---
language:
- en
license: cc
size_categories:
- 10K<n<100K
task_categories:
- text-classification
pretty_name: Tweets
tags:
- toxicity
dataset_info:
  features:
  - name: label
    dtype: int64
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 1621836
    num_examples: 14640
  download_size: 894257
  dataset_size: 1621836
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Tweets

## Overview 

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](https://data.world/crowdflower)
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:

- label
- text

```bash
{
  "label": 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

```python
from datasets import load_dataset

dataset = load_dataset("AiresPucrs/tweets", split='train')

```

## 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 4.0.