Thanks Joel and Andreas,
Joel, I think "highest ranking topic for each doc" is exactly what I am
looking for. Could you elaborate on the code please?
What would be dataset.target_names and dataset.target in my case -
http://lpaste.net/131649
Regards,
Kashyap
On Wed, Apr 29, 2015 at 3:08 AM, Joel Nothman <joel.noth...@gmail.com>
wrote:
> This shows the newsgroup name and highest scoring topic for each doc.
>
> zip(np.take(dataset.target_names, dataset.target),
> np.argmax(nmf.transform(tfidf), axis=1))
>
> I think something based on this should be added to the example.
>
> On 29 April 2015 at 07:01, Andreas Mueller <t3k...@gmail.com> wrote:
>
>> Clusters are one per data point, while topics are not. So the model is
>> slightly different.
>> You can get the list of topics for each sample using
>> NMF().fit_transform(X).
>>
>>
>> On 04/28/2015 01:13 PM, C K Kashyap wrote:
>>
>> Hi everyone,
>> I am new to scikit. I only feel sad for not knowing it earlier - it's
>> awesome.
>>
>> I am trying to do the following. Extract topics from a bunch of tweets.
>> I tried NMF (from the sample here -
>> http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html)
>> but I was not able to figure out how to list documents corresponding to the
>> extracted topics. Could someone please point me to an example that lists
>> the documents under each topic?
>>
>> When I got stuck with NMF, I thought of using kmeans (min batch). I am
>> just wondering though if clustering is a reasonable approach for "topics".
>>
>> I'd really appreciate any advice here.
>>
>> Thanks,
>> Kashyap
>>
>>
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