| --- |
| 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': ❤ |
| '1': 😍 |
| '2': 😂 |
| '3': 💕 |
| '4': 🔥 |
| '5': 😊 |
| '6': 😎 |
| '7': ✨ |
| '8': 💙 |
| '9': 😘 |
| '10': 📷 |
| '11': 🇺🇸 |
| '12': ☀ |
| '13': 💜 |
| '14': 😉 |
| '15': 💯 |
| '16': 😁 |
| '17': 🎄 |
| '18': 📸 |
| '19': 😜 |
| 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 |
| num_bytes: 54775 |
| 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-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} |
| } |
| ``` |
| |
| ### Contributions |
| |
| Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
