The rapid proliferation of online information necessitates efficient Recommendation Systems (RSs) to assist users in discovering relevant content. While English-language RSs have received significant attention, research on Arabic RSs remains limited despite the increasing demand for Arabic digital content. This paper addresses the scarcity of Arabic-focused Collaborative Filtering (CF) approaches for RS. Recognizing the wealth of information embedded in user reviews, we propose novel review-based CF approaches tailored for Arabic, aiming to enhance recommendation accuracy for Arab users. Our work comprises three key stages: we first develop a comprehensive Arabic lexicon specifically for the book domain. Secondly, using this lexicon we then propose three distinct sentiment-aware ratings, leveraging sentiment analysis of Arabic reviews to enrich traditional rating predictions. Thirdly, these sentiment-aware ratings are integrated into ten diverse CF algorithms from the Surprise library and a deep autoencoder neural network, covering a spectrum of traditional and modern approaches. Extensive experiments conducted on the Large Arabic Book Reviews (LABR) dataset demonstrate the superior performance of our proposed sentiment-aware ratings compared to baseline methods across all evaluated metrics. Further analysis reveals the importance of appropriate sentiment word extraction methods and lexicon selection for accurate sentiment rating calculation. Finally, this study makes a significant contribution to the field of Arabic CF recommendation systems by providing a comprehensive framework for leveraging user review and underscores the importance of further research in this area.