I have corpus in which a key term occurs at least once. From this I made fcm that looks much like this.
txts <- c("a a a b b c", "a a c e", "a c b e f g", "e d j b", "b g k l", "b a a g l", "e c b j k l", "b g w m")
total <- fcm(txts, context = "document", count = "frequency")
Feature co-occurrence matrix of: 12 by 12 features.
12 x 12 sparse Matrix of class "fcm"
features
features a b c e f g d j k l w m
a 5 9 6 3 1 3 0 0 0 2 0 0
b 0 1 4 3 1 4 1 2 2 3 1 1
c 0 0 0 3 1 1 0 1 1 1 0 0
e 0 0 0 0 1 1 1 2 1 1 0 0
f 0 0 0 0 0 1 0 0 0 0 0 0
g 0 0 0 0 0 0 0 0 1 2 1 1
d 0 0 0 0 0 0 0 1 0 0 0 0
j 0 0 0 0 0 0 0 0 1 1 0 0
k 0 0 0 0 0 0 0 0 0 2 0 0
l 0 0 0 0 0 0 0 0 0 0 0 0
w 0 0 0 0 0 0 0 0 0 0 0 1
m 0 0 0 0 0 0 0 0 0 0 0 0
From this, I would like to find the different clusters around 'b'.
With an eye on scaling, my actual fcm has 239104369 elements and a size of 1.2GB.
A matrix of its first 10 features looks like this
Feature co-occurrence matrix of: 10 by 10 features.
10 x 10 sparse Matrix of class "fcm"
features
features international monetary fund development association bolivia assessment interim poverty reduction
international 2885797 1345055 3340282 12013377 857864 199985 605036 202117 3996710 1319199
monetary 0 227329 973979 2326677 234565 39802 93927 65773 884341 330250
fund 0 0 1766657 6530594 621315 99900 355415 204229 2534382 927737
development 0 0 0 20054398 1683896 485906 2235294 406575 13674085 4091506
association 0 0 0 0 122947 25954 87756 47038 580721 204144
bolivia 0 0 0 0 0 26062 35164 5336 254924 71428
assessment 0 0 0 0 0 0 203933 24196 1420850 377398
interim 0 0 0 0 0 0 0 20595 172870 67705
poverty 0 0 0 0 0 0 0 0 9131869 4026961
reduction 0 0 0 0 0 0 0 0 0 642944
My goal is to visualise the clusters around the key-term (https://bost.ocks.org/mike/miserables/) and to create term lists from it.
https://www.r-bloggers.com/turning-keywords-into-a-co-occurrence-network/
https://www.r-bloggers.com/collapsing-a-bipartite-co-occurrence-network/
In my search I also stumbled on the cooccurNet package, but I don't how to adept it. https://cran.r-project.org/web/packages/cooccurNet/index.html
quanteda has textstat_simil() that returns a dist object for hierarchical clustering. This function only takes a DFM but an FCM can be converted to the object using as.dfm().
require(quanteda)
txt <- c("a a a b b c", "a a c e", "a c b e f g", "e d j b", "b g k l", "b a a g l", "e c b j k l", "b g w m")
dmt <- dfm(txt)
# dmt <- dfm_trim(dmt, min_termfreq = 10) # you might need this to reduce the size of fcm
fmt <- fcm(dmt, context = "document")
dist <- textstat_simil(as.dfm(fmt), margin = "features")
tree <- hclust(dist)
cutree(tree, 2)