How many distinct words are in your dataset?
On 27 January 2016 at 00:21, Rockenkamm, Christian <
c.rockenk...@stud.uni-goettingen.de> wrote:
> 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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