How Many Topics? Stability Analysis for Topic Models
19 September 2014
21T12:24:57Z June 2015
Topic modeling refers to the task of discovering the underlyingthematic structure in a text corpus, where the output is commonlypresented as a report of the top terms appearing in each topic. Despitethe diversity of topic modeling algorithms that have been proposed, acommon challenge in successfully applying these techniques is the selectionof an appropriate number of topics for a given corpus. Choosingtoo few topics will produce results that are overly broad, while choosingtoo many will result in theover-clustering of a corpus into many small,highly-similar topics. In this paper, we propose a term-centric stabilityanalysis strategy to address this issue, the idea being that a model withan appropriate number of topics will be more robust to perturbations inthe data. Using a topic modeling approach based on matrix factorization,evaluations performed on a range of corpora show that this strategy cansuccessfully guide the model selection process.
Science Foundation Ireland
Type of Material
Machine Learning and Knowledge Discovery in Databases. Proceedings Part I.
Copyright (Published Version)
Status of Item
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML '14), 15-19 September, Nancy, France
This item is made available under a Creative Commons License