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

A method, comprising:

• receiving, from a client device, a textual input via a chatbot interface associated with a profile;
• classifying, by one or more machine learning models, the textual input into one or more categories, the one or more categories corresponding to content-based categories and attribute categories, wherein the attribute categories relate to at least one of a positive emotion, a negative emotion, or a neutral emotion;
• determining, by the one or more machine learning models, a polarity score for the textual input based on the one or more categories;
• detecting, via a payroll processing system, a payroll-related event based on the polarity score and the profile;
• identifying, using the one or more machine learning models, a churning event based on the payroll-related event, wherein the churning event relates to a disengagement of the profile from the payroll processing system;
• and
• in response to identifying the churning event, generating, by the one or more machine learning models, a command to adjust access control policies associated with the profile, wherein the command modifies access to a subset of payroll records maintained in a database.

A non-transitory computer readable medium including one or more instructions stored thereon and executable by a processor to:

• receive, by the processor, from a client device, a textual input via a chatbot interface associated with a profile;
• classify, by the processor, using one or more machine learning models, the textual input into one or more categories, the one or more categories corresponding to content-based categories and attribute categories, wherein the attribute categories relate to at least one of a positive emotion, a negative emotion, or a neutral emotion;
• determine, by the processor, using the one or more machine learning models, a polarity score for the textual input based on the one or more categories;
• detect, by the processor, via a payroll processing system, a payroll-related event based on the polarity score and the profile;
• identify, by the processor, using the one or more machine learning models, a churning event based on the payroll-related event, wherein the churning event relates to a disengagement of the profile from the payroll processing system;
• and
• in response to identifying the churning event, generate, by the processor, using the one or more machine learning models, a command to adjust access control policies associated with the profile, wherein the command modifies access to a subset of payroll records maintained in a database.