Abstract
Review mining from app marketplaces has gained immense popularity from researchers in recent years. Most studies in this area, however, tend to focus on improving the performance of classification prediction. In this study, we consider review mining from a different perspective, that is, mining user actions/decisions along with their respective arguments/reasons. Our motivation is to obtain a deeper understanding of users' decisions regarding applications and their underlying justifications, e.g., why users give ratings or recommendations. These information abstractions can benefit app developers, especially in planning app updates, by providing data-driven requirements from users' points of view. We utilized a supervised learning approach and built a machine-based annotator to set the ground truth. Seven classifiers and different feature configurations were trained and evaluated on two app review datasets. We then extracted relations between user decisions and arguments based on functional and nonfunctional requirement attributes. The results show an improved performance over the results of the baselines and favorably acceptable performance compared to the results from a human assessment.
Highlights
T HE last decade marks the explosion of user-generated content as web and mobile application technologies have progressively developed
Our paper makes the following contributions: 1) We mine app reviews with an emphasis on extracting user arguments that underlie their decisions; 2) We propose an automatic framework based on weakly supervised learning, from handcrafted rule-based annotators and feature configurations to relation extractors, to model user decisions and arguments based on functional/nonfunctional requirement attributes; 3) We evaluate, discuss, and compare our results with human-based judgments
Since we are interested in extracting user arguments in the context of software improvement, we focus on mining software attribute-related arguments in the form of functional requirements (FRs) and nonfunctional requirements (NFRs)
Summary
T HE last decade marks the explosion of user-generated content as web and mobile application (app) technologies have progressively developed. User-generated content, such as product reviews and mobile app reviews, is created every day on a unimaginable scale. This development has given rise to a new business model that is more open and user-oriented [1], [2]. A one-star increase in a Yelp rating indicates a 5-9% increase in revenue [6] For these reasons, developers are eager to obtain positive reviews from their customers/users. Developers are eager to obtain positive reviews from their customers/users These reviews in turn can be analyzed to develop better application updates [7], [8], [9], [10].
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