---
language: 
  - ar
tags:
  - multi label text-classification
  - arabic
  - app-reviews
  - nlp
dataset_info:
  features:
    - name: review
      dtype: string
    - name: appname
      dtype: string
    - name: platform
      dtype: string
    - name: judg_one
      dtype: string
    - name: judg_two
      dtype: string
    - name: judg_three
      dtype: string
    - name: judg_four
      dtype: string
    - name: judg_five
      dtype: string
  splits:
    - name: train
      num_examples: 2900
license: mit
---

# AURA-Classification (Multi-Label Version)

## 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 designed for multi-label text classification, where each review can belong to multiple classes simultaneously.
Each review in the dataset was independently annotated by five different annotators. To construct the multi-label version of the dataset, a review is assigned to a given class if at least one annotator labeled it with that class. As a result, a single review may be associated with up to four labels.

### 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.

- **judg_one, judg_two, judg_three, judg_four, judg_five**: The labels assigned by each of the five annotators.

The possible classification labels are:

  - `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**: Four possible classes in a multi-label setting (each review may have multiple labels).

### Example Entry

```json
{
  "review": "الرجاء تحديث التطبيق لانه اذا سويت البلاغ في النهاية يخرج من الطبيق ولا يتم 
إرسال البلاغ",
  "appname": "تقديم بلاغ مخالفة تجارية",
  "platform": "android",
  "judg_one": "bug_report",
  "judg_two": "improvement_request",
  "judg_three": "bug_report",
  "judg_four": "improvement_request",
  "judg_five": "bug_report"
}
```

## Use Cases

This dataset is suitable for:

- Multi-label text classification of app reviews.

- Issue identification and requirements elicitation.

- 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)},
  volume={24},
  number={2},
  pages={1--28},
  year={2025},
  publisher={ACM New York, NY, USA},
}

@article{alansari2025multilabel,
  title   = {Multi-Label Classification of Arabic App Reviews with Data Augmentation and Explainable AI},
  author  = {Alansari, Aisha and Alomari, Dorieh and Mahmood, Sajjad and Ahmad, Irfan},
  journal = {Arabian Journal for Science and Engineering},
  year    = {to-appear},
  publisher = {Springer}
}
```

## License

This dataset is shared under the [MIT License](https://opensource.org/licenses/MIT). 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).