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我想創建一個基於R中某些word associations一個term network analysis plot,但我不知道如何超越繪製整個期限文檔矩陣:在R中使用tdm或dtm中的igraph在R中繪製關鍵字/單詞關聯(findAssocs)?
# Network analysis
library(igraph)
# load tdm data
# create matrix
Neg.m <- as.matrix(Ntdm_nonsparse)
# to boolean matrix
Neg.m[Neg.m>=1] <- 1
# to term adjacency matrix
# %*% is product of 2 matrices
Neg.m2 <- Neg.m %*% t(Neg.m)
Neg.m2[5:10,5:10]
# build graph with igraph ####
library(igraph)
# build adjacency graph
Neg.g <- graph.adjacency(Neg.m2, weighted=TRUE, mode="undirected")
# remove loops
Neg.g <- simplify(Neg.g)
# set labels and degrees of vertices
V(Neg.g)$label <- V(Neg.g)$name
V(Neg.g)$degree <- degree(Neg.g)
# plot layout fruchterman.reingold
layout1 <- layout.fruchterman.reingold(Neg.g)
plot(Neg.g, layout=layout1, vertex.size=20,
vertex.label.color="darkred")
反正是有應用聯想詞network analysis plot到(以及作爲一般詞關聯bar plot)以下findAssocs
數據例如?:
findAssocs(Ntdm, "verizon", .06)
$verizon
att switched switch wireless basket 09mbps 16mbps
0.16 0.13 0.11 0.11 0.10 0.09 0.09
32mbps 4gbs 5gbs cheaper ima landry nudge
0.09 0.09 0.09 0.09 0.09 0.09 0.09
sears wink collapsed expensive sprint -fine -law
0.09 0.09 0.08 0.08 0.08 0.07 0.07
11yrs 380 980 alltel callled candle cdma
0.07 0.07 0.07 0.07 0.07 0.07 0.07
concert consequence de-evolving dimas doria fluke left
0.07 0.07 0.07 0.07 0.07 0.07 0.07
london lulz lyingly niet outfits pocketbook puny
0.07 0.07 0.07 0.07 0.07 0.07 0.07
recentely redraw reinvesting reservoir satellite's shrimp stratosphere
0.07 0.07 0.07 0.07 0.07 0.07 0.07
strighten switchig switching undergo wheelchair wireless-never worth
0.07 0.07 0.07 0.07 0.07 0.07 0.07
yeap 1994 299 cheapest com' comin crushes
0.07 0.06 0.06 0.06 0.06 0.06 0.06
hhahahahahah mache metro metro-nyc must've rising sabotage
0.06 0.06 0.06 0.06 0.06 0.06 0.06
wholeheartedly
0.06
換句話說,我想以顯現特定關鍵字與R中的其他關鍵字的連接,但我不不知道如何。