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),...
