Scaling virtual assistant system execution via machine learning based data mining and event identification

Abstract

A system can receive textual input from a client device via a chatbot interface. The system can classify, by machine learning models, the textual input into categories, where the categories correspond to content-based categories and attribute categories. The system can determine, by the machine learning models, a polarity score for the textual input based on the categories. The system can detect, via a payroll processing system, a payroll-related event based on the polarity score and the profile. The system can identify, using the machine learning models, a churning event based on the payroll-related event. The system can generate, by the machine learning models, a command to adjust access control policies associated with the profile, where the command modifies access to a subset of payroll records maintained in a database.