Determination of the Number of Topics Intrinsically: Is It Possible?

TL;DR

This paper evaluates existing methods for estimating the number of topics in models, revealing their unreliability and emphasizing the dependence on specific models and methods rather than the data itself.

Contribution

It provides a comparative analysis of current techniques for determining the number of topics, highlighting their limitations and proposing directions for future research.

Findings

Intrinsic methods are unreliable for estimating topic numbers.

The number of topics depends on the model and method used.

Current practices lack a definitive approach for this estimation.

Abstract

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.