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--- |
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annotations_creators: |
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- Jordan Painter, Diptesh Kanojia |
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language: |
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- en |
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license: |
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- cc-by-sa-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: 'Utilising Weak Supervision to create S3D: A Sarcasm Annotated Dataset' |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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task_categories: |
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- text-classification |
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--- |
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# Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset |
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This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models. |
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# SAD |
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The SAD dataset is our gold standard dataset of tweets labelled for sarcasm. These tweets were scraped by observing a '#sarcasm' hashtag and then manually annotated by three annotators. |
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There are a total of 1170 pairs of a sarcastic and non-sarcastic tweets which were both posted by the same user, resulting in a total of 2340 tweets annotated for sarcasm. |
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These tweets can be accessed by using the Twitter API so that they can be used for other experiments. |
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# Data Fields |
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- Tweet ID: The ID of the labelled tweet |
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- Label: A label to denote if a given tweet is sarcastic |
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# Data Splits |
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- Train: 1638 |
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- Valid: 351 |
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- Test: 351 |
