Task Categories: text-classification
Languages: apcapj
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original

Dataset Card for ArSenTD-LEV

Dataset Summary

The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria.

Supported Tasks and Leaderboards

Sentriment analysis


Arabic Levantine Dualect

Dataset Structure

Data Instances

{'Country': 0, 'Sentiment': 3, 'Sentiment_Expression': 0, 'Sentiment_Target': 'هاي سوالف عصابات ارهابية', 'Topic': 'politics', 'Tweet': 'ثلاث تفجيرات في #كركوك الحصيلة قتيل و 16 جريح بدأت اكلاوات كركوك كانت امان قبل دخول القوات العراقية ، هاي سوالف عصابات ارهابية'}

Data Fields

Tweet: the text content of the tweet
Country: the country from which the tweet was collected ('jordan', 'lebanon', 'syria', 'palestine')
Topic: the topic being discussed in the tweet (personal, politics, religion, sports, entertainment and others)
Sentiment: the overall sentiment expressed in the tweet (very_negative, negative, neutral, positive and very_positive)
Sentiment_Expression: the way how the sentiment was expressed: explicit, implicit, or none (the latter when sentiment is neutral)
Sentiment_Target: the segment from the tweet to which sentiment is expressed. If sentiment is neutral, this field takes the 'none' value.

Data Splits

No standard splits are provided

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]


Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

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

[More Information Needed]

Licensing Information

Make sure to read and agree to the license

Citation Information

  title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets},
  author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir},
  journal={arXiv preprint arXiv:1906.01830},


Thanks to @moussaKam for adding this dataset.

Models trained or fine-tuned on arsentd_lev

None yet