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Google Play Store Data Mining and Analysis

Md. Shahriar Kabir, Mohammad Shamsul Arefin in Data Mining

International Journal of Applied Information Systems
Year of Publication: 2019
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Md. Shahriar Kabir, Mohammad Shamsul Arefin
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  1. Md. Shahriar Kabir and Mohammad Shamsul Arefin. Google Play Store Data Mining and Analysis. International Journal of Applied Information Systems 12(26):1-5, December 2019. URL, DOI BibTeX

    	author = "Md. Shahriar Kabir and Mohammad Shamsul Arefin",
    	title = "Google Play Store Data Mining and Analysis",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "December, 2019",
    	volume = 12,
    	number = 26,
    	month = "December",
    	year = 2019,
    	issn = "2249-0868",
    	pages = "1-5",
    	url = "",
    	doi = "10.5120/ijais2019451839",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


With the popularity of smartphones, mobile application markets have been growing exponentially in terms of the number of users and downloads. As a report from says that by 2020 mobile apps are forecast to generate around 189 billion U.S. dollars in revenues via app stores and in-app advertising. So, Google Play store is a crucial place in the field of business. In this work a system has been developed that can mine important data from google play store with the help of app crawler and find correlation among apps rating, reviews, installs and price. The correlation using Spearman Rank Correlation and Pearson Correlation have been compared as well. Besides, reviews have been crawled from the play store for better understanding. The analysis on review tells the top positive and negative keywords in free and paid apps. The system will help to give an overview of google play stores current condition.


  1. Worldwide mobile app revenues in 2015, ”Mobile app revenues 2015-2020 — Statistic”, Statista, 2018. [Online]. Available: worldwide -mobile-app-revenue-forecast/.
  2. ”Mobile Operating System Market Share Worldwide — StatCounter Global Stats”, StatCounter Global Stats, 2018. [Online]. Available: mobile/worldwide.
  3. W. Martin, F. Sarro, Y. Jia, Y. Zhang and M. Harman, ”A Survey of App Store Analysis for Software Engineering”, IEEE Transactions on Software Engineering, vol. 43, no. 9, pp. 817-847, 2017.
  4. Mark Harman, Yue Jia and Yuanyuan Zhang (2012). App store mining and analysis: MSR for app stores. 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).
  5. T. Johann, C. Stanik, A. B. and W. Maalej, ”SAFE: A Simple Approach for Feature Extraction from App Descriptions and App Reviews”, 2017 IEEE 25th International Requirements Engineering Conference (RE), 2017.
  6. N. Chen, J. Lin, S. Hoi, X. Xiao and B. Zhang, ”AR-miner: mining informative reviews for developers from mobile app marketplace”, Proceedings of the 36th International Conference on Software Engineering - ICSE 2014, 2014.
  7. E. Guzman and W. Maalej, ”How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews”, 2014 IEEE 22nd International Requirements Engineering Conference (RE), 2014.
  8. A. Al-Subaihin, F. Sarro, S. Black, L. Capra, M. Harman, Y. Jia and Y. Zhang, ”Clustering Mobile Apps Based on Mined Textual Features”, Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement - ESEM ’16, 2016.
  9. L. Villarroel, G. Bavota, B. Russo, R. Oliveto and M. Di Penta, ”Release planning of mobile apps based on user reviews”, Proceedings of the 38th International Conference on Software Engineering - ICSE ’16, 2016.
  10. J. Davril, E. Delfosse, N. Hariri, M. Acher, J. Cleland- Huang and P. Heymans, ”Feature model extraction from large collections of informal product descriptions”, Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2013, 2013.
  11. M. Acher, A. Cleve, G. Perrouin, P. Heymans, C. Vanbeneden, P. Collet and P. Lahire, ”On extracting feature models from product descriptions”, Proceedings of the Sixth International Workshop on Variability Modeling of Software- Intensive Systems - VaMoS ’12, 2012.
  12. H. Dumitru, M. Gibiec, N. Hariri, J. Cleland-Huang, B. Mobasher, C. Castro-Herrera and M. Mirakhorli, ”Ondemand feature recommendations derived from mining public product descriptions”, Proceeding of the 33rd international conference on Software engineering - ICSE ’11, 2011.
  13. H. Yu, Y. Lian, S. Yang, L. Tian and X. Zhao, ”Recommending Features of Mobile Applications for Developer”, Advanced Data Mining and Applications, pp. 361-373, 2016.


Mobile Apps, Mining Software Repositories, Correlation Analysis , Extract Review