Subjective assessment is rampant in literature verification and title evaluation. While subjective assessment is a valid practice but creates a void in terms of validity of results. Intuition is unique and tend to depend on several other aspects which are not methodological. As a result of which, there may be a possibility of unreasonable yet unfair amount of personal opinion in action. Natural Language Processing (NLP) offers robust mechanisms or techniques to evaluate unstructured data. Latent Dirichlet Allocation (LDA) is one of such techniques which adds logic while processing unstructured but subjective data. This article explains suitability of topmodpy to perform Latent Semantic Analysis (LSA) using Latent Dirichlet Allocation (LDA). topmopy is a Python script and is a collection of 12 different functions each with a unique aim. This article shows as how to use topmopy module on certain data collected using a valid search criteria. topmodpy module found to have obtained these latent constructs related to search criteria. Hence the efficacy of the topmodpy module has been proved.