Abstract: Methods and systems for generating recommendation data to address behaviors exhibited by an entity are described. A processor may construct a finite automaton based on entity data associated with the entity. Each state of the finite automaton may represent a sentiment, and the finite automaton may accept a language representing a set of behaviors. The processor may receive a request comprising an input behavior string. The processor may apply the input behavior string on the finite automata to determine an output string. The processor may identify at least one neural model mapped to the output string, where the identified neural model comprises logic that facilitates interpretation of a cause of the behaviors among the input behavior string. The processor may generate the recommendation data using the identified neural model, where the recommendation data comprises a recommendation to address the behaviors among the input behavior string.