metadata

annotations_creators: []
language_creators:
  - found
languages:
  - en
licenses:
  - unknown
multilinguality:
  - monolingual
size_categories:
  emoji:
    - 100K<n<1M
  emotion:
    - 1K<n<10K
  hate:
    - 10K<n<100K
  irony:
    - 1K<n<10K
  offensive:
    - 10K<n<100K
  sentiment:
    - 10K<n<100K
  stance_abortion:
    - n<1K
  stance_atheism:
    - n<1K
  stance_climate:
    - n<1K
  stance_feminist:
    - n<1K
  stance_hillary:
    - n<1K
source_datasets:
  emoji:
    - extended|other-tweet-datasets
  emotion:
    - extended|other-tweet-datasets
  hate:
    - extended|other-tweet-datasets
  irony:
    - extended|other-tweet-datasets
  offensive:
    - extended|other-tweet-datasets
  sentiment:
    - extended|other-tweet-datasets
  stance_abortion:
    - extended|other-tweet-datasets
  stance_atheism:
    - extended|other-tweet-datasets
  stance_climate:
    - extended|other-tweet-datasets
  stance_feminist:
    - extended|other-tweet-datasets
  stance_hillary:
    - extended|other-tweet-datasets
task_categories:
  - text-classification
task_ids:
  emoji:
    - multi-class-classification
  emotion:
    - multi-class-classification
    - sentiment-classification
  hate:
    - intent-classification
  irony:
    - multi-class-classification
  offensive:
    - intent-classification
  sentiment:
    - multi-class-classification
    - sentiment-classification
  stance_abortion:
    - intent-classification
    - multi-class-classification
  stance_atheism:
    - intent-classification
    - multi-class-classification
  stance_climate:
    - intent-classification
    - multi-class-classification
  stance_feminist:
    - intent-classification
    - multi-class-classification
  stance_hillary:
    - intent-classification
    - multi-class-classification

Dataset Card for tweet_eval

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: [Needs More Information]
Repository: GitHub
Paper: EMNLP Paper
Leaderboard: GitHub Leaderboard
Point of Contact: [Needs More Information]

Dataset Summary

TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.

Supported Tasks and Leaderboards

text_classification: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.

Languages

The text in the dataset is in English, as spoken by Twitter users.

Dataset Structure

Data Instances

An instance from emoji config:

{'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user ️ ️ ️ @ Abbot Kinney, Venice'}

An instance from emotion config:

{'label': 2, 'text': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer.  #motivation #leadership #worry"}

An instance from hate config:

{'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on you…'}

An instance from irony config:

{'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'}

An instance from offensive config:

{'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'}

An instance from sentiment config:

{'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'}

An instance from stance_abortion config:

{'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'}

An instance from stance_atheism config:

{'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'}

An instance from stance_climate config:

{'label': 0, 'text': 'Why Is The Pope Upset?  via @user #UnzippedTruth #PopeFrancis #SemST'}

An instance from stance_feminist config:

{'label': 1, 'text': "@user @user is the UK's answer to @user and @user  #GamerGate #SemST"}

An instance from stance_hillary config:

{'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"}

Data Fields

For emoji config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: ❤

1: 😍

2: 😂

3: 💕

4: 🔥

5: 😊

6: 😎

7: ✨

8: 💙

9: 😘

10: 📷

11: 🇺🇸

12: ☀

13: 💜

14: 😉

15: 💯

16: 😁

17: 🎄

18: 📸

19: 😜

For emotion config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: anger

1: joy

2: optimism

3: sadness

For hate config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: non-hate

1: hate

For irony config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: non_irony

1: irony

For offensive config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: non-offensive

1: offensive

For sentiment config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: negative

1: neutral

2: positive

For stance_abortion config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: none

1: against

2: favor

For stance_atheism config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: none

1: against

2: favor

For stance_climate config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: none

1: against

2: favor

For stance_feminist config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: none

1: against

2: favor

For stance_hillary config:

text: a string feature containing the tweet.
label: an int classification label with the following mapping:

0: none

1: against

2: favor

Data Splits

name	train	validation	test
emoji	45000	5000	50000
emotion	3257	374	1421
hate	9000	1000	2970
irony	2862	955	784
offensive	11916	1324	860
sentiment	45615	2000	12284
stance_abortion	587	66	280
stance_atheism	461	52	220
stance_climate	355	40	169
stance_feminist	597	67	285
stance_hillary	620	69	295

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP.

Licensing Information

[Needs More Information]

Citation Information

@inproceedings{barbieri2020tweeteval,
title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
booktitle={Proceedings of Findings of EMNLP},
year={2020}
}

Contributions

Thanks to @gchhablani and @abhishekkrthakur for adding this dataset.

Datasets:

cardiffnlp
/

tweet_eval

Dataset Card for tweet_eval

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

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

Contributions