A Semantic-Based Framework for Analyzing App Users' Feedback

The competitive market of mobile apps requires app developers to consider the users' feedback frequently. This feedback, when comes from different resources, e.g. App Stores and Twitter, will provide a broader picture of the state of the app, as the users discuss different topics on each platform. Automated tools are developed to filter the informative comments for app developers. However, to integrate the feedbacks from different platforms, one should evaluate the similarities and/or differences of the text from each one. Different meaning of the words in various context, makes this evaluation a challenging task for automated processes. For example, Request night theme and Add dark mode are two comments that are requesting the same feature. This similarity cannot be identified automatically if the semantics of the words are not embedded in the analysis. In this paper, we propose a new framework to analyze the users' feedback by embedding their semantics. As a case study, we investigate whether our approach can identify the similar/different comments from Google Play Store and Twitter, in the two well studied classes of bug reports and feature requests from literature. The initial results, validated by expert evaluation and statistical analysis, shows that this framework can automatically measure the semantic differences among users' comments in both groups. The framework can be used to build intelligent tools to integrate the users' feedback from other platforms, as well as providing ways to analyze the reviews in more detail automatically.