AURA-Classification

Dataset Description

The AURA (App User Review in Arabic) Classification dataset is a collection of 2,900 Arabic-language app reviews collected from various mobile applications. This dataset is primarily designed for text classification tasks.

Features

The dataset includes the following fields:

• review: The text of the review in Arabic.

• appName: The name of the application being reviewed.

• platform: The platform (iOS or Android) where the review was posted.

• label: The classification label for the review, categorized as:

  • bug_report: The review highlights a bug or issue in the app.
  • improvement_request: The review suggests improvements or features.
  • rating: The review expresses a general rating or opinion.
  • others: Miscellaneous or uncategorized reviews.

Dataset Statistics

• Total Reviews: 2,900
• Platforms: iOS, Android
• Applications: Multiple apps from diverse categories.
• Labels Distribution: The dataset is labeled into four categories for multi-class classification tasks.

Example Entry

{
  "review": "يبيله تصليحات كثير",
  "appName": "OSN - Streaming App",
  "platform": "ios",
  "label": "bug_report"
}

Use Cases

This dataset is suitable for:

• Text classification (e.g., topic classification, issue identification).

• Multilingual NLP research focused on Arabic.

• Fine-tuning models for app review classification.

Citation

If you use this dataset, please cite it as:

@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).