Abstract

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.

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