A Multi Factor Algorithmic Approach to Prioritize Grievances

Abstract

Various online grievance systems to address the grievances of people are provided by different organizations on which the complainant can easily lodge grievances. Due to this, the lodging of grievances by citizens has increased many folds, but this makes the job of e-governance really difficult. A lot of times the same grievances are raised by many complainants so the officials have to go through the same grievances repeatedly. Due to this, critical grievances can go unnoticed or even trivial grievances might flood up the system. To solve such problems in grievances systems, we propose a priority algorithm, that ranks different grievances according to their priority of crucialness using machine learning which will help to address the more critical grievances in the required time as compared to less critical grievances. The algorithm considers different factors along with similarity of already raised grievances with new grievance and comparison of important keywords.