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How to build a Term-Document-Matrix from a set of texts and a specific set of terms (tags)?

I have two data sets:

  • tag set (single type words php, htmletc.)

  • typing

Now I want to build a Term-Document-Matrix that represents the number of occurrences of an element tagsin an element text.

I looked at the R library tm and function TermDocumentMatrix, but I see no way to specify tags as input.

Is there any way to do this?

I am open to any tool (R, Python, etc.), although using R will be great.

Set the data as:

TagSet <- data.frame(c("c","java","php","javascript","android"))
colnames(TagSet)[1] <- "tag"

TextSet <- data.frame(c("How to check if a java file is a javascript script java blah","blah blah php"))
colnames(TextSet)[1] <- "text"

Now I would like to have TermDocumentMatrix TextSet according to TagSet.

I tried this:

myCorpus <- Corpus(VectorSource(TextSet$text))
tdm <- TermDocumentMatrix(myCorpus, control = list(removePunctuation = TRUE, stopwords=TRUE))


>inspect(tdm)
A term-document matrix (7 terms, 2 documents)

Non-/sparse entries: 8/6
Sparsity           : 43%
Maximal term length: 10 
Weighting          : term frequency (tf)

            Docs
Terms        1 2
  blah       1 2
  check      1 0
  file       1 0
  java       2 0
  javascript 1 0
  php        0 1
  script     1 0

but checking the text for words of the text, while I want to check for the presence of already defined tags.

+4
2
tdm.onlytags <- tdm[rownames(tdm)%in%TagSet$tag,]

, .

+10
DocumentTermMatrix(docs, list(dictionary = Dictionary$Var1))

,

+1

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