Ideology Classification of U.S. News through Sentiment. Political division is a major problem in the U.S. The Ideology of news providers widens this division. Classifying ideology through NLP is crucial.
I wanted to determine if news providers’ ideology can be objectively classified by performing some common natural language processing tasks on news articles.
But let’s start from the beginning…
Political division is a vital problem in the U.S. Nowadays, even what we eat, what we drive, and how we live is associated with either being Democrat or Republican. Decisions like wearing or not wearing a mask become part of political identity.
This political division’s foundation was laid when people with college degrees moved to the cities, and less-educated people stayed in rural areas in the 1970s. Jobs, technologies, and industries followed accordingly, and the cultural differences between Democrats and Republicans were amplified with the emergence of partisan media and social media networks .
If you think about it: People are exposed to different kinds of information. What people read and what people hear plays a vital role in how they see the world.
So if people are exposed to a news provider that is considered as “far-right” and “ideologically driven,” for example, Breitbart News , the world view of this news provider could have a notable influence on their reader’s world view.
Would I recognize the political ideology of a news provider by just reading some of their articles? And how about other people? Imagine reading “far-right” articles and thinking that this is considered “normal”…
This is why I started to think about how I can objectively classify news providers’ ideology.
Ultimately, I had the idea that Breitbart News would use, for example, more negative words when reporting about Joe Biden while using more positive comments for Trump than other news outlets.
Hence, articles written about a topic that agrees with a particular ideology would have a positive sentiment, while disagreeing issues would negatively affect sentiment.
_Using data from four news providers and applying sentiment and topic models, I will test if my hypotheses are true. If you’re interested in knowing how I conducted the analysis using Python and R, please look at my _GitHub repository. There you can find everything that you need.
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