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

This dataset contains texts from customers posted on Twitter regarding their air travel experiences,
whether they were upset, neutral, or satisfied with the trip and the airline's service.

## Dataset Details

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 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.

---