tweets {fastNaiveBayes}

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

Description

As the original source says,

Usage

tweets

Format

A data frame with 14640 rows and 2 columns

airline_sentiment

sentiment, as either 'negative','neutral','positive'

text

raw text, character

Details

"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 provided here is an altered version of the original source.

Source

https://www.figure-eight.com/data-for-everyone/