Consider the following example
library(quanteda)
library(tidyverse)
tibble(text = c('the dog is growing tall',
'the grass is growing as well')) %>%
corpus() %>% dfm()
Document-feature matrix of: 2 documents, 8 features (31.2% sparse).
features
docs the dog is growing tall grass as well
text1 1 1 1 1 1 0 0 0
text2 1 0 1 1 0 1 1 1
I would like to create an interaction between dog and the other tokens in each sentence. That is, creating the features the-dog, is-dog, growing-dog, tall-dog and adding them to the dfm (on top of the ones we already have).
That is, for instance, the-dog would be equal to 1 if both the and dog are present in the sentence (and zero otherwise). So the-dog would be one for the first sentence and zero for the second one.
Notice how I only create interaction terms when dog is in the sentence, so dog-grass is not required here.
How can I do that efficiently in quanteda?
library("quanteda")
## Package version: 2.1.2
toks <- tokens(c(
"the dog is growing tall",
"the grass is growing as well"
))
# now keep just tokens co-occurring with "dog"
toks_dog <- tokens_select(toks, "dog", window = 1e5)
# create the dfm and label other terms as interactions with dog
dfmat_dog <- dfm(toks_dog) %>%
dfm_remove("dog")
colnames(dfmat_dog) <- paste(featnames(dfmat_dog), "dog", sep = "-")
dfmat_dog
## Document-feature matrix of: 2 documents, 4 features (50.00% sparse) and 0 docvars.
## features
## docs the-dog is-dog growing-dog tall-dog
## text1 1 1 1 1
## text2 0 0 0 0
# combine with other features
print(cbind(dfm(toks), dfmat_dog), max_nfeat = -1)
## Document-feature matrix of: 2 documents, 12 features (37.50% sparse) and 0 docvars.
## features
## docs the dog is growing tall grass as well the-dog is-dog growing-dog
## text1 1 1 1 1 1 0 0 0 1 1 1
## text2 1 0 1 1 0 1 1 1 0 0 0
## features
## docs tall-dog
## text1 1
## text2 0
Created on 2021-03-18 by the reprex package (v1.0.0)