R: keyword extraction with naive bayes -

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i've got documents , each document i've got keywords. want use these documents train model. data looks follows: 

postag <- list()    tfidf <- list()    labels <- list()

one element of each list represents document. there 50 documents. postag[[1]] vector part of speech tagging each word in document 1, tfidf[[1]] vector tfidffactor each word in document 1, labels[[1]] vector labels (0 = no keyword, 1 = keyword). note: words each document ordered: postag[[1]][1] pos first word in document 1, tfidf[[1]][1] tfidffactor same word in document 1, , labels[[1]][1] says if word keyword.

now want use these 50 documents train (naive bayes) model predicts if word keyword or not. features tfidf factor , pos. me?

you can use e1071 package:

data(iris) m <- naivebayes(species ~., data = iris) predict(m, iris)

note column species must factor variable example:

iris["species"]<- as.factor(iris["species"])

but in case species factor already, don't have change it.


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