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.

P.S.:

• 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.