The title (name) is the primary information related to a mobile (smartphone) application, as it describes its functions and services. An eye-catching title can entice customers to choose a certain application over others. Application development companies are well aware of this phenomenon and invest significant efforts in crafting their application titles with compelling keywords, phrases and topics in pursuit of higher installs. However, to the best of our knowledge, traditional literature that investigates the impact of application titles on success is limited. There may be only a few instances where scientific (data-analytical) approaches have been used to examine application titles. Moreover, these investigations of titles are dominated by supervised learning and traditional literature may lack any unsupervised (cluster) data analysis techniques to measure the impact of titles on application success. Therefore, this research work proposes an unsupervised data analysis approach based on multiple layers and algorithms. The initial layer clusters the application titles, the subsequent layer extracts various textual features from these clusters and the final layer refines the extracted attributes. In general, certain textual features in the titles are proven to be positively and negatively linked with the application installs. Verification of the results has confirmed that this proposed approach can successfully detect the most prominent features from application titles (textual data) that correlate with success.

中文翻译：

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调查 Google-Play 应用程序标题对成功的影响

标题（名称）是与移动（智能手机）应用程序相关的主要信息，因为它描述了其功能和服务。引人注目的标题可以吸引客户选择某个应用程序而不是其他应用程序。应用程序开发公司充分意识到这一现象，并投入大量精力用引人注目的关键字、短语和主题来制作应用程序标题，以追求更高的安装量。然而，据我们所知，研究应用程序名称对成功影响的传统文献是有限的。可能只有少数情况使用科学（数据分析）方法来检查申请标题。此外，这些头衔调查以监督学习为主，传统文献可能缺乏任何无监督（集群）数据分析技术来衡量头衔对申请成功的影响。因此，本研究工作提出了一种基于多层和算法的无监督数据分析方法。初始层对应用程序标题进行聚类，后续层从这些聚类中提取各种文本特征，最后一层对提取的属性进行细化。一般来说，标题中的某些文本特征被证明与应用程序安装呈正相关和负相关。结果验证证实，所提出的方法可以成功地从与成功相关的应用程序标题（文本数据）中检测到最突出的特征。