Measuring immigration rhetoric in the UK parliament

Migration is a defining phenomenon of our time, around which heightened political rhetoric creates the impression of unprecedented political conflict. In the UK, this perception coincides with major political developments, such as the Brexit referendum and the rise of anti-immigration politics. However, systematic understanding of elite discourse on this subject remains limited. We introduce a novel machine learning pipeline that combines LLMs and human inputs to analyze sentiment towards immigration in political speech. Applying our model to spoken contributions in the UK House of Commons from 1923 to 2025, we report high classifier performance and stability across modeling choices. Our descriptive findings validate and extend existing qualitative research: Labour Members of Parliament (MPs) have expressed more positive sentiment than Conservatives since the early 1960s, with party divergence widening in the early 1980s. Furthermore, cross-party rhetoric has grown increasingly negative over the past fifteen years, with Conservative and Reform UK MPs now using a more negative tone than any party has in the period studied. We discuss how this pipeline and dataset can support future research, highlighting use cases on the linkages between elite discourse and public opinion, discourse moralization, and positive and negative frames.