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Alabama faces battle at the ballot box; groups look to federal laws for protection; Israeli Cabinet votes to shut down Al Jazeera in the country; Florida among top states for children losing health coverage post-COVID; despite the increase, SD teacher salary one of the lowest in the country.

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Civil rights groups criticize police actions against student protesters, Republicans accuse Democrats of "buying votes" through student debt relief, and anti-abortion groups plan legal challenges to a Florida ballot referendum.

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Bidding begins soon for Wyoming's elk antlers, Southeastern states gained population in the past year, small rural energy projects are losing out to bigger proposals, and a rural arts cooperative is filling the gap for schools in Pennsylvania and West Virginia.

Make America Tweet Again

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Monday, November 7, 2016   

SALT LAKE CITY – Americans have learned a lot through Twitter about how Donald Trump feels about some people, but how do Twitter users feel about Donald Trump?

Computer scientists from the University of Utah have developed what they call sentiment analysis software that can determine how someone feels based on what he or she writes or says.

Feifei Li helped develop the program, and says it provides a real-time window into how the public is reacting to political events.

"What's cool is that you can actually adjust the lens of the window,” he states. “If you look at the last few months of data altogether, the sentiments for Democrats is stronger than the sentiments for Republicans. Given the recent outburst of email scandals, things might change a little bit."

Researchers say the biggest surge of positive tweets for Republicans came during their national convention, and again after a video was released of Trump boasting about sexually assaulting women. The peak for both positive and negative tweets for Democrats came on the heels of the final debates.

Li's team started with more than 250 million global tweets and used advanced software to identify 1.6 million political posts in the United States. Li notes the program was only able to detect the leanings of Twitter users, which are not reflective of the population at large.

The group's results suggest that if larger data streams can be tapped in future versions, today's pollsters could find themselves out of a job.

"We feel like if you collect enough data from the population, if you get an unbiased sample from the population using this data-driven approach, the results might be more accurate," he states.

The scientists used artificial intelligence and machine learning to help the program decipher complexities in human language. The group then compared its results with The New York Times' forecasts and found the state-by-state analysis was very similar.





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