Communication content tailoring

A message personalizing system for personalizing a message between a sender and a receiver, said message personalizing system comprising:

• a memory;
• and
• a processor in communication with the memory, the processor being configured to perform operations comprising:
• semantically analyzing a communication history between said sender and said receiver and adapted for forming a knowledge graph between a sender identifier identifying said sender and a receiver identifier identifying said receiver;
• deriving from said knowledge graph a formality level between said sender and said receiver using a first trained machine-learning model;
• analyzing parameter values of replies in said communication history to determine receiver impact score values;
• training a second machine-learning system to generate a model to predict said receiver impact score values based on said knowledge graph and said formality level;
• selecting a linguistic expression in a message being drafted;
• determining an expression intent of said linguistic expression;
• modifying said linguistic expression based on said formality level and said expression intent to generate a modified linguistic expression;
• a testing if said modified linguistic expression has an increased likelihood of a higher receiver impact score value using a third trained machine-learning model;
• and
• repeating said selecting said linguistic expression, determining said expression intent, modifying said linguistic expression, and testing for said higher receiver impact score until a stop criterion is met.

A computer program product for personalizing a message between a sender and a receiver, said computer program product comprising a computer readable storage medium having program instructions embodied therewith, said program instructions being executable by one or more computing systems or controllers to cause said one or more computing systems to:

• analyze, semantically, a communication history between said sender and said receiver to generate an analysis wherein said analysis is adapted for forming a knowledge graph between a sender identifier identifying said sender and a receiver identifier identifying said receiver,
• derive from said knowledge graph a formality level between said sender and said receiver using a first trained machine-learning model,
• analyze parameter values of replies in said communication history to determine receiver impact score values,
• train a second machine-learning system to generate a model to predict said receiver impact score value based on said knowledge graph and said formality level,
• select a linguistic expression in a message being drafted,
• determine an expression intent of said linguistic expression,
• generate a modified linguistic expression by modifying said linguistic expression based on said formality level and said expression intent to,
• test whether said modified linguistic expression has an increased likelihood of a higher receiver impact score value by using a third trained machine-learning model, and
• repeat said program instruction of said selection, said determination, said generation, and said test until a stop criterion is met.