MIAR: A Context-Aware Approach for App Review Intention Mining

Abstract

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users. To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective. Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost. In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically. We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism. The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall and [Formula: see text]-score evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.