Dataset Card for tweet_eval

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.

Models trained or fine-tuned on tweet_eval

None yet