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International Conference on Collaborative Computing: Networking, Applications and Worksharing

CollaborateCom 2016: Collaborate Computing: Networking, Applications and Worksharing pp 312–323Cite as

An Approach of Extracting Feature Requests from App Reviews

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Abstract

With the rapid development of mobile technologies, developing high-quality mobile apps becomes increasingly important. App reviews, which are collaboratively collected from various users, are viewed as important sources for enhancing or evolving mobile apps, wherein how to accurately extract feature requests becomes an important issue. However, the scale of app reviews is so large that it is intractable to manually identify feature requests from these reviews. In this paper, we propose a semi-automated approach to extract feature requests based on machine learning approaches. In our approach, we firstly identify reviews on feature requests by defining suitable classification features and selecting appropriate classification approaches. Afterwards, these identified reviews are clustered using topic models, and phrases are extracted as feature requests, which serve as the basis of feature modeling. Experiments conducted on a real world data set show that the proposed approach can contribute to extracting feature requests from app reviews.

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Notes

  1. 1.

    https://itunes.apple.com/us/genre/ios/id36.

  2. 2.

    https://play.google.com/store?hl=en.

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Acknowledgments

The work is supported by the National Basic Research Program of China under grant No. 2014CB340404, and the National Key Research and Development Program of China under grant No. 2016YFB0800400, and the National Natural Science Foundation of China under Nos. 61672387, 61373037, 61572186 and 61562073. The authors would like to thank anonymous reviewers for their valuable suggestions.

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Authors and Affiliations

  1. State Key Lab of Software Engineering, Computer School, Wuhan University, Wuhan, China

    Zhenlian Peng, Jian Wang & Keqing He

  2. Computer School, Hunan University of Science and Technology, Xiangtan, China

    Zhenlian Peng & Mingdong Tang

Authors
  1. Zhenlian Peng
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  2. Jian Wang
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  3. Keqing He
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  4. Mingdong Tang
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Corresponding author

Correspondence to Jian Wang .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Ao Zhou

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Peng, Z., Wang, J., He, K., Tang, M. (2017). An Approach of Extracting Feature Requests from App Reviews. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_28

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  • DOI: https://doi.org/10.1007/978-3-319-59288-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

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