README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - apc
  - ajp
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
  - topic-classification
paperswithcode_id: arsentd-lev
pretty_name: ArSenTD-LEV
dataset_info:
  features:
    - name: Tweet
      dtype: string
    - name: Country
      dtype:
        class_label:
          names:
            '0': jordan
            '1': lebanon
            '2': syria
            '3': palestine
    - name: Topic
      dtype: string
    - name: Sentiment
      dtype:
        class_label:
          names:
            '0': negative
            '1': neutral
            '2': positive
            '3': very_negative
            '4': very_positive
    - name: Sentiment_Expression
      dtype:
        class_label:
          names:
            '0': explicit
            '1': implicit
            '2': none
    - name: Sentiment_Target
      dtype: string
  splits:
    - name: train
      num_bytes: 1233980
      num_examples: 4000
  download_size: 392666
  dataset_size: 1233980

Dataset Card for ArSenTD-LEV

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: ArSenTD-LEV homepage
• Paper: ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets

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

Languages

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]

Annotations

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

@article{baly2019arsentd,
  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},
  year={2019}
}

Contributions

Thanks to @moussaKam for adding this dataset.