|
--- |
|
annotations_creators: |
|
- found |
|
language_creators: |
|
- found |
|
language: |
|
- en |
|
license: |
|
- unknown |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
- 100K<n<1M |
|
- 10K<n<100K |
|
- 1K<n<10K |
|
- n<1K |
|
source_datasets: |
|
- extended|other-tweet-datasets |
|
task_categories: |
|
- text-classification |
|
task_ids: |
|
- intent-classification |
|
- multi-class-classification |
|
- sentiment-classification |
|
paperswithcode_id: tweeteval |
|
pretty_name: TweetEval |
|
train-eval-index: |
|
- config: emotion |
|
task: text-classification |
|
task_id: multi_class_classification |
|
splits: |
|
train_split: train |
|
eval_split: test |
|
col_mapping: |
|
text: text |
|
label: target |
|
metrics: |
|
- type: accuracy |
|
name: Accuracy |
|
- type: f1 |
|
name: F1 macro |
|
args: |
|
average: macro |
|
- type: f1 |
|
name: F1 micro |
|
args: |
|
average: micro |
|
- type: f1 |
|
name: F1 weighted |
|
args: |
|
average: weighted |
|
- type: precision |
|
name: Precision macro |
|
args: |
|
average: macro |
|
- type: precision |
|
name: Precision micro |
|
args: |
|
average: micro |
|
- type: precision |
|
name: Precision weighted |
|
args: |
|
average: weighted |
|
- type: recall |
|
name: Recall macro |
|
args: |
|
average: macro |
|
- type: recall |
|
name: Recall micro |
|
args: |
|
average: micro |
|
- type: recall |
|
name: Recall weighted |
|
args: |
|
average: weighted |
|
- config: hate |
|
task: text-classification |
|
task_id: binary_classification |
|
splits: |
|
train_split: train |
|
eval_split: test |
|
col_mapping: |
|
text: text |
|
label: target |
|
metrics: |
|
- type: accuracy |
|
name: Accuracy |
|
- type: f1 |
|
name: F1 binary |
|
args: |
|
average: binary |
|
- type: precision |
|
name: Precision macro |
|
args: |
|
average: macro |
|
- type: precision |
|
name: Precision micro |
|
args: |
|
average: micro |
|
- type: precision |
|
name: Precision weighted |
|
args: |
|
average: weighted |
|
- type: recall |
|
name: Recall macro |
|
args: |
|
average: macro |
|
- type: recall |
|
name: Recall micro |
|
args: |
|
average: micro |
|
- type: recall |
|
name: Recall weighted |
|
args: |
|
average: weighted |
|
- config: irony |
|
task: text-classification |
|
task_id: binary_classification |
|
splits: |
|
train_split: train |
|
eval_split: test |
|
col_mapping: |
|
text: text |
|
label: target |
|
metrics: |
|
- type: accuracy |
|
name: Accuracy |
|
- type: f1 |
|
name: F1 binary |
|
args: |
|
average: binary |
|
- type: precision |
|
name: Precision macro |
|
args: |
|
average: macro |
|
- type: precision |
|
name: Precision micro |
|
args: |
|
average: micro |
|
- type: precision |
|
name: Precision weighted |
|
args: |
|
average: weighted |
|
- type: recall |
|
name: Recall macro |
|
args: |
|
average: macro |
|
- type: recall |
|
name: Recall micro |
|
args: |
|
average: micro |
|
- type: recall |
|
name: Recall weighted |
|
args: |
|
average: weighted |
|
- config: offensive |
|
task: text-classification |
|
task_id: binary_classification |
|
splits: |
|
train_split: train |
|
eval_split: test |
|
col_mapping: |
|
text: text |
|
label: target |
|
metrics: |
|
- type: accuracy |
|
name: Accuracy |
|
- type: f1 |
|
name: F1 binary |
|
args: |
|
average: binary |
|
- type: precision |
|
name: Precision macro |
|
args: |
|
average: macro |
|
- type: precision |
|
name: Precision micro |
|
args: |
|
average: micro |
|
- type: precision |
|
name: Precision weighted |
|
args: |
|
average: weighted |
|
- type: recall |
|
name: Recall macro |
|
args: |
|
average: macro |
|
- type: recall |
|
name: Recall micro |
|
args: |
|
average: micro |
|
- type: recall |
|
name: Recall weighted |
|
args: |
|
average: weighted |
|
- config: sentiment |
|
task: text-classification |
|
task_id: multi_class_classification |
|
splits: |
|
train_split: train |
|
eval_split: test |
|
col_mapping: |
|
text: text |
|
label: target |
|
metrics: |
|
- type: accuracy |
|
name: Accuracy |
|
- type: f1 |
|
name: F1 macro |
|
args: |
|
average: macro |
|
- type: f1 |
|
name: F1 micro |
|
args: |
|
average: micro |
|
- type: f1 |
|
name: F1 weighted |
|
args: |
|
average: weighted |
|
- type: precision |
|
name: Precision macro |
|
args: |
|
average: macro |
|
- type: precision |
|
name: Precision micro |
|
args: |
|
average: micro |
|
- type: precision |
|
name: Precision weighted |
|
args: |
|
average: weighted |
|
- type: recall |
|
name: Recall macro |
|
args: |
|
average: macro |
|
- type: recall |
|
name: Recall micro |
|
args: |
|
