For large text corpora, the task of extracting and following information about topics, authors, and opinions is very challenging. Applications are numerous and relate to various domains, including social networks. The authors’ proposed model is a novel contribution to this research area. It is highly related to other probabilistic models, such as latent Dirichlet allocation (LDA) [1] and McCallum’s model [2].

In this paper, Rosen-Zvi et al. propose a new generative model for document collection. Their author-topic (AT) model differs from McCallum’s in the way that each author is associated with a distribution over topics. This approach leads to numerous applications such as word sense disambiguation and information retrieval (IR), which are described in detail. Although they present a well-grounded, detailed theoretical basis, the choice of fixing hyperparameters α and β could have been discussed in more depth. The paper lacks a formal and experimental comparison with a different type of approach, such as a graph-based one [3]. Also, the authors compare their approach with term frequency-inverse document frequency (tf-idf) as if it were an algorithm. In fact, tf-idf is a formula that (sometimes) gives a better representation of textual data, typically in an IR task. Hence, the comparison between AT models and tf-idf needs more in-depth investigation.

In summary, the authors present an interesting and well-grounded model. That being said, potential readers should be fairly familiar with Bayesian statistics.