Transfer Topic Labeling with Domain-Specific Knowledge Base: An Analysis of UK House of Commons Speeches 1935-2014

Topic models are among the most widely used methods in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models use unsupervised methods and hence require the additional step of attaching meaningful labels to estimated topics. This process of manual labeling is not scalable and often problematic because it depends on the domain expertise of the researcher and may be affected by cardinality in human decision making. As a consequence, insights drawn from a topic model are difficult to replicate. We present a semi-automatic transfer topic labeling method that seeks to remedy some of these problems. We take advantage of the fact that domain-specific codebooks exist in many areas of research that can be exploited for automated topic labeling. We demonstrate our approach with a dynamic topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We show that our method works well for a majority of the topics we estimate, but we also find institution-specific topics, in particular on subnational governance, that require manual input. The method proposed in the paper can be easily extended to other areas with existing domain-specific knowledge bases, such as party manifestos, open-ended survey questions, social media data, and legal documents, in ways that can add knowledge to research programs.