Topic Modeling is a text-mining technique. This method is categorized as unsupervised learning in the Machine Learning literature.
Latent Dirichlet Allocation (LDA) is a state-of-the-art implementation of Topic Modeling that proposes a generative process to model the collection of discrete textual datasets. LDA tries to classify documents based on their similarities into groups by capturing their semantic relations.
LDA has several realizations via Markov Chain Monte Carlo (MCMC), Variational Bayes (VB) method, Expectation Maximization (EM), Likelihood Maximization, and several hybrid methods. In this paper, we propose a novel interpretation of parameters and latent variables and the Iterative Variational Bayes (IVB) method. We provide detailed proof of updating rules in the appendix section of the paper as a supplementary.
We share all implementation and source codes in this repository. You can access a copy of the paper at the following link: