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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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- 100K<n<1M |
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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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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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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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# S3D Summary |
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The S3D dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by our **BERTweet-sarcasm-combined** model. |
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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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S3D contains 38879 tweets labelled as sarcastic, and 61211 tweets labelled as not being sarcastic. |
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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: 70,000 |
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- Valid: 15,000 |
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- Test: 15,000 |
