Multi-Label Learning for Aspect Category Detection of Arabic Hotel Reviews Using AraBERT

Abstract

Studying people’s satisfaction with social media is vital to understanding the users’ needs. Nowadays, textual hotel reviews are used to evaluate the hotel’s e-reputation. In this context, we are interested in Aspect Category Detection (ACD) as a subtask of aspect-based sentiment analysis. This task needs to be investigated through multi-label classification, which is more challenging, in natural language processing, than single-label classification. Our study leverages the potential of transfer learning with the pre-trained AraBERT model for contextual text representation. We are based on the Arabic SemEval-2016 data set for hotel reviews. We propose a specific preprocessing for this Arabic reviews dataset to improve the performance. In addition, as this data suffers from an imbalanced distribution, we use a dynamically weighted loss function approach to deal with imbalanced classes. The carried-out results outperform the pioneering state-of-the-art of the Arabic ACD with an F1 score of 67.3%.