Topic Discovery

The main goal of topic modeling is to find a set of topics that can be used to classify a set of documents. These topics are implicit because we do not know what they are beforehand, and they are unnamed.

The number of topics could vary from around 3 to, say, 400 (or even more) topics. Since it is the algorithm that discovers the topics, the number is generally fixed as an input to the algorithm, except in the case of non-parametric models in which the number of topics is inferred from the text. These topics may not always directly correspond to topics that a human would find meaningful. In practice, the number of topics should be much smaller than the number of documents. In general, the number of topics specified in a parametric model ought to be greater than or equal to the expected number of topics in the text. In other words, one should err on the side of a greater number of topics rather than fewer topics. This is because fewer topics can cause a problem for...