User Comment Analysis for Android apps and CSPI Detection with Comment Expansion

Along with the exponential growth on markets of mobile apps, comes the serious public concern about the security and privacy issues. User comments serves as a valuable source of information for evaluating a mobile app, for both new users and developers. However, for the purpose of evaluation on the security/privacy aspects of an app, user comments are not always directly useful. Most of the comments are about issues like functionality, missing feature or just pure emotional expression. Therefore, further efforts are required in order to identify those Comments with Security/Privacy Issues (CSPI) for future evaluation. In this paper, a dataset of comments is collected from Google Play, and a two dimensional label system is proposed to describe those CSPI within it. A supervised multi-label learning method utilizing comment expansion is adopted to detect different types of CSPI described by this label system. Experiments on the collected dataset shows that the proposed method outperforms the method without the comment expansion.