Author-Topic Models in gensim

Recently, gensim, a Python package for topic modeling, released a new version of its package which includes the implementation of author-topic models.

The most famous topic model is undoubtedly latent Dirichlet allocation (LDA), as proposed by David Blei and his colleagues. Such a topic model is a generative model, described by the following directed graphical models:


In the graph, \alpha and \beta are hyperparameters. \theta is the topic distribution of a document, z is the topic for each word in each document, \phi is the word distributions for each topic, and w is the generated word for a place in a document.

There are models similar to LDA, such as correlated topic models (CTM), where \phi is generated by not only \beta but also a covariance matrix \Sigma.

There exists an author model, which is a simpler topic model. The difference is that the words in the document are generated from the author for each document, as in the following graphical model. x is the author of a given word in the document.


Combining these two, it gives the author-topic model as a hybrid, as shown below:


The new release of Python package, gensim, supported the author-topic model, as demonstrated in this Jupyter Notebook.


  • I am also aware that there is another topic model called structural topic model (STM), developed for the field of social science. However, there is no Python package supporting this, but an R package, called stm, is available for it. You can refer to their homepage too.
  • I may consider including author-topic model and STM in the next release of the Python package shorttext.

  • gensim: Topic Modeling for Humans. [gensim]
  • Ólavur Mortensen, “New Gensim feature: Author-topic modeling. LDA with metadata,” RaRE Technologies Blog (Jan 2017). [RaRE]
  • David M. Blei, Andrew Y.Ng, Michael I Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, 3 (4–5): pp. 993–1022. (Jan 2003) [JMLR]
  • Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth, “The author-topic model for authors and documents,” Proceeding UAI ’04, 487-494 (2004). [ACL] [arXiv]
  • David Blei, John D. Lafferty, “Correlated Topic Models.” (2006) [CiteSeer]
  • “The author-topic model: LDA with metadata.” (Jan 2017) [Jupyter]
  • Margaret E. Roberts, Brandon M. Stewart, Edoardo M. Airold, “A Model of Text for Experimentation in the Social Sciences,” Journal of American Statistical Association 111 (515): 988-1003 (2016). [PDF]
  • “stm: Estimation of the Structural Topic Model.” [CRAN]
  • PyPI: shorttext. [PyPI] [WordPress]

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