Topic modeling
From Cohen Courses
Jump to navigationJump to searchThis is a problem discussed in Social Media Analysis 10-802 in Fall 2012.
The goal of Topic modeling is to estimate parameters for a Topic model, which are probabilistic, generative models that use Hierarchical Bayesian Analysis of a document collection (data) to uncover the underlying semantic structure. Approaches such as Expectation Maximization (EM) and Gibbs sampling are used to estimate the parameters, and generate semantically coherent topics, from unlabeled data.