average: micro |
|
- type: recall |
|
name: Recall weighted |
|
args: |
|
average: weighted |
|
configs: |
|
- emoji |
|
- emotion |
|
- hate |
|
- irony |
|
- offensive |
|
- sentiment |
|
- stance_abortion |
|
- stance_atheism |
|
- stance_climate |
|
- stance_feminist |
|
- stance_hillary |
|
--- |
|
|
|
# Dataset Card for tweet_eval |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** [Needs More Information] |
|
- **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval) |
|
- **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf) |
|
- **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval) |
|
- **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 |
|
|
|
This is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions). |
|
|
|
All of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy) |
|
|
|
Additionally the license are: |
|
- emoji: Undefined |
|
- emotion(EmoInt): Undefined |
|
- hate (HateEval): Need permission [here](http://hatespeech.di.unito.it/hateval.html) |
|
- irony: Undefined |
|
- Offensive: Undefined |
|
- Sentiment: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ) |
|
- Stance: Undefined |
|
|
|
|
|
### 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} |
|
} |
|
``` |
|
|
|
If you use any of the TweetEval datasets, please cite their original publications: |
|
|
|
#### Emotion Recognition: |
|
``` |
|
@inproceedings{mohammad2018semeval, |
|
title={Semeval-2018 task 1: Affect in tweets}, |
|
author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana}, |
|
booktitle={Proceedings of the 12th international workshop on semantic evaluation}, |
|
pages={1--17}, |
|
year={2018} |
|
} |
|
|
|
``` |
|
#### Emoji Prediction: |
|
``` |
|
@inproceedings{barbieri2018semeval, |
|
title={Semeval 2018 task 2: Multilingual emoji prediction}, |
|
author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and |
|
Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio}, |
|
booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, |
|
pages={24--33}, |
|
year={2018} |
|
} |
|
``` |
|
|
|
#### Irony Detection: |
|
``` |
|
@inproceedings{van2018semeval, |
|
title={Semeval-2018 task 3: Irony detection in english tweets}, |
|
author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique}, |
|
booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, |
|
pages={39--50}, |
|
year={2018} |
|
} |
|
``` |
|
|
|
#### Hate Speech Detection: |
|
``` |
|
@inproceedings{basile-etal-2019-semeval, |
|
title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter", |
|
author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and |
|
Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela", |
|
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation", |
|
year = "2019", |
|
address = "Minneapolis, Minnesota, USA", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://www.aclweb.org/anthology/S19-2007", |
|
doi = "10.18653/v1/S19-2007", |
|
pages = "54--63" |
|
} |
|
``` |
|
#### Offensive Language Identification: |
|
``` |
|
@inproceedings{zampieri2019semeval, |
|
title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)}, |
|
author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh}, |
|
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation}, |
|
pages={75--86}, |
|
year={2019} |
|
} |
|
``` |
|
|
|
#### Sentiment Analysis: |
|
``` |
|
@inproceedings{rosenthal2017semeval, |
|
title={SemEval-2017 task 4: Sentiment analysis in Twitter}, |
|
author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav}, |
|
booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)}, |
|
pages={502--518}, |
|
year={2017} |
|
} |
|
``` |
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|
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#### Stance Detection: |
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``` |
|
@inproceedings{mohammad2016semeval, |
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title={Semeval-2016 task 6: Detecting stance in tweets}, |
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author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin}, |
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booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)}, |
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pages={31--41}, |
|
year={2016} |
|
} |
|
``` |
|
|
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### Contributions |
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|
|
Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
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|
