Datasets:
Languages:
English
Multilinguality:
monolingual
Annotations Creators:
diana_logan
Source Datasets:
other-generated-datasets
ArXiv:
License:
| annotations_creators: | |
| - diana_logan | |
| language: | |
| - en | |
| license: | |
| - apache-2.0 | |
| multilinguality: | |
| - monolingual | |
| source_datasets: | |
| - other-generated-datasets | |
| task_categories: | |
| - text | |
| - linear-regression | |
| task_ids: | |
| - intent-classification | |
| - multi-class-classification | |
| - sentiment-classification | |
| 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} | |
| } | |
| ``` | |
| #### 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} | |
| } | |
| ``` | |