Domain Adaptation Approach for Arabic Sarcasm Detection in Hotel Reviews based on Hybrid Learning

Abstract

Posting sarcastic comments on hotel opinion websites became a common trend. Therefore, sarcasm detection becomes of primary importance, mainly since sarcasm can flip the review's polarity. To our knowledge, no work related to detecting sarcasm exists in the hospitality industry. The lack of a labeled dataset is an utmost issue for researchers. We propose a new approach based on the domain adaptation using the self-training technique for the semi-supervised learning of sarcastic Arabic hotel reviews. We use AraBERT for contextual embedding and machine learning models for self-training classification. The obtained accuracy reaches 91.1%.