Method and system for automatic determination of human sentiment

A system for determining a sentiment of a participant in an interaction comprising a plurality of participants, the system comprising:

• a non-transitory memory device, wherein modules of instruction code are stored, and at least one processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the at least one processor is configured to:
• obtain a plurality of textual segments, each representing a portion of the interaction, and labeled according to a specific participant;
• infer a pretrained LM model on one or more textual segments of the plurality of textual segments, to generate one or more respective, semantic embedding vectors, each representing a semantic meaning of the respective textual segment in a semantic vector space;
• compile a semantic vector set comprising:
• (i) a target semantic embedding vector, corresponding to a target textual segment of a target participant of the plurality of participants, and (ii) one or more peripheral semantic embedding vectors, respectively corresponding to one or more peripheral textual segments of the plurality of textual segments;
• and
• infer a composite ML-based model on the semantic vector set, to classify a sentiment of the target participant, as expressed in the target textual segment.