Topic modelling is often described as a text-mining tool for conducting a study of hidden semantic structures of a text or a text corpus by extracting topics from a document or a collection of documents. Yet, instead of one singular method, there are various tools for topic modelling that can be utilised for historical research. Dynamic topic models, for example, are often constructed temporally year by year, which makes it possible to track and analyse the ways in which topics change over time. This chapter provides a case example on topic modelling historical primary sources. The chapter uses two tools to carry out topic modelling, MALLET and Dynamic Topic Model (DTM), in one dataset, containing texts from the early 19th-century German-language press which have been subjected to optical character recognition (OCR). All of these texts were discussing humanism, which was a newly emerging concept before mid-century, gaining various meanings in the public discourse before, during and after the 1848–1849 revolutions. Yet, these multiple themes and early interpretations of humanism in the press have been previously under-studied. By analysing the evolution of the topics between 1829 and 1850, this chapter aims to shed light on the change of the discourse surrounding humanism in the early 19th-century German-speaking Europe.