使用findAssocs建立R中所有单词组合的相关矩阵

Question

I'm trying to write code that builds a table that shows all the correlations between all the words from a corpus.

I know that I can use findAssocs in the tm package to find all word correlations for a single word ie findAssocs(dtm, "quick", 0.5) - would give me all the words that have a correlation with the word "quick" above 0.5, but I do not want to do this manually for each word in the text I have.

#Loading a .csv file into R
file_loc <- "C:/temp/TESTER.csv"
x <- read.csv(file_loc, header=FALSE)
require (tm)
corp <- Corpus(DataframeSource(x))
dtm <- DocumentTermMatrix(corp)

#Clean up the text
corp <- tm_map(corp, content_transformer(tolower))
corp <- tm_map(corp, removeNumbers)
corp <- tm_map(corp, removePunctuation)
corp <- tm_map(corp, content_transformer(stripWhitespace))
dtm <- DocumentTermMatrix(corp)

From here I can find the word correlations for individual words:

findAssocs(dtm, "quick", 0.4)

But I want to find all the correlations like this:

       quick  easy   the   and 
quick   1.00  0.54  0.72  0.92     
 easy   0.54  1.00  0.98  0.54   
  the   0.72  0.98  1.00  0.05  
  and   0.92  0.54  0.05  1.00

Any suggestions?

Example of the "TESTER.csv" data file (starting from cell A1)

[1] I got my question answered very quickly
[2] It was quick and easy to find the information I needed
[3] My question was answered quickly by the people at stack overflow
[4] Because they're good at what they do
[5] They got it dealt with quickly and didn't mess around
[6] The information I needed was there all along
[7] They resolved it quite quickly

Answer 1

You can probably use as.matrix and cor . findAssocs has a lower limit of 0:

(cor_1 <- findAssocs(dtm, colnames(dtm)[1:2], 0))
#               all along
#  there       1.00  1.00
#  information 0.65  0.65
#  needed      0.65  0.65
#  the         0.47  0.47
#  was         0.47  0.47

cor gets you all pearson correlations, for what it's worth:

cor_2 <- cor(as.matrix(dtm))
cor_2[c("there", "information", "needed", "the", "was"), c("all", "along")]
#                   all     along
# there       1.0000000 1.0000000
# information 0.6454972 0.6454972
# needed      0.6454972 0.6454972
# the         0.4714045 0.4714045
# was         0.4714045 0.4714045

The preceding code:

x <- readLines(n = 7)
[1] I got my question answered very quickly
[2] It was quick and easy to find the information I needed
[3] My question was answered quickly by the people at stack overflow
[4] Because they're good at what they do
[5] They got it dealt with quickly and didn't mess around
[6] The information I needed was there all along
[7] They resolved it quite quickly
library(tm)
corp <- Corpus(VectorSource(x))
dtm <- DocumentTermMatrix(corp)
corp <- tm_map(corp, content_transformer(tolower))
corp <- tm_map(corp, removeNumbers)
corp <- tm_map(corp, removePunctuation)
corp <- tm_map(corp, content_transformer(stripWhitespace))
dtm <- DocumentTermMatrix(corp)