A Performance Evaluation of Correlated and Dynamic Topic Modeling on a QA Dataset

Topic modeling is a set of algorithms which is used to mine the data that is hidden inside a large collection of documents. In this paper we discusses about the Correlated topic modeling and dynamic topic modeling in detail and comparing their performance on a question answer dataset based on autism. Log Likelihood and Perplexity are the measures used for comparing the discussed topic modeling algorithms.