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The exponential growth of digital information, particularly user-generated content on social media and blogging platforms, has underscored the importance of sentiment analysis (SA). Arabic language sentiment analysis (ASA) involves identifying the orientation of ideas, feelings, emotions, and attitudes within Arabic text to determine whether they convey a positive, negative, or neutral sentiment. This paper presents a comprehensive review of the past decade, focusing on the utilization of SA in the Arabic language. It examines various applications, methodologies, and challenges associated with ASA, highlighting gaps and limitations in existing approaches, lexicons, and annotated datasets. The primary objective of this review is to assist researchers in identifying these gaps and limitations while offering accessible annotated datasets, preprocessing techniques, and procedures. We defined specific criteria for selecting research publications from the last 10 years, including 150 papers in our review process, while excluding earlier publications. The review utilized multiple databases, including Google Scholar, Scopus, and Web of Science. The inherent complexity of the Arabic language, due to its unique traits and diverse dialects, presents significant challenges in ASA. Moreover, the lack of annotated datasets, lexicon resources, and programming tools further complicates sentiment analysis in Arabic. The morphological variations within Arabic make it linguistically challenging. To address these issues, it is crucial to develop additional resources and construct new Arabic sentiment lexicons that account for the various dialects within Modern Standard Arabic (MSA). Our findings reveal that there is no standard public lexicon that adequately enhances the calculation of ASA across different domains, such as e-commerce, politics, public health, and marketing.

Online product reviews play a major role in the success or failure of an E-commerce business. Before procuring products or services, the shoppers usually go through the online reviews posted by previous customers to get recommendations of the details of products and make purchasing decisions. Nevertheless, it is possible to enhance or hamper specific E-business products by posting fake reviews, which can be written by persons called fraudsters. These reviews can cause financial loss to E-commerce businesses and misguide consumers to take the wrong decision to search for alternative products. Thus, developing a fake review detection system is ultimately required for E-commerce business. The proposed methodology has used four standard fake review datasets of multidomains include hotels, restaurants, Yelp, and Amazon. Further, preprocessing methods such as stopword removal, punctuation removal, and tokenization have performed as well as padding sequence method for making the input sequence has fixed length during training, validation, and testing the model. As this methodology uses different sizes of datasets, various input word-embedding matrices of n-gram features of the review’s text are developed and created with help of word-embedding layer that is one component of the proposed model. Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. Based on gate mechanisms, the LSTM layer is combined with the CNN technique for learning and handling the contextual information of n-gram features of the review’s text. Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. In this paper, the proposed CNN-LSTM model was evaluated in two types of experiments, in-domain and cross-domain experiments. For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated entirely. The testing results of the model in-domain experiment datasets were 77%, 85%, 86%, and 87% in the terms of accuracy for restaurant, hotel, Yelp, and Amazon datasets, respectively. Concerning the cross-domain experiment, the proposed model has attained 89% accuracy. Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared methods.