Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews

Asma Ameur*, Sana Hamdi, Sadok Ben Yahia

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


In many fields, like aspect category detection (ACD) in aspect-based sentiment analysis, it is necessary to label each instance with more than one label at the same time. This study tackles the multilabel classification problem in the ACD task for the Arabic language. For this purpose, we used Arabic hotel reviews from the SemEval-2016 dataset, comprising 13,113 annotated tuples provided for training (10,509) and testing (2,604). To extract valuable information, we first propose specific data preprocessing. Then, we suggest using the dynamic weighted loss function and a data augmentation method to fix the problem with this dataset's imbalance. Using two possible approaches, we develop new ways to find different categories of things in a review sentence. The first is based on classifier chains using machine learning models. The second is based on transfer learning using pretrained AraBERT fine-tuning for contextual representation. Our findings show that both approaches outperformed the related works for ACD on the Arabic SemEval-2016. Moreover, we observed that AraBERT fine-tuning performed much better and achieved a promising (Formula presented.) -score of (Formula presented.).

Original languageEnglish
Article numbere12609
JournalComputational Intelligence
Issue number1
Number of pages23
Publication statusPublished - Feb 2024


  • AraBERT
  • Arabic hotel reviews
  • aspect category
  • imbalanced data
  • multilabel classification
  • preprocessing


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