An Innovative Arabic Word Embedding Representation for Enhanced Sentiment Analysis

Abstract

Despite the plethora of data generated on Arabic social media, research dedicated to this language remains comparatively scarce. Sentiment analysis, an extensively studied field in various languages, has seen limited development in Arabic. Existing approaches to Arabic sentiment analysis primarily employ machine learning, wherein word vector representations serve as features for model training. A significant challenge encountered in this approach is the substantial volume and sparsity of the matrix representation, attributable to the extensive vocabulary of the Arabic language. This paper proposes a novel word embedding that amalgamates the Bag of Roots (BoR) technique with Global Vector distributional representations (GloVe). This innovation is inspired by the characteristic of the Arabic language, where it is rare to find two or more words sharing the same root but conveying different sentiments. The impact of this innovative word embedding technique is highlighted through an evaluation using sentiment analysis. This involves the implementation of conventional classifiers, specifically Support Vector Machines (SVM) and Logistic Regression (LR). The results obtained demonstrate promising precision, recall, and F1-score metrics. Additionally, a significant reduction in processing time is observed when compared to other approaches referenced in literature. Thus, this paper contributes to the advancement of Arabic sentiment analysis, offering a potential pathway to overcoming the challenges associated with the large vocabulary and complex structure of the Arabic language.