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.
Contrary to the fine-grained extraction of app features, the approach of REF extracts all potential keywords from user reviews and ranks them based on the review rating and occurrence frequency.
Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T)
2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)
2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Computer Science