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

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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.
Publisher
Korean Institute of Information Scientists and Engineers (KIISE)
Issue Date
2016-01-18
Language
English
Citation

The 3rd International Conference on Big Data and Smart Computing (BigComp2016), pp.502 - 504

URI
http://hdl.handle.net/10203/210144
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