Mobile app stores provide an extremely rich source of information on app descriptions, characteristics, and usage, and analyzing these data provides insights and a deeper understanding of the nature of apps. However, manual analysis of this vast amount of information on mobile apps is not a simple and straightforward task; it is costly in terms of human effort and time. Computational methods such as topic modeling can provide an efficient and satisfactory approach to mobile app information analysis. Topic modeling is a type of statistical modeling technique for discovering abstract topics that occur in a set of documents. This study explores the relationship between features of Arabic apps and investigates how well the current predefined Google Play app categories represent the type and genre of Arabic mobile apps. Based on the textual app description analysis, we aim to design and develop a sustainable classification system using the Latent Dirichlet Allocation (LDA) method of topic modeling in order to cover the Arabic apps classification in Google Play app store. Our study supports the hypothesis that the textual app descriptions are effective in suggesting new categories for Arabic mobile apps in Google Play app store. Also, the results indicated that the current classification on Google Play app store is not suitable for our case study “Arabic apps,” as well as it is not sustainable, as it can not cover the new app types including Arabic apps. This study offers an important contribution to Arabic app analysis and design, to improve app search and exploration in several domains such as business, marketing, and technical development. Furthermore, it provides insights for the future of Arabic app research and provides guidance for the development of an Arabic app dashboard that will support users on how to select an app based on their specific needs.

中文翻译：

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使用主题模型对移动应用程序进行分析和分类：以Google Play阿拉伯文应用程序为例

移动应用程序商店提供了有关应用程序描述，特征和使用情况的极其丰富的信息源，对这些数据进行分析可以提供见解和对应用程序性质的更深刻理解。但是，手动分析移动应用程序上的大量信息并不是一项简单明了的任务。就人力和时间而言，这是昂贵的。诸如主题建模之类的计算方法可以为移动应用程序信息分析提供有效且令人满意的方法。主题建模是一种统计建模技术，用于发现出现在一组文档中的抽象主题。这项研究探讨了阿拉伯语应用程序功能之间的关系，并研究了当前预定义的Google Play应用程序类别如何很好地代表了阿拉伯语移动应用程序的类型和类型。基于文本应用程序描述分析，我们旨在使用主题建模的潜在狄利克雷分配（LDA）方法来设计和开发可持续分类系统，以涵盖Google Play应用商店中的阿拉伯语应用程序分类。我们的研究支持以下假设：文本应用程序描述可以有效地在Google Play应用程序商店中为阿拉伯移动应用程序建议新的类别。此外，结果表明，Google Play应用商店上的当前分类不适合我们的案例研究“阿拉伯应用”，并且由于它不能涵盖包括阿拉伯应用在内的新应用类型，因此不可持续。这项研究为阿拉伯语应用程序分析和设计做出了重要贡献，以改善商业，营销和技术开发等多个领域中的应用程序搜索和探索。此外，