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Analyze how travelers in February 2015 expressed their feelings on Twitter.
A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").
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Nice work. Perhaps you'd want to weight the twitter counts with the passenger numbers to avoid sampling bias?