--- 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} } ```