AURA-Sentiment

Dataset Description

The AURA Sentiment Dataset is a collection of 29,700 app reviews in Arabic from iOS and Android platforms. Each review is labeled with a sentiment class, enabling researchers and practitioners to develop and evaluate sentiment analysis models tailored to the Arabic language.

Features

The dataset includes the following columns:

• review: The text of the app review in Arabic.
• appName: The name of the application reviewed.
• platform: The platform from which the review was posted (iOS or Android).
• sentiment: The sentiment label assigned to the review:
  • positive
  • negative

Dataset Statistics

• Total Reviews: 29,700
• Platforms: iOS and Android
• Applications: Multiple apps spanning various categories.
• Sentiment Distribution: Balanced between positive and negative reviews.

Example Rows

review	appName	platform	sentiment
جميل بس ليه فلوس انا ماعندي	ﺷﺎﻫﺪ - Shahid	android	positive
التطبيق السابق افضل	ALKAHRABA	android	negative
تطبيق لا يستحق ولا نجمه انا عندي واجبات مايطلعه وإذا رحت قوقل القى الواجب بعدين اروح اشوف في التطبيق...	مدرستي	android	negative
بعد التحديث الاخير لا أستطيع تسجيل الدخول ارجو حلها على الفور	State of Survival: Zombie War	ios	negative
لعبه جميله جدا	State of Survival: Survive the Zombie...	android	positive

Use Cases

This dataset is suitable for:

• Sentiment classification in Arabic-language text.
• Developing and fine-tuning sentiment analysis models for app reviews.
• Studying user feedback trends on mobile applications in Arabic-speaking regions.
• Multilingual NLP research focused on sentiment tasks.

Citation

If you use this dataset, please cite it as follows:

@article{Aljeezani2025arabic,
  title={Arabic App Reviews: Analysis and Classification},
  author={Aljeezani, Othman and  Alomari, Dorieh and Ahmad, Irfan},
  journal={ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)},
  pages={1--28},
  year={to-appear},
  publisher={ACM New York, NY, USA},
}

License

This dataset is shared under the MIT License. Please ensure appropriate attribution when using this dataset.

Acknowledgments

Special thanks to the contributors and reviewers who made this dataset possible.

Contact

For questions or feedback, please reach out to the corresponding author (Irfan Ahmad).