###### Abstract  
Opinion mining refers to the process that helps to identify and to classify users' emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers' opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojs and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojs and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information. opinion mining; sentiment analysis; data mining; automation; artificial intelligence; rating prediction; machine learning; Arabic reviews; emoji and emoticon; Arabic lexicon +
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hotel. In the tourism and hospitality industries, there are two main concerning factors: hotel guest experience and guest satisfaction, as increasing them is crucial to customer loyalty, distributing positive word-of-mouth, and repeat visits, which are all critical factors in the hospitality industry [7]. Therefore, a hotel's reputation highly affects reservations, services, and product sales. The tourism and hospitality industries involve many service sectors that significantly boost the economies of many countries through destination spots and entertainment places and many services, including lodging services, such as hotels, guest houses, guest rooms, resorts, and motels. Thus, tourism and hospitality industries significantly increase gross domestic product growth and raise countries' economies [8]. Additionally, destinations and countries benefit significantly from it in terms of their innovation and social development [9]. Automating opinion mining can help to predict and to understand customers' ratings for a particular product or service after interpreting customer satisfaction levels based on their comments about it on social media. Thus, it saves time for both customers and stakeholders who do not want to read many comments to understand and obtain an insight into a specific product or service [10]. Tertiary sectors of cities and tourism can be among stakeholders of automated hotel rating predictors, as they can provide insight into customer satisfaction levels. Then, policymakers can improve hotel policies or upgrade hotel marketing strategies to contribute to promoting the tourism industry, thus helping a country maintain its economic sustainability. Automating opinion mining is particularly important when using social media platforms, such as Twitter.