Aspect-based Sentiment Analysis on Mobile Application Reviews

Abstract

With the popularity of smartphones, mobile application (A.K.A Mobile App) development has become a booming industry all across the World. One of the main hurdles that app developers are facing, is understanding users' needs and catering their products to satisfy the users. Though Users are one of the main stakeholders of the App development process it is harder to incorporate them into the requirement elicitation process. Numerous studies have shown that incorporating user reviews in the requirement elicitation process paves the way to a better understanding of user needs which, in turn, helps developers develop better apps that satisfy the targeted audience of the app. In this paper, we introduce a CNN-based approach to analyze user reviews using Aspect-based Sentiment Analysis (ABSA). The results show that our approach could achieve 87.88%, 93.75%, and 31.25% improvements in aspect category classification and 16.43%, 23.35%, and 3.72% improvements in aspect sentiment classification over the baseline results for AWER dataset in productivity, social networking, and game domains respectively.