CODEWITHSUNDEEP
rtext-miningdata-analysistmcosine-similarity
Rich_Rich
Rich_Rich

Reputation: 439

Cosine Similarity with two Term Frequency vectors in R

I made usingtm in R a DocumentTermMatrix (dtm). if I understand correctly, this matrix displays for each document how often each possible term occurs. Now I can inspect this matrix and I get

    Terms
Docs     can design door easy finish include light provide use water
  176004   1      2   11    8      0       3     3       4   4     4
  181288   1      2   11    8      0       2     3       4   4     4
  182465   4      4    0    2      0       0    42      13   6     0
etc.

How can I now retrieve the vector of (for example) document 181288? So I will get something like

1      2   11    8      0       2     3       4   4     4 ………

Also, it says my dtm's sparsity is 100%, is it (by approximation) 100% empty?

Upvotes: 0

Views: 385

Answers (1)

phiver
phiver

Reputation: 23608

To retrieve your vector you can do it in multiple ways.

simple, but not recommended unless for quick test:

my_doc <- inspect(dtm[dtm$dimnames$Docs == "181288",])

Doing it like this limits you to what inspect does and this only shows a maximum of 10 documents.

Better way, create a selection list if you want to and filter the dtm. This keeps the sparse matrix format, then transform what you need into a data.frame for further manipulation if needed.

my_selection <- c("181288", "182465")

# selection in case of dtm
my_dtm_selection <- dtm[dtm$dimnames$Docs %in% my_selection, ]

# selection in case of tdm
my_tdm_selection <- tdm[, tdm$dimnames$Docs %in% my_selection]

# create data.frame with document names as first column, followed by the terms
my_df_selection <- data.frame(docs = Docs(my_dtm_selection), as.matrix(my_dtm_selection))

The answer to your second question: yes, almost empty. Or better framed, a lot of empty cells. But you might have more data than you think if you have a lot of documents and terms.

Upvotes: 1

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