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Requirements Engineering

, Volume 21, Issue 3, pp 311–331 | Cite as

On the automatic classification of app reviews

  • Walid Maalej
  • Zijad Kurtanović
  • Hadeer Nabil
  • Christoph Stanik
RE 2015

Abstract

App stores like Google Play and Apple AppStore have over 3 million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. The majority of the reviews, however, is rather non-informative just praising the app and repeating to the star ratings in words. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and text ratings. For this, we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with simple text classification and natural language preprocessing of the text—particularly with bigrams and lemmatization—the classification precision for all review types got up to 88–92 % and the recall up to 90–99 %. Multiple binary classifiers outperformed single multiclass classifiers. Our results inspired the design of a review analytics tool, which should help app vendors and developers deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders. We describe the tool main features and summarize nine interviews with practitioners on how review analytics tools including ours could be used in practice.

Keywords

User feedback Review analytics Software analytics Machine learning Natural language processing Data-driven requirements engineering 

Notes

Acknowledgments

We thank D. Pagano for his support with the data collection, M. Häring for contributing to the development of the coding tool, as well as the RE15 reviewers, M. Nagappan, and T. Johann for the comments on the paper. We are also very grateful to the participants in the evaluation interviews. This work was partly supported by Microsoft Research (SEIF Award 2014).

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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Walid Maalej
    • 1
  • Zijad Kurtanović
    • 1
  • Hadeer Nabil
    • 2
  • Christoph Stanik
    • 1
  1. 1.Department of InformaticsUniversity of HamburgHamburgGermany
  2. 2.German University of CairoCairoEgypt

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