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A network approach to topic models.

Martin Gerlach1,2, Tiago P Peixoto3,4, Eduardo G Altmann2,5

  • 1Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA.

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Summary
This summary is machine-generated.

This study introduces a novel topic modeling framework using community detection in complex networks. The approach, based on a stochastic block model (SBM), offers a principled method for uncovering topical structures in text data.

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Area of Science:

  • Computational linguistics
  • Machine learning
  • Network science

Background:

  • Topic models, like latent Dirichlet allocation (LDA), are widely used for extracting information from unstructured text.
  • Existing topic models face challenges including justification of priors, statistical discrepancies, and determining the number of topics.

Purpose of the Study:

  • To develop a more versatile and principled topic modeling framework.
  • To address limitations of current topic models by relating them to community detection in complex networks.

Main Methods:

  • Representing text corpora as bipartite networks of documents and words.
  • Adapting community detection methods, specifically a stochastic block model (SBM) with nonparametric priors, for topic modeling.

Main Results:

  • The SBM approach automatically detects the number of topics and hierarchically clusters words and documents.
  • Demonstrated superior performance of the SBM approach over LDA in statistical model selection on artificial and real corpora.

Conclusions:

  • The study establishes a formal link between community detection and topic modeling.
  • This framework offers a more principled and versatile approach to topic modeling, enabling cross-fertilization between fields.