《釜山行》是一部丧尸灾难片，其人物少、关系简单，非常适合我们学习文本处理。这个项目将介绍共现在关系中的提取，使用R编写代码实现对《釜山行》文本的人物关系进行分析。
library(rvest)
library(jiebaR)
library(stringr)
library(readr)
library(tidyverse)
library(network)
library(sna)
library(ggnetwork)
library(igraph)
抓取《釜山行》剧本
url='http://labfile.oss.aliyuncs.com/courses/677/busan.txt'
text=read_html(url)%>%html_text()
#对剧本进行分段
spltext=str_split(text,'\r\n\r\n \r\n\r\n')
names(spltext) = 'spltextname'
subtext=spltext$spltextname
人物关系挖掘
思路：若两个人物出现在同一段则对其权重加1，每段中对应关系出现多少次加多少次
n=length(spltext$spltextname)
data=vector('list',n)
cutter = worker('mix')
for(i in 1:n) data[[i]] = cutter[subtext[i]][which(cutter[subtext[i]] %in% iden)]
#which(cutter[subtext1] %in% iden)
weidata=data.frame(t(combn(iden,2)))
names(weidata)=c('name1','name2')
weiname=rep(0,136)
for(i in 1:136) weiname[i]=paste(weidata$name1[i],weidata$name2[i],sep = '--')
weidata$weiname=weiname
weidata$weight = rep(0,136)
for(i in 1:72){
if(length(data[[i]]) != 0){
test=as.data.frame(table(paste(expand.grid(data[[i]],data[[i]])$Var1,
expand.grid(data[[i]],data[[i]])$Var2,sep = '--')))
test$Var1=as.vector(test$Var1)
test$Freq=test$Freq/max(test$Freq)
id1=which(test$Var1 %in% weidata$weiname)
id2=which( weidata$weiname %in% test$Var1)
weidata$weight[id2]=weidata$weight[id2]+test$Freq[id1]
}
}
结果
weight=0说明两人没有过交际，weight越大关系越密切
taltext=as.data.frame(table(cutter[subtext][which(cutter[subtext] %in% iden)]))
taltext$Var1=as.vector(taltext$Var1)
ind3=rep(0,17)
for(i in 1:17) ind3[i] = taltext$Freq[which(taltext$Var1[i] == iden)]
weidata1 = weidata[which(weidata$weight !=0),]
g1=graph.data.frame(weidata1[,-3], directed = F)
op=par(mar=c(0,0,0,0))
plot(g1, edge.width = E(g1)$weight,
vertex.size=rank(ind3),
layout=layout.fruchterman.reingold,
vertex.color= ind3,
vertex.label.cex=0.75,
vertex.label.color='blue'
)
par(op)
n=fortify(g1)
ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(linetype = 2, color = "grey50",curvature = 0.1) +
geom_nodes(aes(color = vertex.names, size = weight)) +
geom_nodelabel_repel(aes(color = vertex.names, label = vertex.names),
fontface = "bold", box.padding = unit(1, "lines")) +
theme(legend.position='none',
axis.text = element_blank(),
axis.title = element_blank(),
panel.background = element_rect(fill = "grey25"),
panel.grid = element_blank()
)
require(RColorBrewer)
library(wordcloud)
#这里的停词是把底层的stop_words.utf-8改为txt格式，改之前先备份
wk stop_word="D:/Program Files/R/R-3.3.2/library/jiebaRD/dict/stop_words.txt")
textda=wk[subtext]
datext=as.data.frame(table(textda))
datext$textda=as.vector(datext$textda)
textdata=arrange(datext,desc(Freq))
textdata1=textdata[which(textdata$Freq>5),]
pal2 wordcloud(textdata1$textda,textdata1$Freq,colors=pal2,random.order=FALSE,ot.per=.45)
library(wordcloud2)
wordcloud2(textdata,color="random-light",backgroundColor = 'black')
End.
作者：周世荣
来源：数据玩家
