Improving the energy efficiency of smartphones is critical for increasing the utility that they provide to the users. With most mobile operating systems, users are responsible for managing their phone’s battery efficiency by utilizing the various settings provided by the operating system, as well as selecting energy-efficient apps. However, current app marketplaces do not provide users with information about app energy efficiency, which makes it challenging for the user to make informed decision when selecting an app. This paper presents a novel machine learning approach to estimate app energy efficiency by utilizing textual information available in the Google Play store such as an app’s description, user reviews, as well as system permissions. Our detailed analysis of the resulting system shows that hardware permissions, app description, and user reviews correlate well with energy efficiency ratings. We evaluate five models that represent popular classes of machine learning algorithms in their ability to predict energy efficiency ratings. Finally, we compare our approach to gold truth ratings obtained by the actual energy profiling of the app, demonstrating that the proposed system is able to estimate an app’s energy efficiency within less than 1 point on the 1 – 5 scale provided by the profiler, without requiring any kind of profiling.