"Probabilistic topic models aka Topic Models are probabilistic generative models which uncovers hidden thematic structures in large collection of documents. Its statistical nature does not need any information other than the text itself. It does not use metadata, labels or annotations to build up the topics from the text. One needs to define only the number of clusters(this is quite hard, albeit). There are various different inference algorithms that enables fast inference in the corpus and some of the them also work in an online fashion so that one does not have to load the data into the memory." - Sean McBride