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https://issues.apache.org/jira/browse/SPARK-5571?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14624152#comment-14624152
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Feynman Liang commented on SPARK-5571:
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[~a...@jivesoftware.com], are you still working on this? I wanted to point out
[CountVectorizer|https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala]
was recently merged and seems appropriate for this task.
If you aren't working on this anymore, I would be happy to take this task.
> LDA should handle text as well
> ------------------------------
>
> Key: SPARK-5571
> URL: https://issues.apache.org/jira/browse/SPARK-5571
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.3.0
> Reporter: Joseph K. Bradley
>
> Latent Dirichlet Allocation (LDA) currently operates only on vectors of word
> counts. It should also supporting training and prediction using text
> (Strings).
> This plan is sketched in the [original LDA design
> doc|https://docs.google.com/document/d/1kSsDqTeZMEB94Bs4GTd0mvdAmduvZSSkpoSfn-seAzo/edit?usp=sharing].
> There should be:
> * runWithText() method which takes an RDD with a collection of Strings (bags
> of words). This will also index terms and compute a dictionary.
> * dictionary parameter for when LDA is run with word count vectors
> * prediction/feedback methods returning Strings (such as
> describeTopicsAsStrings, which is commented out in LDA currently)
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