Making sense of app reviews : efficient analysis of user reviews for mobile apps with STM

This thesis considers the analysis of app reviews written on mobile app platforms using Structural Topic Modeling (STM). Analyzing reviews can help developers increase their app’s popularity and revenues. Due to a large number of app reviews being available, manual analysis can be difficult. STM offers a way to discover the topics that are discussed in large sets of texts and can provide a way to quickly analyze the contents of the reviews. Since the reviews often have low quality or contain irrelevant information, the resulting STM models can contain much noise, making analysis difficult. Conventional solutions to remove uninformative texts from the data often include supervised learning methods and other approaches that still require human labor, which goes against the advantages of STM’s unsupervised nature. This work uses four review datasets from the Google Play Store to show that filtering reviews using their helpfulness/thumbs-up rating, along with some other filtering and pre-processing techniques, can improve the usage of STM for app reviews.
by Alexander W. de Jong
Masterarbeit Universität Innsbruck 2022