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
config_names:
- emoji
- emotion
- hate
- irony
- offensive
- sentiment
- stance_abortion
- stance_atheism
- stance_climate
- stance_feminist
- stance_hillary
dataset_info:
- config_name: emoji
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': β€
'1': π
'2': π
'3': π
'4': π₯
'5': π
'6': π
'7': β¨
'8': π
'9': π
'10': π·
'11': πΊπΈ
'12': β
'13': π
'14': π
'15': π―
'16': π
'17': π
'18': πΈ
'19': π
splits:
- name: train
num_bytes: 3803167
num_examples: 45000
- name: test
num_bytes: 4255901
num_examples: 50000
- name: validation
num_bytes: 396079
num_examples: 5000
download_size: 5939308
dataset_size: 8455147
- config_name: emotion
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': anger
'1': joy
'2': optimism
'3': sadness
splits:
- name: train
num_bytes: 338871
num_examples: 3257
- name: test
num_bytes: 146645
num_examples: 1421
- name: validation
num_bytes: 38273
num_examples: 374
download_size: 367016
dataset_size: 523789
- config_name: hate
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-hate
'1': hate
splits:
- name: train
num_bytes: 1223650
num_examples: 9000
- name: test
num_bytes: 428934
num_examples: 2970
- name: validation
num_bytes: 154144
num_examples: 1000
download_size: 1196346
dataset_size: 1806728
- config_name: irony
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non_irony
'1': irony
splits:
- name: train
num_bytes: 259187
num_examples: 2862
- name: test
num_bytes: 75897
num_examples: 784
- name: validation
num_bytes: 86017
num_examples: 955
download_size: 297647
dataset_size: 421101
- config_name: offensive
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-offensive
'1': offensive
splits:
- name: train
num_bytes: 1648061
num_examples: 11916
- name: test
num_bytes: 135473
num_examples: 860
- name: validation
num_bytes: 192417
num_examples: 1324
download_size: 1234528
dataset_size: 1975951
- config_name: sentiment
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
splits:
- name: train
num_bytes: 5425122
num_examples: 45615
- name: test
num_bytes: 1279540
num_examples: 12284
- name: validation
num_bytes: 239084
num_examples: 2000
download_size: 4849675
dataset_size: 6943746
- config_name: stance_abortion
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 68694
num_examples: 587
- name: test
num_bytes: 33171
num_examples: 280
- name: validation
num_bytes: 7657
num_examples: 66
download_size: 73517
dataset_size: 109522
- config_name: stance_atheism
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
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num_examples: 461
- name: test
num_bytes: 25716
num_examples: 220
- name: validation
num_bytes: 6320
num_examples: 52
download_size: 62265
dataset_size: 86811
- config_name: stance_climate
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 40249
num_examples: 355
- name: test
num_bytes: 19925
num_examples: 169
- name: validation
num_bytes: 4801
num_examples: 40
download_size: 48493
dataset_size: 64975
- config_name: stance_feminist
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 70509
num_examples: 597
- name: test
num_bytes: 33305
num_examples: 285
- name: validation
num_bytes: 8035
num_examples: 67
download_size: 76345
dataset_size: 111849
- config_name: stance_hillary
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': against
'2': favor
splits:
- name: train
num_bytes: 69596
num_examples: 620
- name: test
num_bytes: 34487
num_examples: 295
- name: validation
num_bytes: 7532
num_examples: 69
download_size: 74057
dataset_size: 111615
configs:
- config_name: emoji
data_files:
- split: train
path: emoji/train-*
- split: test
path: emoji/test-*
- split: validation
path: emoji/validation-*
- config_name: emotion
data_files:
- split: train
path: emotion/train-*
- split: test
path: emotion/test-*
- split: validation
path: emotion/validation-*
- config_name: hate
data_files:
- split: train
path: hate/train-*
- split: test
path: hate/test-*
- split: validation
path: hate/validation-*
- config_name: irony
data_files:
- split: train
path: irony/train-*
- split: test
path: irony/test-*
- split: validation
path: irony/validation-*
- config_name: offensive
data_files:
- split: train
path: offensive/train-*
- split: test
path: offensive/test-*
- split: validation
path: offensive/validation-*
- config_name: sentiment
data_files:
- split: train
path: sentiment/train-*
- split: test
path: sentiment/test-*
- split: validation
path: sentiment/validation-*
- config_name: stance_abortion
data_files:
- split: train
path: stance_abortion/train-*
- split: test
path: stance_abortion/test-*
- split: validation
path: stance_abortion/validation-*
- config_name: stance_atheism
data_files:
- split: train
path: stance_atheism/train-*
- split: test
path: stance_atheism/test-*
- split: validation
path: stance_atheism/validation-*
- config_name: stance_climate
data_files:
- split: train
path: stance_climate/train-*
- split: test
path: stance_climate/test-*
- split: validation
path: stance_climate/validation-*
- config_name: stance_feminist
data_files:
- split: train
path: stance_feminist/train-*
- split: test
path: stance_feminist/test-*
- split: validation
path: stance_feminist/validation-*
- config_name: stance_hillary
data_files:
- split: train
path: stance_hillary/train-*
- split: test
path: stance_hillary/test-*
- split: validation
path: stance_hillary/validation-*
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
Dataset Card for tweet_eval
Table of Contents
- 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 οΈ οΈ οΈ @ Abbot Kinney, Venice'}
An instance from emotion
config:
{'label': 2, 'text': "βWorry 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 youβ¦'}
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
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: β€1
: π2
: π3
: π4
: π₯5
: π6
: π7
: β¨8
: π9
: π10
: π·11
: πΊπΈ12
: β13
: π14
: π15
: π―16
: π17
: π18
: πΈ19
: π
For emotion
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: anger1
: joy2
: optimism3
: sadness
For hate
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: non-hate1
: hate
For irony
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: non_irony1
: irony
For offensive
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: non-offensive1
: offensive
For sentiment
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: negative1
: neutral2
: positive
For stance_abortion
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: none1
: against2
: favor
For stance_atheism
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: none1
: against2
: favor
For stance_climate
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: none1
: against2
: favor
For stance_feminist
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: none1
: against2
: favor
For stance_hillary
config:
text
: astring
feature containing the tweet.label
: anint
classification label with the following mapping:0
: none1
: against2
: 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 and Twitter API Terms Of Service
Additionally the license are:
- emoji: Undefined
- emotion(EmoInt): Undefined
- hate (HateEval): Need permission here
- irony: Undefined
- Offensive: Undefined
- Sentiment: Creative Commons Attribution 3.0 Unported License
- 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}
}
Stance Detection:
@inproceedings{mohammad2016semeval,
title={Semeval-2016 task 6: Detecting stance in tweets},
author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin},
booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
pages={31--41},
year={2016}
}
Contributions
Thanks to @gchhablani and @abhishekkrthakur for adding this dataset.