Abstract
A key step in publishing on Google Play Store (GPS) is the manual selection of the app category. The category is highly relevant for users when searching for a suitable app. To prevent misclassification, existing work focused on automating the apps’ categories identification through different learning methods. However, most existing approaches do not consider a validation of the categories. This research proposes Latent Dirichlet Allocation (LDA) for categories’ validation and for identification of similar apps. LDA can provide human-understandable topics (mixture of words) and it can help in discovering Android apps’ categories based on their descriptions. For diversity, the most popular 5,940 apps in US and Romania were considered. LDA performance is evaluated under different scenarios defined by data set processing methods. The evaluation relies on the user’s defined categories from GPS. LDA topics are labeled with categories’ names based on these and by applying cosine similarity and human interpretation. Results show a model with a corpus containing various parts of speech (i.e., nouns, adjectives, verbs) and improved with phrases can achieve a precision of 0.69. Moreover, the analysis hints there might exist discrepancies between the GPS guideline regarding the categories’ content and their actual content, but further studies are required.
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Flondor, E., Frincu, M. (2023). Analysing Android Apps Classification and Categories Validation by Using Latent Dirichlet Allocation. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_22
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