Using Computational Methods to Explore Law in Sermons

An empirical study on the use of law in Christian sermons has so far been a blank space in research, especially when large corpora of sermons are examined. In this article, we present the first findings of the ongoing RUNIP project, in which computer-assisted methods are used and validated in sermon analysis. The process integrates manual coding via MaxQDA with machine learning techniques, notably contextual embeddings derived from Transformer architectures such as SBERT, enabling us to detect patterns across large corpora. We argue that embeddings in text analysis can help to complement a manual, human-based text analysis. Clustering based on sentence embeddings helps identify semantically related sermon passages, although the complexity and length of the original texts, as well as the nuanced theological language, pose challenges to computer-aided analysis. By bridging historical and contemporary sermon analysis with data science methodologies, we demonstrate how an interdisciplinary approach can expand our understanding of how preachers address law, norms, and moral questions in Christian sermons. This is demonstrated by qualitative results from the analysis of the large historical sermon corpus of Friedrich D. E. Schleiermacher.