macildur/distilbert-base-uncasedv1-finetuned-twitter-sentiment
Text Classification
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Dataset Card for "sentiment140"
Dataset Summary
Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for sentiment classification. For more detailed information please refer to the paper.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
sentiment140
- Size of downloaded dataset files: 81.36 MB
- Size of the generated dataset: 225.82 MB
- Total amount of disk used: 307.18 MB
An example of 'train' looks as follows.
{
"date": "23-04-2010",
"query": "NO_QUERY",
"sentiment": 3,
"text": "train message",
"user": "train user"
}
Data Fields
The data fields are the same among all splits.
sentiment140
text: astringfeature.date: astringfeature.user: astringfeature.sentiment: aint32feature.query: astringfeature.
Data Splits
| name | train | test |
|---|---|---|
| sentiment140 | 1600000 | 498 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{go2009twitter,
title={Twitter sentiment classification using distant supervision},
author={Go, Alec and Bhayani, Richa and Huang, Lei},
journal={CS224N project report, Stanford},
volume={1},
number={12},
pages={2009},
year={2009}
}
Contributions
Thanks to @patrickvonplaten, @thomwolf for adding this dataset.
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