---
language:
- en
task_categories:
- text-classification
---
## Tweets

The dataset is a smaller version of the original dataset [This data originally came from Crowdflower's Data for Everyone library](http://www.crowdflower.com/data-for-everyone)

The original Twitter data was scraped from February 2015, and contributors were asked first to classify positive, negative[, and neutral tweets,

followed by categorizing negative reasons (such as "late flight" or "rude service").

This version contains whether the sentiment of the tweets in this set was positive (16%), neutral (21%), or negative (63%) for six US airlines.


## Dataset Details
Dataset Name: [Twitter US Airline Sentiment](https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment)

Language:  English

Total Size: 14,640  demonstrations 

## Contents

The dataset consists of a data frame with the following columns:

- airline_sentiment

- text

{
"airline_sentiment": "negative[0]",

"text":"virginamerica why are your first fares in may over three times more than other carriers when all seats are available to select.",
}

## How to use
```
import pandas as pd
from datasets import load_dataset

dataset = load_dataset("AiresPucrs/tweets")

df = dataset.to_pandas()

```

## License

The Twitter US Airline Sentiment is licensed under the [Creative Commons(CC)](https://creativecommons.org/licenses/by-nc-sa/4.0/) License CC BY-NC-SA 4.09.