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, and are hyperparameters. is the topic distribution of a document, is the topic for each word in each document, is the word distributions for each topic, and is the generated word for a place in a document.
There are models similar to LDA, such as correlated topic models (CTM), where is generated by not only but also a covariance matrix .
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. 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]