Knowledge Engineering Using Natural Language Processing of User Reviews for Bahrain’s Mobile Government Applications

Abstract

The Kingdom of Bahrain has launched various mobile government applications that work side by side with the national e-government portal by providing a range of government services, while services offered on mobile applications are still limited compared to the e-government portal, utilization of end users’ feedback is vital to improve and enhance functionality to ensure proper digital integration on the mobile environment. In this research, knowledge engineering using natural language processing is implemented to analyze 20,000 user reviews of the top four most reviewed google play mobile government applications in Bahrain. Two resampling techniques were used to under-sample and over-sample unbalanced datasets; Near-Miss and Synthetic Minority Oversampling combined with Edited Nearest Neighbor. The performance of three classifiers for data analysis was compared and assessed before and after data resampling. Results suggest that the Random Forest classifier outperformed Artificial Neural Network and LogitBoost.