Mining User Opinions in Mobile App Reviews: A Keyword-based Approach

TL;DR

This paper introduces MARK, a semi-automated framework that leverages keywords to analyze large volumes of mobile app reviews, helping developers identify issues and trends efficiently.

Contribution

The paper presents a novel keyword-based system that automates review analysis, trend detection, and keyword extraction for mobile app feedback.

Findings

Effective identification of relevant reviews using keywords

Trend analysis reveals significant issues over time

Automatic keyword extraction improves analysis efficiency

Abstract

User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.