Decision support grievance redressal system using sentence sentiment analysis

After effective implementation of online grievance redressal frameworks by various government offices, the lodging of grievances by common citizens has increased many folds. As the number exponentially builds up, it challenges the administration to redress the grievances appropriately, proficiently and satisfactorily. In this paper the authors are proposing a model, applying various text mining and sentiment analysis algorithms, on the content of the grievance, to categorize the grievances submitted to the grievance cell of an "Administrator" having public interface. Using the proposed model, the grievances are organized as High Priority, Medium Priority and Low Priority. It helps in the decision making process of the concerned government officials for redressing the Top Priority grievances within a specified time period, in contrast to Medium and Low Priority complaints. This enables the needy and common citizen to get appropriate public services delivery and government support which increases their confidence and faith on the administration and governmental services.