I use quanteda to build a document term matrix:
library(quanteda)
mytext = "This is my old text"
dtm <- dfm(mytext, tolower=T)
convert(dtm,to="data.frame")
Which yields:
doc_id this is my old text
1 text1 1 1 1 1 1
I need to fit "new" text (a new corpus) to my existing dtm (using the same vocabulary so that the same matrix columns will be present)
Suppose my "new" text/corpus would be:
newtext = "This is my new text"
How can I fit this "new" text/corpus to the existing dtm vocabulary, so to get a matrix like:
doc_id this is my old text
1 text1 1 1 1 0 1
You want dfm_match(), before converting to data.frame.
library(quanteda)
## Package version: 2.1.2
mytext <- c(oldtext = "This is my old text")
dtm_old <- dfm(mytext)
dtm_old
## Document-feature matrix of: 1 document, 5 features (0.0% sparse).
## features
## docs this is my old text
## oldtext 1 1 1 1 1
newtext <- c(newtext = "This is my new text")
dtm_new <- dfm(newtext)
dtm_new
## Document-feature matrix of: 1 document, 5 features (0.0% sparse).
## features
## docs this is my new text
## newtext 1 1 1 1 1
To match them up, use dfm_match() to conform the new dfm to the feature set and order of the old one:
dtm_matched <- dfm_match(dtm_new, featnames(dtm_old))
dtm_matched
## Document-feature matrix of: 1 document, 5 features (20.0% sparse).
## features
## docs this is my old text
## newtext 1 1 1 0 1
convert(dtm_matched, to = "data.frame")
## doc_id this is my old text
## 1 newtext 1 1 1 0 1