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--- |
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language: |
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- ar |
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tags: |
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- multi label text-classification |
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- arabic |
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- app-reviews |
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- nlp |
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dataset_info: |
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features: |
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- name: review |
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dtype: string |
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- name: appname |
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dtype: string |
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- name: platform |
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dtype: string |
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- name: judg_one |
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dtype: string |
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- name: judg_two |
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dtype: string |
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- name: judg_three |
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dtype: string |
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- name: judg_four |
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dtype: string |
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- name: judg_five |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 2900 |
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license: mit |
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--- |
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# AURA-Classification (Multi-Label Version) |
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## Dataset Description |
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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. |
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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. |
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### Features |
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The dataset includes the following fields: |
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- **review**: The text of the review in Arabic. |
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- **appName**: The name of the application being reviewed. |
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- **platform**: The platform (iOS or Android) where the review was posted. |
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- **judg_one, judg_two, judg_three, judg_four, judg_five**: The labels assigned by each of the five annotators. |
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The possible classification labels are: |
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- `bug_report`: The review highlights a bug or issue in the app. |
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- `improvement_request`: The review suggests improvements or features. |
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- `rating`: The review expresses a general rating or opinion. |
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- `others`: Miscellaneous or uncategorized reviews. |
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### Dataset Statistics |
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- **Total Reviews**: 2,900 |
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- **Platforms**: iOS, Android |
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- **Applications**: Multiple apps from diverse categories. |
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- **Labels Distribution**: Four possible classes in a multi-label setting (each review may have multiple labels). |
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### Example Entry |
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```json |
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{ |
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"review": "الرجاء تحديث التطبيق لانه اذا سويت البلاغ في النهاية يخرج من الطبيق ولا يتم |
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إرسال البلاغ", |
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"appname": "تقديم بلاغ مخالفة تجارية", |
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"platform": "android", |
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"judg_one": "bug_report", |
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"judg_two": "improvement_request", |
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"judg_three": "bug_report", |
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"judg_four": "improvement_request", |
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"judg_five": "bug_report" |
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} |
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``` |
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## Use Cases |
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This dataset is suitable for: |
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- Multi-label text classification of app reviews. |
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- Issue identification and requirements elicitation. |
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- Multilingual NLP research focused on Arabic. |
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- Fine-tuning models for app review classification. |
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## Citation |
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If you use this dataset, please cite it as: |
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``` |
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@article{Aljeezani2025arabic, |
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title={Arabic App Reviews: Analysis and Classification}, |
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author={Aljeezani, Othman and Alomari, Dorieh and Ahmad, Irfan}, |
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journal={ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)}, |
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volume={24}, |
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number={2}, |
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pages={1--28}, |
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year={2025}, |
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publisher={ACM New York, NY, USA}, |
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} |
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@article{alansari2025multilabel, |
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title = {Multi-Label Classification of Arabic App Reviews with Data Augmentation and Explainable AI}, |
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author = {Alansari, Aisha and Alomari, Dorieh and Mahmood, Sajjad and Ahmad, Irfan}, |
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journal = {Arabian Journal for Science and Engineering}, |
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year = {to-appear}, |
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publisher = {Springer} |
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} |
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``` |
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## License |
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This dataset is shared under the [MIT License](https://opensource.org/licenses/MIT). Please ensure appropriate attribution when using this dataset. |
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## Acknowledgments |
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Special thanks to the contributors and reviewers who made this dataset possible. |
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## Contact |
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For questions or feedback, please reach out to the corresponding author (Irfan Ahmad). |
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