Large Language Models, such as ChatGPT, offer numerous possibilities and prospects for academic research. However, there has been a gap in empirical research regarding their utilisation as keyword extraction and classification tools in qualitative research; perspectives from the social sciences and humanities have been notably limited. Moreover, Finnish-language data have not been used in previous studies. In this article, I aim to address these gaps by providing insights into the utilisation of ChatGPT and drawing comparisons with a rule-based Natural Language Processing method called Etuma. I will focus on assessing the effectiveness of classification and the methods’ adherence to scientific principles. The findings of the study indicate that the classic recall and precision trade-off applies to the methods: ChatGPT’s precision is high, but its recall is comparatively low, while the results are the opposite for Etuma. I also discuss the implications of the results and outline ideas for leveraging the strengths of both methods in future studies.