https://github.com/q-maze/exploringthenews
Exploratory text analytics project to examine cultural trends through analysis of 2016-2017 news articles.
https://github.com/q-maze/exploringthenews
news nltk-python sentiment-analysis sentiment-classification word-em
Last synced: 26 days ago
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Exploratory text analytics project to examine cultural trends through analysis of 2016-2017 news articles.
- Host: GitHub
- URL: https://github.com/q-maze/exploringthenews
- Owner: q-maze
- Created: 2021-12-13T17:04:27.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-27T14:23:34.000Z (over 3 years ago)
- Last Synced: 2024-07-09T00:54:36.665Z (10 months ago)
- Topics: news, nltk-python, sentiment-analysis, sentiment-classification, word-em
- Language: HTML
- Homepage:
- Size: 4.86 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ExploringTheNews
This project's Jupyter notebook is hosted here as a GitHub page: https://q-maze.github.io/ExploringTheNews/
Beginning with the 2016 United States presidential election and continuing through the 2020 presidential election and current COVID-19 pandemic, the United States political landscape has become increasingly polarized. This source of this polarization is attributed by many to real and perceived biases of the news media. This project aimed to explore this cultural trend through anlysis of articles published by three sources, The Guardian, PowerLine, and Daily Kos over the time period of mid-2016 to the end of 2017. The analysis techniques utilzed include sentiment analysis, word embeddings, and topic modelling.