditional machine learning model. For all test sets, the SVM model show an accuracy of 0.73 and the RNN model have an accuracy of 0.83.
Keywords
recurrent neural network; opinion mining; semantic analysis;
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Times Cited By KSCI : 2  (Citation Analysis)
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