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multilang_sarcasm_dataset.csv filter=lfs diff=lfs merge=lfs -text
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README.md
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# Dataset Card for Multilingual Sarcasm Detection
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-instances)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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## Dataset Description
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- Repository: https://github.com/helinivan/multilingual-sarcasm-detector
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### Dataset Summary
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Dataset consists of news article headlines in Dutch, English and Italian. The news article headlines are both from actual news sources and sarcastic/satirical newspapers. The news article is determined sarcastic/non-sarcastic based on the news article source.
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The sources of news articles are:
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- The Huffington Post (en, non-sarcastic)
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- The Onion (en, sarcastic)
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- NOS (nl, non-sarcastic)
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- De Speld (nl, sarcastic)
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- Il Giornale (it, non-sarcastic)
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- Lercio (it, sarcastic)
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### Languages
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`en`, `nl`, `it`
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## Dataset Structure
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### Data Instances
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- total_length: 67,480
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- sarcastic: 25,609
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- non_sarcastic: 41,817
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- english: 22,837
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- dutch: 20,771
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- italian: 23,871
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### Data Fields
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- article_url: str
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- article_title: str
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- is_sarcastic: int
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- lang: str
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- title_length: int
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## Dataset Creation
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### Source Data
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- Selected all English news article titles from this Kaggle dataset: https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection
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- Randomly selected 15k Dutch non-sarcastic news article titles from this Kaggle dataset: https://www.kaggle.com/datasets/maxscheijen/dutch-news-articles
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Rest of the data is scraped directly from the newspapers.
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multilang_sarcasm_dataset.csv → helinivan--sarcasm_headlines_multilingual/csv-train.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:94624b21b42c768679abf84a1dd1f6e5a0d9fa8f8b3915c4cc58da69a4a98320
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size 7507273
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