R: partial match dictionary terms using grep and tm package - r

Question

Hi: I have a dictionary of negative terms that has been prepared by others. I am not sure how they have gone about doing the stemming, but it looks like they have used something other than the Porter Stemer. The dictionary has a wildcard character (*) that I think is supposed to enable a stemming to happen. But I don't know how to make use of that with grep() or the tm package in the R context, so I stripped it out hoping to find a way to grep the partial match.
So the original dictionary looks like this
#load libraries
library(tm)
#sample dictionary terms for polarize and outlaw
negative<-c('polariz*', 'outlaw*')
#strip out wildcard
negative<-gsub('*', '', negative)
#test corpus
test<-c('polarize', 'polarizing', 'polarized', 'polarizes', 'outlaw', 'outlawed', 'outlaws')
#Here is how R's porter stemmer stems the text
stemDocument(test)
So, if I stemmed my corpus with R's stemmer, terms like 'outlaw' would be found in the dictionary, but it wouldn't match terms like 'polarized' and such because they would be stemmed differently than what is found in the dictionary.
So, what I would like to have is some way to have the tm package match only exact parts of each word. So, without stemming my documents, I would like it to be able to pick out 'outlaw' in the term 'outlawing' and 'outlaws' and to pick out 'polariz' in 'polarized', 'polarizing and 'polarizes'. Is this possible?
#Define corpus
test.corp<-Corpus(VectorSource(test))
#make Document Term Matrix
dtm<-documentTermMatrix(test.corp, control=list(dictionary=negative))
#inspect
inspect(dtm)

Answer 1

I haven't seen any tm answers, so here's one using the quanteda package as an alternative. It allows you to use "glob" wildcard values in your dictionary entries, which is the default valuetype for quanteda's dictionary functions. (See ?dictionary.) With this approach, you do not need to stem your text.
library(quanteda)
packageVersion("quanteda")
## [1] ‘0.9.6.2’
# create a quanteda dictionary, essentially a named list
negative <- dictionary(list(polariz = 'polariz*', outlaw = 'outlaw*'))
negative
## Dictionary object with 2 key entries.
## - polariz: polariz*
## - outlaw: outlaw*
test <- c('polarize', 'polarizing', 'polarized', 'polarizes', 'outlaw', 'outlawed', 'outlaws')
dfm(test, dictionary = negative, valuetype = "glob", verbose = FALSE)
## Document-feature matrix of: 7 documents, 2 features.
## 7 x 2 sparse Matrix of class "dfmSparse"
## features
## docs polariz outlaw
## text1 1 0
## text3 1 0
## text2 1 0
## text4 1 0
## text5 0 1
## text6 0 1
## text7 0 1