{"id":1344,"date":"2016-11-02T19:36:11","date_gmt":"2016-11-02T19:36:11","guid":{"rendered":"http:\/\/www.rsoftsolution.com\/blog\/make-america-tweet-again.html"},"modified":"2016-11-02T19:36:11","modified_gmt":"2016-11-02T19:36:11","slug":"make-america-tweet-again","status":"publish","type":"post","link":"https:\/\/www.rbsitsoftwaresolution.in\/blog\/make-america-tweet-again.html","title":{"rendered":"Make America tweet again"},"content":{"rendered":"
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\n A screenshot from estorm.org showing Twitter sentiment toward or against the major presidential candidates. Credit: estorm.org
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We know how Donald Trump feels about everyone through Twitter, but how do Twitter users feel about Donald Trump?\n <\/p>\n


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Computer scientists from the University of Utah’s College of Engineering have developed what they call “sentiment analysis” software that can automatically determine how someone feels based on what they write or say. To test out the accuracy of this software’s machine-learning model, the team used it to analyze the individual sentiments of more than 1.6 million (and counting) geo-tagged tweets<\/a> about the U.S. presidential election over the last five months. A database of these tweets is then examined to determine whether states and their counties are leaning toward the Republicans or Democrats.<\/p>\n

“With sentiment analysis, it will try to predict the emotions behind every human being when he or she is talking or writing something,” says Debjyoti Paul, a doctoral student in the University of Utah’s School of Computing and the project leader along with School of Computing associate professor Feifei Li. “With that in mind, we are not just trying to look at the information in the tweets. We are trying to incorporate the emotion with the information.”<\/p>\n

As a result of their work, the team has created an interactive website at http:\/\/www.estorm.org<\/a> in which users can find out if the tweets coming out of their state and its counties are more positive or negative toward Republicans or Democrats during any defined period of time since June 5. Also, the data can tell you the percentage of both positive and negative tweets toward a political party and when there was a surge for a particular type of tweet in the last five months.<\/p>\n

Some interesting facts about this year’s U.S. presidential election based on a sample of what people are tweeting:<\/p>\n