Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
toxicity
License:
File size: 1,948 Bytes
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---
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.
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