Contemporary Natural Language Processing (NLP) architectures and sentiment analysis paradigms are inherently constrained by stochastic associative weighting and lexical entropy. To rectify this cognitive distortion, this paper details a Deterministic Thermodynamic Model of Semantics. This novel architecture transitions semantic processing from probabilistic inference to rigid kinematic calculation by applying fixed physical constraints to abstract vocabulary. Lexical nodes are parameterized via Typographical Information Density, scaled by a Lexical Symmetry Coefficient, and mapped across spatial-temporal transit paths using precise thermodynamic variables. Crucially, by operating as a closed-loop "Kinetic Sandbox," the model enables unprecedented hyper-scale information extraction. It systematically identifies and bridges epistemological voids, mathematically deducing missing conceptual links in the same manner missing physical elements are predicted via atomic mass. The resulting zero-entropy computational matrix provides a mathematically defensible methodology for quantifying language, establishing an absolute baseline for advanced intelligence extraction, cognitive systems architecture, and predictive behavioral;modeling.
Christopher Jacob Smith (Mon,) studied this question.