Automated Topic Analysis of the Voynich Manuscript

[quimqu]

Isn't the number of topics a parameter of LDA? Which approach/algorithm/implementation do you use for deciding how many topics there are?

I iterate through different topic numbers and calculate the Coherence Score, then I take the maximum. In the last case (without repetitive words) I got this scores:



I use this function that uses gensim LdaModel and CoherenceModel

Code:

# 4. Coherence computation function def compute_coherence(dictionary, corpus, texts, start=2, limit=15, step=1):     coherence_values = []     model_list = []         for num_topics in range(start, limit + 1, step):         model = LdaModel(             corpus=corpus,             id2word=dictionary,             num_topics=num_topics,             random_state=42,             passes=20,             alpha='auto'         )         model_list.append(model)                 coherencemodel = CoherenceModel(             model=model,             texts=texts,             dictionary=dictionary,             coherence='c_v'         )         coherence_values.append(coherencemodel.get_coherence())         return model_list, coherence_values