LDA is the most popular method among the different methods of topic modeling. It is a form of text data mining and machine learning, where backtracking is performed to figure out the topic for the document. It also involves the use of probability, as it is a generative probabilistic model.

LDA represents the documents as a mixture of topics that will give a topic based on probability.

Any given document has a greater or lesser chance of having a certain word as its underlying topic; for example, given a document about sports, the probability of the word "cricket" occurring is higher than the probability of the word "Android One Phone". If the document is about mobile technology, then the probability of the word "Android One Phone" will be higher than the word "cricket". Using a sampling method, some words are selected from a document as a topic using Dirichlet distribution in a semi random manner. These randomly selected topics may not be the best suited as the potential...