Comparing mobile apps by identifying 'Hot' features

User review is a crucial component of open mobile app market such as the Google Play Store. These markets allow users to submit feedback for downloaded apps in the form of (a) star ratings and (b) opinions in the form of text reviews. Users read these reviews in order to gain insight into the app before they buy or download it. The user opinion about the product also influences on the purchasing decisions of potential users; that in trun, plays a key role in the generation of revenue for the developers. The mobile apps can contain large volume of reviews, which make it nearly impossible for a user to skim through thousands of reviews to find the opinion of other users about the features which interest them the most. Towards this end, we propose a methodology to automatically extract the features of an app from its corresponding reviews using machine learning technique. Moreover, our proposed methodology would aid users to compare the features across multiple apps, using the sentiments, expressed in their associated reviews. The proposed methodology can be used to understand a user's preference for a certain mobile app and could uncover the reasons why users prefer one app over another. (C) 2018 Elsevier B.V. All rights reserved.