###### Abstract
Customers or potential ones do not generally state their opinion explicitly through a rating on Twitter; instead, they talk about their experience with the object; this is due to the different nature of social media platforms when compared to review or trading applications and websites, where there is no particular mandatory place for rating. Social media is an attractive platform for spreading opinions about anything, rather than traditional methods, such as filling out a survey. Social media provide users with ease of use anytime and anywhere, thereby obtaining attention from others and expressing freedom of opinion. However, the spread of opinions can affect potential clients' decisions about specific products or services. Both parties of customers and organizations that provide a product or service can benefit from the predicted satisfaction level. Customers can find more appropriate products or services, and their providers can understand their needs and opinions and improve their outcomes. Moreover, classifying user opinions can be used to utilize recent user-generated data in developing large-scale datasets [10]. Alternatively, they can be used to recommend alternative or similar products or services for future customers based on the opinion analysis of their posts. This paper predicts user opinion based on user ratings for Arabic hotel reviews using Arabic resources, different produced features, and artificial intelligence techniques. Furthermore, it seeks to bridge a gap in the opinion mining field, which is the lack of opinion lexicons consisting of both emojs and emoticons and assigning their score based on levels of ratings that comply with the data, since no lexicon includes a comprehensive list of emojs and emoticon symbols that suit any Arabic dataset consisting of those mixed symbols. Thus, this study generated a lexicon for its dataset to handle its emojs, in addition to both Eastern and Western emoticons. These were coupled with a new consideration for the five levels of satisfaction of users through data. This method was employed in calculating the emotion score. The list of emojs and emoticons can be increased to be more comprehensive with additional data in the future. This paper provides the following main contributions:  
* It proposes an Arabic emojs and emoticons opinion lexicon (ArEmo lexicon) for Arabic opinion mining application tasks. It contains emoticons and emojs with additional descriptions. Each emotion score was calculated based on five levels of ratings using the HARD dataset instead of the three levels in other studies. * It suggests an emoticon disambiguation algorithm by applying regular expression and recursion techniques to prepare a text for calculating the total weight of emoticons and replacing emoticons with their meanings to reflect their emotional contents. * It proposes a user opinion classification approach for five-star ratings of online Arabic hotel reviews using supervised learning, which combines shallow and deep learning methods and employs Arabic resources to obtain valuable features, in addition to the fastText word embeddings. The remainder of this paper is structured as follows. Section 2 presents the relevant literature on Arabic users' opinion prediction and potentially valuable resources for this study. Section 3 details the proposed work, including feature production and emoticon disambiguation-related algorithms, the creation of an Arabic emoji and emoticon opinion lexicon, and the proposed modeling of users' opinion prediction. It is followed by showing and discussing results in Section 4. Finally, both the conclusion and suggested future work are presented in Section 5.