Datasets:
language:
- en
task_categories:
- text-classification
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 datase. This data originally came from Crowdflower's Data for Everyone library 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
- 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
from datasets import load_dataset
dataset = load_dataset("AiresPucrs/tweets")
License
The Twitter US Airline Sentiment is licensed under the Creative Commons(CC) License CC BY-NC-SA 4.09.