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Developing a Natural Language Understanding Model to Characterize Cable News Bias
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This story was originally published on HackerNoon at: https://hackernoon.com/developing-a-natural-language-understanding-model-to-characterize-cable-news-bias.
The increasing trend of political polarization in the U.S. is reflected in media consumption patterns that indicate partisan polarization.
Check more stories related to media at: https://hackernoon.com/c/media. You can also check exclusive content about #media, #media-bias-analysis, #media-bias-in-the-usa, #cable-news-bias, #stance-analysis, #natural-language-processing, #political-polarization, #bias-in-the-news, and more.
This story was written by: @mediabias. Learn more about this writer by checking @mediabias's about page, and for more stories, please visit hackernoon.com.
The increasing trend of political polarization in the U.S. is reflected in media consumption patterns that indicate partisan polarization. We develop an unsupervised machine learning method to characterize the bias of cable news programs without any human input. This method relies on the analysis of what topics are mentioned through Named Entity Recognition and how those topics are discussed through Stance Analysis.
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