Hallo,
I have question concerning the Latent Dirichlet Allocation. The results I get
from using it are a bit confusing.
At first I use about 3000 documents. In the preparation with the
CountVectorizrt I use the following parameters : max_df=0.95 and min_df=0.05.
For the LDA fit I use the bath learning method. For the other parameters I have
tried many different values. However regardless of which configuration I used,
I face one common problem. I get topics that are never used in any of the docs
and said topics all show the same structure (topic-word-distribution). I even
tried gensim with the same configuration as scikit, yet I still encountered
this problem. I also tried lowering the number of topics in the model, but this
did not lead to the expected results either. For 100 topics, 20-27 were still
affected by this problem, for 50 topics, there were still 2-8 of them being
affected, depending on the parameter setting.
Does anybody have an idea as to what might be causing this problem and how to
resolve it?
Best regards,
Christian Rockenkamm
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