Predicting an Effective Android Application Release Based on User Reviews and Ratings

Android applications' acceptance rate is growing rapidly, which causes a huge competition among the developers. To develop acceptable and successful application, user reviews are very important source of information. Especially for a better release of new version apps, it is necessary to know the users' demand. However, it is very difficult to analyze and maintain all the reviews manually due to limited resource and time. Only a little solution domain have discovered to categorize user reviews automatically. However, those works have only categorized the reviews without giving any suggestion regarding which category is more significant. This paper proposed an automatic category prioritization technique by analyzing the corresponding ratings and the reviews. This approach has been experimented on Opera and Firefox reviews by collecting data from Google play store. Experimental results reported that this approach can effectively categorized and prioritized user reviews, where the proposed prioritized categories play the significant role in next version of android application release.