Hierarchical Topic Presence Models
This work addresses the need for more flexible topic modeling in hierarchical document structures, but it is incremental as it builds on existing Poisson factor analysis methods.
The authors tackled the problem of analyzing text from documents nested in known groups, such as web pages within web sites, by extending Poisson factor analysis topic models to include hierarchical topic presence parameters and local topics, and applied it to public health department web pages to extract topical information and national patterns.
Topic models analyze text from a set of documents. Documents are modeled as a mixture of topics, with topics defined as probability distributions on words. Inferences of interest include the most probable topics and characterization of a topic by inspecting the topic's highest probability words. Motivated by a data set of web pages (documents) nested in web sites, we extend the Poisson factor analysis topic model to hierarchical topic presence models for analyzing text from documents nested in known groups. We incorporate an unknown binary topic presence parameter for each topic at the web site and/or the web page level to allow web sites and/or web pages to be sparse mixtures of topics and we propose logistic regression modeling of topic presence conditional on web site covariates. We introduce local topics into the Poisson factor analysis framework, where each web site has a local topic not found in other web sites. Two data augmentation methods, the Chinese table distribution and Pólya-Gamma augmentation, aid in constructing our sampler. We analyze text from web pages nested in United States local public health department web sites to abstract topical information and understand national patterns in topic presence.