###### Abstract  
Recently, business sectors have focused on offering a wide variety of services through utilizing different modern technologies such as super apps in order to fulfill customers' needs and create a satisfactory user experience. Accordingly, studying the user experience has become one of the most popular trends in the research field due to its essential role in business prosperity and continuity. Thus, many researchers have dedicated their efforts to exploring and analyzing the user experience across social media, blogs, and websites, employing a variety of research methods such as machine learning to mine users' reviews. However, there are limited studies concentrated on analyzing super app users' experiences and specifically mining Arabic users' reviews. Therefore, this paper aims to analyze and discover the most important topics that affect the user experience in the super app environment by mining Arabic business sector users' reviews in Saudi Arabia using biterm topic modeling, CAMel sentiment analyzer, and doc2 vec with k-means clustering. We explore users' feelings regarding the extracted topics in order to identify the weak aspects to improve and the strong aspects to enhance, which will promote a satisfactory user experience. Hence, this paper proposes an Arabic text annotation framework to help the business sector in Saudi Arabia to determine the important topics with negative and positive impacts on users' experience. The proposed framework uses two approaches: _topic modeling with sentiment analysis_ and _topic modeling with clustering_. As a result, the proposed framework reveals four important topics: delivery and payment, customer service and updates, prices, and application. The retrieved topics are thoroughly studied, and the findings show that, in most topics, negative comments outweigh positive comments. These results are provided with general analysis and recommendations to help the business sector to improve its level of services. machine learning; super app; user experience; topic modeling; biterm topic model; sentiment analysis; clustering; data mining; Arabic data annotation +
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point to mention is that 90% of consumers may decide not to return and use the application again after a negative user experience, resulting in a 35% loss of sales worth USD 1.4 trillion [4]. Hence, this reinforces the importance of user experience, which focuses on the user's interaction with the product or service in terms of emotion, feeling, value, and importance [5]. In the same context, Gartner Inc. (Stamford, CT, USA), a technology research and consulting firm in the United States, announced that the super app had become one of the top ten emerging technologies for 2022 [6]. According to a January 2022 survey, a successful super app may reach 71 million users on day one in the US, resulting in an annual expenditure of over USD 2.23 billion [7]. To clarify, super applications, or super apps, consolidate several providers' services into a single mobile app, eliminating the need for numerous sign-ins and passwords [8]. This characteristic makes companies more likely to offer these integrated services to suit a wide range of customer demands. As a result, researchers have focused on examining super apps from a variety of perspectives, including user behavior, privacy, security, and user experience [9; 10; 11; 12]. Although super apps offer a good user experience by merging several services into a single mobile application, customer pleasure and experience can still be affected by a variety of factors. One of these factors is a poor user experience, causing huge losses for the business, not to mention the magnitude of the impact on the super app combining many services in one place. Such an effect will cause huge losses that go beyond the owner of the super app, reaching all service providers, which will double the losses for the owners of the services and the super app.