build word co-occurence edge list in R

问题

I have a chunk of sentences and I want to build the undirected edge list of word co-occurrence and see the frequency of every edge. I took a look at the tm package but didn't find similar functions. Is there some package/script I can use? Thanks a lot!

Note: A word doesn't co-occur with itself. A word which appears twice or more co-occurs with other words for only once in the same sentence.

DF:

sentence_id text
1           a b c d e
2           a b b e
3           b c d
4           a e
5           a
6           a a a

OUTPUT

word1 word2 freq
a     b     2
a     c     1
a     d     1
a     e     3
b     c     2
b     d     2
b     e     2
c     d     2
c     e     1
d     e     1

回答1:

It's convoluted so there's got to be a better approach:

dat <- read.csv(text="sentence_id, text
1,           a b c d e
2,           a b b e
3,           b c d
4,           a e", header=TRUE)


library(qdapTools); library(tidyr)
x <- t(mtabulate(with(dat, by(text, sentence_id, bag_o_words))) > 0)
out <- x %*% t(x)
out[upper.tri(out, diag=TRUE)] <- NA

out2 <- matrix2df(out, "word1") %>%
    gather(word2, freq, -word1) %>%
    na.omit() 

rownames(out2) <- NULL
out2

##    word1 word2 freq
## 1      b     a    2
## 2      c     a    1
## 3      d     a    1
## 4      e     a    3
## 5      c     b    2
## 6      d     b    2
## 7      e     b    2
## 8      d     c    2
## 9      e     c    1
## 10     e     d    1

Base only solution

out <- lapply(with(dat, split(text, sentence_id)), function(x) {
    strsplit(gsub("^\\s+|\\s+$", "", as.character(x)), "\\s+")[[1]]
})

nms <- sort(unique(unlist(out)))

out2 <- lapply(out, function(x) {
    as.data.frame(table(x), stringsAsFactors = FALSE)
})

dat2 <- data.frame(x = nms)

for(i in seq_along(out2)) {
    m <- merge(dat2, out2[[i]], all.x = TRUE)
    names(m)[i + 1] <- dat[["sentence_id"]][i]
    dat2 <- m
}

dat2[is.na(dat2)] <- 0
x <- as.matrix(dat2[, -1]) > 0

out3 <- x %*% t(x)
out3[upper.tri(out3, diag=TRUE)] <- NA
dimnames(out3) <- list(dat2[[1]], dat2[[1]])

out4 <- na.omit(data.frame( 
        word1 = rep(rownames(out3), ncol(out3)),  
        word2 = rep(colnames(out3), each = nrow(out3)),
        freq = c(unlist(out3)),
        stringsAsFactors = FALSE)
)

row.names(out4) <- NULL

out4

回答2:

This is very closely related to @TylerRinker's answer, but using different tools.

library(splitstackshape)
library(reshape2)

temp <- crossprod(
  as.matrix(
    cSplit_e(d, "text", " ", type = "character", 
             fill = 0, drop = TRUE)[-1]))
temp[upper.tri(temp, diag = TRUE)] <- NA
melt(temp, na.rm = TRUE)
#      Var1   Var2 value
# 2  text_b text_a     2
# 3  text_c text_a     1
# 4  text_d text_a     1
# 5  text_e text_a     3
# 8  text_c text_b     2
# 9  text_d text_b     2
# 10 text_e text_b     2
# 14 text_d text_c     2
# 15 text_e text_c     1
# 20 text_e text_d     1

The "text_" parts of "Var1" and "Var2" can be stripped easily with sub or gsub.

回答3:

Here's a base R way:

d <- read.table(text='sentence_id text
1           "a b c d e"
2           "a b b e"
3           "b c d"
4           "a e"', header=TRUE, as.is=TRUE)

result.vec <- table(unlist(lapply(d$text, function(text) {
    pairs <- combn(unique(scan(text=text, what='', sep=' ')), m=2)
    interaction(pairs[1,], pairs[2,])
})))
# a.b b.b c.b d.b a.c b.c c.c d.c a.d b.d c.d d.d a.e b.e c.e d.e 
#   2   0   0   0   1   2   0   0   1   2   2   0   3   2   1   1 

result <- subset(data.frame(do.call(rbind, strsplit(names(result.vec), '\\.')), freq=as.vector(result.vec)), freq > 0)
with(result, result[order(X1, X2),])

#    X1 X2 freq
# 1   a  b    2
# 5   a  c    1
# 9   a  d    1
# 13  a  e    3
# 6   b  c    2
# 10  b  d    2
# 14  b  e    2
# 11  c  d    2
# 15  c  e    1
# 16  d  e    1

来源：https://stackoverflow.com/questions/27153320/build-word-co-occurence-edge-list-in-r

标签

text-mining

network-analysis