Topic modeling and text clustering

In TM, a topic is defined by a cluster of words, with each word in the cluster having a probability of occurrence for the given topic, and different topics having their respective clusters of words along with corresponding probabilities. Different topics may share some words, and a document can have more than one topic associated with it. So in short, we have a collection of text datasets—that is, a set of text files. Now the challenging part is finding useful patterns about the data using LDA.

There is a popular TM approach, based on LDA, where each document is considered a mixture of topics and each word in a document is considered randomly drawn from a document's topics. The topics are considered hidden and must be uncovered via analyzing joint distributions to compute the conditional distribution of hidden variables (topics), given the observed variables and words in documents. The TM technique is widely used in the task of mining text from a large collection...