|International Journal of Applied Information Systems
|Foundation of Computer Science (FCS), NY, USA
|Volume 12 - Number 26
|Year of Publication: 2019
|Authors: Md. Shahriar Kabir, Mohammad Shamsul Arefin
Md. Shahriar Kabir, Mohammad Shamsul Arefin . Google Play Store Data Mining and Analysis. International Journal of Applied Information Systems. 12, 26 ( December 2019), 1-5. DOI=10.5120/ijais2019451839
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 Statista.com 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.