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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 502-504
Won-Tae Joo , Department of Computer Science, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea (South)
Young-Seob Jeong , Department of Computer Science, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea (South)
KyoJoong Oh , Department of Computer Science, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea (South)
ABSTRACT
In Korea, authors of the newspaper article tend to express their intention indirectly, that is, they choose a method to leave out some important facts, or sometimes uses biased terms to support their opinion. Since they're not expressing their opinion directly, detecting the political bias is a difficult task. In this paper, we propose a method to detect political bias in the Korean articles by first building word vectors and sentence vectors, and second do a DBN-Training with those vectors and finally do a regression with SVM to calculate the bias. We used our own dataset which is scored with the political bias before doing the regression.
INDEX TERMS
Support vector machines, Training, Presses, Pipelines, Sentiment analysis, Machine learning, Computer science
CITATION

Won-Tae Joo, Y. Jeong and KyoJoong Oh, "Political orientation detection on Korean newspapers via sentence embedding and deep learning," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 502-504.
doi:10.1109/BIGCOMP.2016.7425979
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