Automatic labeling of mobile apps by the type of psychological needs they satisfy

App usage is now a ubiquitous phenomenon, but little is known about what types of psychological needs are met by which apps. We proposed a method to label automatically mobile apps in terms of whether and to what extent they can satisfy users' particular psychological needs. First, using the grounded theory approach, we conducted semi-structured in-depth interviews to identify types of needs associated with app usage. Substantive and theoretical coding of the data from the interviews as well as data from samples of app reviews yielded eight types of psychological needs app users had: utilitarian, low-cost, security, health, hedonic, social, cognitive, and self-actualization needs. Second, using the needs corpus (words and phrases) generated above, a classifier was trained using latent Dirichlet allocation (LDA) and support vector machine (SVM) algorithms to filter reviews in terms of whether they included needs-related comments. The classifier showed good performance. Finally, Labeled-LDA was used to automatically provide each review with multiple labels of the types of needs mentioned and the apps were analyzed for the different types of needs they satisfied.