Tough votes on Trump Cabinet picks could pose big risks for midterm Senate candidates
- Bias Rating
8% Center
- Reliability
40% ReliableFair
- Policy Leaning
50% Medium Conservative
- Politician Portrayal
-30% Negative
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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
-2% Negative
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
36% : Trump won a comprehensive victory, and before he announced his more controversial cabinet picks, Republicans on Capitol Hill were touting the need for unity as they prepared to take over a unified government in January.32% : Those running in purple districts could opt to punt, noting their lack of a vote, or they could knock the more controversial contenders, also risking blowback from Trump.
28% : Still, underscoring the catch-22, any votes for nominees deemed too controversial could end up in ads from their ultimate Democratic opponents labeling them a "rubber stamp" for Trump, the source warned.
28% : But going too far risks turning off Democrats, a major risk when they'll need every single supporter energized in states Trump won this month.
14% : And with a base in such lockstep with Trump, any defiance from other GOP lawmakers could trigger outrage from the famously mercurial president-elect -- fury that could in turn result in a primary challenge, and not just for swing-staters like Tillis.
*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.