New York Post Article Rating

Federal lands hold the key to unleashing a new American energy...

Dec 27, 2024 View Original Article
  • Bias Rating

    28% Somewhat Conservative

  • Reliability

    20% ReliablePoor

  • Policy Leaning

    -10% Center

  • Politician Portrayal

    44% Positive

Bias Score Analysis

The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.

Sentiments

Overall Sentiment

40% Positive

  •   Liberal
  •   Conservative
SentenceSentimentBias
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Bias Meter

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Bias Meter

Contributing sentiments towards policy:

91% : Trump's opportunity centers on his excellent picks for three key federal agencies: Doug Burgum at the Interior Department, Lee Zeldin at the Environmental Protection Agency and Chris Wright at the Energy Department.
84% : Donald Trump can prove that the answer is still yes -- by revolutionizing how America builds energy infrastructure.
67% : Trump's Cabinet secretaries at Interior, EPA and Energy could work in concert to designate abundance zones across the 640 million acres owned by the federal government.
62% : This three-pronged approach would open up huge swaths of land for energy infrastructure.
55% : At EPA, Zeldin could establish standardized environmental criteria for all projects within the zones, eliminating the need for project-by-project reviews and speeding up construction dramatically.
53% : The solution is to set aside designated zones on federal lands where innovators could quickly deploy energy infrastructure without fear of NEPA permitting delays or lawsuits.
44% : By creating American Abundance Zones, President Trump can not only usher in a brighter economic future, but restore Americans' belief that such a future is possible -- and worth pursuing, nationwide.

*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.

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