Automated Topic Analysis of the Voynich Manuscript

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this should be run from scratch, first identifying the topics using NMF separately for language A and language B. Just to avoid any possibility that the language information affects the outcome.

Hello oshfdk,

I’ve already run the NMF topic modeling separately for languages A and B. The results for language A are quite consistent, while language B appears a bit messier in terms of topic structure. However, splitting by language goes beyond my current goal.

What I’m aiming for is to detect which paragraphs “talk” about the same topic, locate them throughout the manuscript, view the illustrations on those pages, and try to infer what the content might be about. Of course, this is a very challenging task, since we have no clear understanding of the visual design or semantic meaning, but I wanted to explore this relationship across the entire manuscript.

If I follow your suggestion, I end up with topics 0 to N for language A and topics 0 to M for language B, but then I lose any potential connection between the two sets of topics. So, I feel this approach would fragment the analysis too much.

Topic modeling algorithms like NMF are often used in contexts such as news categorization—grouping articles into topics like politics, sports, etc. These models don’t understand the meaning of the text per se, but they can learn statistical associations between words. In known languages, we can assign semantic labels to topics once the model clusters them. But in the case of the Voynich Manuscript, we can’t yet interpret the clusters in semantic terms—so we just observe their structure and distribution.

Best regards