Dataset:
ar_sarcasm

Task Categories: text-classification
Languages: ar
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: mit
Language Creators: found
Annotations Creators: no-annotation

Dataset Card for ArSarcasm

Dataset Summary

ArSarcasm is a new Arabic sarcasm detection dataset. The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them.

The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.

For more details, please check the paper From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset

Supported Tasks and Leaderboards

You can get more information about an Arabic sarcasm tasks and leaderboard here.

Languages

Arabic (multiple dialects)

Dataset Structure

Data Instances

{'dialect': 1, 'original_sentiment': 0, 'sarcasm': 0, 'sentiment': 0, 'source': 'semeval', 'tweet': 'نصيحه ما عمرك اتنزل لعبة سوبر ماريو مش زي ما كنّا متوقعين الله يرحم ايامات السيقا والفاميلي #SuperMarioRun'}

Data Fields

  • tweet: the original tweet text
  • sarcasm: 0 for non-sarcastic, 1 for sarcastic
  • sentiment: 0 for negative, 1 for neutral, 2 for positive
  • original_sentiment: 0 for negative, 1 for neutral, 2 for positive
  • source: the original source of tweet: SemEval or ASTD
  • dialect: 0 for Egypt, 1 for Gulf, 2 for Levant, 3 for Magreb, 4 for Modern Standard Arabic (MSA)

Data Splits

The training set contains 8,437 tweets, while the test set contains 2,110 tweets.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them.

Who are the source language producers?

SemEval 2017 and ASTD

Annotations

Annotation process

For the annotation process, we used Figure-Eight crowdsourcing platform. Our main objective was to annotate the data for sarcasm detection, but due to the challenges imposed by dialectal variations, we decided to add the annotation for dialects. We also include a new annotation for sentiment labels in order to have a glimpse of the variability and subjectivity between different annotators. Thus, the annotators were asked to provide three labels for each tweet as the following:

  • Sarcasm: sarcastic or non-sarcastic.
  • Sentiment: positive, negative or neutral.
  • Dialect: Egyptian, Gulf, Levantine, Maghrebi or Modern Standard Arabic (MSA).

Who are the annotators?

Figure-Eight crowdsourcing platform

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

  • Ibrahim Abu-Farha
  • Walid Magdy

Licensing Information

MIT

Citation Information

@inproceedings{abu-farha-magdy-2020-arabic,
    title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
    author = "Abu Farha, Ibrahim  and Magdy, Walid",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.5",
    pages = "32--39",
    language = "English",
    ISBN = "979-10-95546-51-1",
}

Contributions

Thanks to @mapmeld for adding this dataset.

Models trained or fine-tuned on ar_sarcasm

None yet