###### Abstract
To obtain a competitive advantage, companies and decision-makers must prioritize creating a pleasant experience for users and improving service quality. The use of customer reviews is regarded as a valuable tool for discovering user experience elements and experience concerns via mining user reviews [13]. Customer reviews are an important aspect of any online service or product since they provide useful information about the service's quality and efficacy. In today's competitive environment, these reviews can assist businesses in finding areas for improvement and development, providing them with a good source of information about the pros and cons of the services or products in order to fine-tune and promptly improve their services and products quickly and effectively. To enhance the overall experience and usefulness of super applications, decision-makers could use reviews to estimate user experience, whereby every USD 1 invested in enhancing the customer experience will yield a return of USD 10 to 100, thus increasing revenue [14]. All of these facts highlight how crucial it is to analyze users' experiences, especially those of super apps, to comprehend their needs and to avoid them having poor experiences. Hence, analyzing user experience will help to retain users and ensure their loyalty, which improves the application's reputation and increases its profits. Considering the significance of the user's experience, several techniques have evolved to analyze it through textual reviews. One of these approaches is opinion mining or sentiment analysis, comprising numerous levels including document, sentence, and aspect analysis, focused on determining what parts of a review are negative, positive, or neutral [14]. Additionally, the topic modeling approach is used to analyze and extract the main ideas from a document; however, further studies are needed for its application in Arabic texts [15; 16]. Meanwhile, the clustering approach uses the data distribution's underlying structure to generate criteria for grouping data with similar characteristics [17]. All of these techniques have provided solutions to real-world problems. However, studies presented in Arabic still require further exploration [15; 16]. The Arabic language is the world's sixth most spoken language, with over 400 million speakers, most of whom live in the Middle East and North Africa [18; 19]. The Arabic language has three distinct categories: (1) classical Arabic, the language of the holy book of Islam (The Qur'an); (2) modern standard Arabic, which is a simplified variant of classical Arabic grammar that incorporates modern terminology; and (3) dialectical Arabic (colloquial), which refers to all oral kinds utilized in daily discussions and differs among different Arab countries and areas within the same country [20]. Therefore, several machine learning techniques face challenges when utilized in Arabic because of the language's inherent ambiguities, dialectal diversity, morphological richness, and intricate morphosyntactic agreement rules, as well as the scarcity of Arabic resources [21]. As a result, examining the Arabic language remains an increasingly hot topic for researchers. While 59% of the current research analyzes social media content, especially on Twitter ([[https://twitter.com/](https://twitter.com/)]([https://twitter.com/](https://twitter.com/)) (19 December 2021)) (now called X), just 4% of studies analyze business-related content [22]. This presents an opportunity for researchers to uncover this content [22]. This research introduces a novel method to explore user experience through focusing on users' reviews in the business sector super app environment by analyzing Arabic users' reviews in the Kingdom of Saudi Arabia (KSA) using machine learning (ML) approaches. The main contributions of this research are as follows:  
* We propose a comprehensive framework that uses users' reviews to provide decision-makers with a clear view of users' opinions regarding their super app in a visualized way that will be easy and quick to understand.