Applying Naive Bayes Classification to Google Play Apps Categorization
This addresses the challenge of app discovery and earnings for developers on the Google Play Store, but it is incremental as it uses an existing method on new data.
The authors tackled the problem of categorizing apps on the Google Play Store by applying Naive Bayes classifiers to open metadata from top developer apps, achieving good performance that could automate app categorization for publishers.
There are over one million apps on Google Play Store and over half a million publishers. Having such a huge number of apps and developers can pose a challenge to app users and new publishers on the store. Discovering apps can be challenging if apps are not correctly published in the right category, and, in turn, reduce earnings for app developers. Additionally, with over 41 categories on Google Play Store, deciding on the right category to publish an app can be challenging for developers due to the number of categories they have to choose from. Machine Learning has been very useful, especially in classification problems such sentiment analysis, document classification and spam detection. These strategies can also be applied to app categorization on Google Play Store to suggest appropriate categories for app publishers using details from their application. In this project, we built two variations of the Naive Bayes classifier using open metadata from top developer apps on Google Play Store in other to classify new apps on the store. These classifiers are then evaluated using various evaluation methods and their results compared against each other. The results show that the Naive Bayes algorithm performs well for our classification problem and can potentially automate app categorization for Android app publishers on Google Play Store