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Political orientation detection on Korean newspapers via sentence embedding and deep learning

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.