Determination of the Number of Topics Intrinsically: Is It Possible?
This addresses the problem of unreliable topic number estimation for researchers in topic modeling, but it is incremental as it highlights limitations without introducing new solutions.
This study compared various methods for estimating the number of topics in topic models on public corpora, finding that intrinsic methods are unreliable and the number of topics depends on the method and model rather than being an absolute property of the corpus.
The number of topics might be the most important parameter of a topic model. The topic modelling community has developed a set of various procedures to estimate the number of topics in a dataset, but there has not yet been a sufficiently complete comparison of existing practices. This study attempts to partially fill this gap by investigating the performance of various methods applied to several topic models on a number of publicly available corpora. Further analysis demonstrates that intrinsic methods are far from being reliable and accurate tools. The number of topics is shown to be a method- and a model-dependent quantity, as opposed to being an absolute property of a particular corpus. We conclude that other methods for dealing with this problem should be developed and suggest some promising directions for further research.