The proposed combination of zero-shot LLM annotation and topic modelling has several advantages. On a technical level, it allows the automatic analysis of a video corpus with reasonable resources. Furthermore, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> produces concise annotations but is too expensive for several thousand videos. Compared to GPT-4o mini, the LLaVA annotations proved not as accurate, but this difference did not affect the efficiency of our pipeline, precisely the output of the Ensemble LDA topic modelling. Another inconvenience of LLaVA is its relatively low speed: processing the 60-minute documentary took approximately 72 hours on a single A100 GPU, while GPT-4o mini completed the same task in only 3 hours and 30 minutes through the cloud API accessed via Jupyter Notebook. Possible speedups include reducing the frame size from Full HD to a lower resolution and using more focused frame sampling techniques.