App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

App reviews provide a rich source of user opinions that can support requirement engineering activities. Analysing them manually to find these opinions, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for so-called opinion mining. These approaches facilitate the analysis by extracting features discussed in app reviews and identifying their associated sentiments. The effectiveness of these approaches has been evaluated using different methods and datasets. Unfortunately, replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present an empirical study in which, we evaluated feature extraction and sentiment analysis approaches on the same dataset. The results show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.

Context and motivation] App reviews can be a rich source of information for requirements engineers. Recently, many approaches have been proposed to classify app reviews as bug reports, feature requests, or to elicit requirements. [Question/problem] None of these approaches, however, allow requirements engineers to search for users' opinions about specific features of interest. Retrieving reviews on specific features would help requirements engineers during requirements elicitation and prioritization activities involving these features. [Principal idea/results] This paper presents a research preview on our toolsupported method for taking requirements engineering decisions about specific features. The tool will allow one to (i) find reviews that talk about a specific feature, (ii) identify bug reports, change requests and users' sentiment about this feature, and (iii) visualize and compare users' feedback for different features in an analytic dashboard. [Contributions] Our contribution is threefold: (i) we identify a new problem to address, i.e. searching for users' opinions on a specific feature, (ii) we provide a research preview on an analytics tool addressing the problem, and finally (iii) we discuss preliminary results on the searching component of the tool.