Removing stopwords from R data frame column

Question

Here's the situation, one whose solution seemed to be simple at first, but that has turned out to be more complicated than I expected.

I have an R data frame with three columns: an ID, a column with texts (reviews), and one with numeric values which I want to predict based on the text.

I have already done some preprocessing on the text column, so it is free of punctuation, in lower case, and ready to be tokenized and turned into a matrix so I can train a model on it. The problem is I can't figure out how to remove the stop words from that text.

Here's what I am trying to do with the text2vec package. I was planning on doing the stop-word removal before this chunk at first. But anywhere will do.

library(text2vec)

test_data <- data.frame(review_id=c(1,2,3),
                        review=c('is a masterpiece a work of art',
                        'sporting some of the best writing and voice work',
                        'better in every possible way when compared'),
                         score=c(90, 100, 100))

tokens <- word_tokenizer(test_data$review)
document_term_matrix <- create_dtm(itoken(tokens), hash_vectorizer())
model_tfidf <- TfIdf$new()
document_term_matrix <- model_tfidf$fit_transform(document_term_matrix)

document_term_matrix <- as.matrix(document_term_matrix)

I am hoping to get the review column to be something like:

review=c('masterpiec work art',
         'sporting best writing voice work',
         'better possible way compared')

Answer 1

You can use tidytext package for this:

library(tidytext)
library(dplyr)

test_data %>%
  unnest_tokens(review, review) %>%
  anti_join(stop_words, by= c("review" = "word"))

#    review_id      review score
#1.2         1 masterpiece    90
#1.6         1         art    90
#2           2    sporting   100
#2.5         2     writing   100
#2.7         2       voice   100
#3.6         3    compared   100

To get the words back in one row you could do:

test_data %>%
  unnest_tokens(review, review) %>%
  anti_join(stop_words, by= c("review" = "word")) %>%
  group_by(review_id, score) %>%
  summarise(review = paste0(review, collapse = ' '))

#  review_id score review                
#      <dbl> <dbl> <chr>                 
#1         1    90 masterpiece art       
#2         2   100 sporting writing voice
#3         3   100 compared              

Answer 2

It turns out that I ended up solving my own problem.

I created the following function:

remove_words_from_text <- function(text) {
  text <- unlist(strsplit(text, " "))
  paste(text[!text %in% words_to_remove], collapse = " ")
}

And called it via lapply.

words_to_remove <- stop_words$word
test_data$review <- lapply(test_data$review, remove_words_from_text)

Here's hoping that helps those who have the same problem that I did.