Twitter US Airline Sentiment (CrowdFlower)
Twitter US Airline Sentiment, a CrowdFlower job on the problems of each major U.S. airline: 14,640 tweets about 6 airlines (United 3,822, US Airways 2,913, American 2,759, Southwest 2,420, Delta 2,222, Virgin America 504); the publisher says the tweets were scraped in February 2015. Contributors first classified each tweet as positive, negative or neutral (9,178 negative, 3,099 neutral, 2,363 positive) and then gave a reason for the negative ones (9,178 tweets have a reason, in 10 categories). Each row has the crowd job's unit ID, the sentiment and the reason with the confidence of each, the airline, the author's screen name, the tweet text, its retweet count and time, and the author's free-text location and time zone; 1,019 tweets carry coordinates. The package also holds the copy of the tweets from the folder named kaggle-v2 (Tweets.csv, 14,640 rows) as its own table.
Cite this dataset at
twitter-airline-sentiment.echo.directory
Analytics
The full graph has 8,270 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Topic 0
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
Topic 6
Topic 7
Topic 8
Topic 9
- negative9.2K (62.7%)
- neutral3.1K (21.2%)
- positive2.4K (16.1%)
Computed over the full corpus.
Record
- Catalog key
twitter-airline-sentiment- Authors
- CrowdFlower
- Year
- 2015
- Source paper
- Twitter US Airline Sentiment
- License
- CC BY 4.0 (Figure Eight 'Data for Everyone' library) (CC-BY-4.0)
- Records
- 14,640
- Nodes
- 8,270
- Edges
- 9,232 (directed)
- Nodes are
- account
- Edges are
- mention (author -> @mentioned account)
- Attributes
- nodes have no attributes; edges have attributes
- Text corpus
- tweet text
- Languages
- en
- Collected
- 2015-02-16 to 2015-02-24 (2010s)
- Tasks
- Text classification (multi-class;
airline_label, complaint_label) inferred, Sentiment classification (multi-class;sentiment_label) inferred
From the graph: link prediction, community detection - Region
- United States · North America (inferred: named in the description)
Provenance
- Originally published at
- https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment link checked
- Original files as received
- The source files, unconverted, kept for reference: mirrored on echo.directory on 2026-10-20, 6.8 MB across 2 files. Checksums in MANIFEST.json. For use, start with the data package below.
- Data package
- Version 1.0.0 of this dataset as an ECHO Data Package: posts (14,640 rows), annotations (29,280 rows), x_kaggle_tweets (14,640 rows).
- Structure
- Labelled data, Text
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@misc{crowdflower2015twitterusair,
title = {Twitter US Airline Sentiment},
author = {CrowdFlower},
year = {2015},
note = {Data for Everyone library, added 2015-02-12},
howpublished = {\url{https://www.figure-eight.com/data-for-everyone/}}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
CrowdFlower (2015). Twitter US Airline Sentiment (CrowdFlower) [Data set]. https://twitter-airline-sentiment.echo.directory/
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
CrowdFlower. Twitter US Airline Sentiment (CrowdFlower). 2015, twitter-airline-sentiment.echo.directory/.