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LDA and tf-idf document term matrix #1
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Try this stackoverflow explanation with a workaround. I have never done it myself. |
Hi Ted
My plan is to use TF-IDF as a tool to take some terms out of the
corpus after the analytical pre-processing. As you know, words (that are
not in a list of stop words) with high frequency do not always contribute
meaningful information to the document.
'Term frequency' shows only how frequent the terms appear in the
document, but TF-IDF weights these term with 'rarity'. I would like to
clean the corpus in this fashion before applying LDA with the corpus.
Thank you for your elaboration
Sapphasak
2017-08-09 16:19 GMT+01:00 kwartler <notifications@github.com>:
… Try this stackoverflow
<https://stackoverflow.com/questions/33770287/documenttermmatrix-needs-to-have-a-term-frequency-weighting-error>
explanation with a workaround. I have never done it myself.
Apparently, LDA requires TF not TfIdf because its measuring distributions.
I wouldn't recommend using LDA this way. I suppose you could do some data
wrangling to get it into a useable format for LDA but the authors of LDA
clearly wants Tf.
What exactly are you trying to accomplish?
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Was giving this some thought and I think you could perform some sort of tf-idf TDM, then apply a heuristic to identify the low quality terms.
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Dear Ted
Question: Can we input tf-idf document term matrix into Latent Dirichlet Allocation (LDA)? if yes, how?
it does not work in my case and the LDA function requires the 'term-frequency' document term matrix.
Thank you
(I make a question as concise as possible. So, if you need more details, I can add
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