ALDA: An aggregated LDA for polarity enhanced aspect identification technique in mobile app domain

Abstract

With the increased popularity of the smart mobile devices, mobile applications (a.k.a apps) have become essential. While the app developers face an extensive challenge to improve user satisfaction by exploiting the valuable feedbacks, the app users are overloaded with way too many apps. Extracting the valuable features from apps and mining the associated sentiments is of utmost importance for the app developers. Similarly, from the user perspective, the key preferences should be identified. This work deals with profiling users and apps using a novel LDA based aspect identification technique. Polarity aggregation technique is used to tag the weak features of the apps the developers should concentrate on. The proposed technique has been experimented on an Android review dataset to validate the efficacy compared to state-of-the-art algorithms. Experimental findings suggest superiority and applicability of our model in practical scenarios.