Syntactic- and morphology-based text augmentation framework for Arabic sentiment analysis

Abstract

Arabic language is a challenging language for automatic processing. This is due to several intrinsic reasons such as Arabic multi-dialects, ambiguous syntax, syntactical flexibility and diacritics. Machine learning and deep learning frameworks require big datasets for training to ensure accurate predictions. This leads to another challenge faced by researches using Arabic text; as Arabic textual datasets of high quality are still scarce. In this paper, an intelligent framework for expanding or augmenting Arabic sentences is presented. The sentences were initially labelled by human annotators for sentiment analysis. The novel approach presented in this work relies on the rich morphology of Arabic, synonymy lists, syntactical or grammatical rules, and negation rules to generate new sentences from the seed sentences with their proper labels. Most augmentation techniques target image or video data. This study is the first work to target text augmentation for Arabic language. Using this framework, we were able to increase the size of the initial seed datasets by 10 folds. Experiments that assess the impact of this augmentation on sentiment analysis showed a 42% average increase in accuracy, due to the reliability and the high quality of the rules used to build this framework.