This data originally came from Crowdflower's Data for Everyone library.

As the original source says,

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").

The data we're providing  is a slightly reformatted version of the original source. It includes both a CSV file and SQLite database. The code that does these transformations is available on GitHub

For example, it contains whether the sentiment of the tweets in this set was positive, neutral, or negative for six US airlines: