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Topic models are widely used in natural language processing, allowingresearchers to estimate the underlying themes in a collection of documents.Most topic models use unsupervised methods and hence require the additionalstep of attaching meaningful labels to estimated topics. This process of manuallabeling is not scalable and suffers from human bias. We present asemi-automatic transfer topic labeling method that seeks to remedy theseproblems. Domain-specific codebooks form the knowledge-base for automated topiclabeling. We demonstrate our approach with a dynamic topic model analysis ofthe complete corpus of UK House of Commons speeches 1935-2014, using the codinginstructions of the Comparative Agendas Project to label topics. We show thatour method works well for a majority of the topics we estimate; but we alsofind that institution-specific topics, in particular on subnational governance,require manual input. We validate our results using human expert coding.

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