Topic count graph demonstrating the optimization rationale for our stm algorithm's choice of 50 topic models. The 'K-value' shows the optimum number of 'structural topic models' the algorithm has to go through the text to find the optimum semantic coherence. In other words, the K number designates the optimum number of structural topic models in texts that have the highest statistical coherence coefficients. Often, K values are assigned by the programmer and an optimum number gets eyeballed after several trial and error runs. K-value optimization uses machine learning to iterate through the text multiple times to find the optimum K-value by statistical clustering of frequently collocated word combinations.