Text mining of big data using R Server
Thank you for your reply.
By accident I found out that MicrosoftML (https://msdn.microsoft.com/en-us/microsoft-r/microsoftml-introduction) offers just what I need - by using the featurizeText functions offered within Microsoft R client and R server.
Maybe it helps others - I found their example  and could it translate to my data (featurizeText() help under Microsoft ML).

trainReviews &lt;- data.frame(review = c( 
        "This is great",
        "I hate it",
        "Love it",
        "Do not like it",
        "Really like it",
        "I hate it",
        "I like it a lot",
        "I kind of hate it",
        "I do like it",
        "I really hate it",
        "It is very good",
        "I hate it a bunch",
        "I love it a bunch",
        "I hate it",
        "I like it very much",
        "I hate it very much.",
        "I really do love it",
        "I really do hate it",
        "Love it!",
        "Hate it!",
        "I love it",
        "I hate it",
        "I love it",
        "I hate it",
        "I love it"),
     like = c(TRUE, FALSE, TRUE, FALSE, TRUE,
        FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE,
        FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, 
        FALSE, TRUE, FALSE, TRUE), stringsAsFactors = FALSE
    )

    testReviews &lt;- data.frame(review = c(
        "This is great",
        "I hate it",
        "Love it",
        "Really like it",
        "I hate it",
        "I like it a lot",
        "I love it",
        "I do like it",
        "I really hate it",
        "I love it"), stringsAsFactors = FALSE)


outModel &lt;- rxLogisticRegression(like ~ reviewTran, data = trainReviews,
    mlTransforms = list(featurizeText(vars = c(reviewTran = "review"),
    stopwordsRemover = stopwordsDefault(), keepPunctuations = FALSE)))
# 'hate' and 'love' have non-zero weights summary(outModel)