Topic models are widely used 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 suffers from human bias. We present a
semi-automatic transfer topic labeling method that seeks to remedy these
problems. Domain-specific codebooks form the knowledge-base for automated topic
labeling. We demonstrate our approach with a dynamic topic model analysis of
the complete corpus of UK House of Commons speeches 1935-2014, 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 that institution-specific topics, in particular on subnational governance,
require manual input. We validate our results using human expert coding.