Mining Collective Opinions for Comparison of Mobile Apps

User review is a crucial component of open mobile app market such as Google Play Store. These markets allow users to submit feedback for downloaded apps in the form of a) start 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 influence on the purchasing decisions of potential users; indeed play a key role in the generation of revenue for the developers. The mobile apps can contain large volumes of reviews and it is impossible for a user to skim through thousands of reviews to find the opinion of other users about the features he/she is interested in. 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 aid user to compare the features across multiple apps, using the sentiments, expressed in their associated reviews. The proposed methodology can be used to understand user's preference to a certain mobile app and can uncover the relational behind why users prefer an app over other.