R（igraph）中的子图文本分析

我很好奇如何访问与边关联的图的附加属性。下面是一个简单的例子：

library("igraph")
library("SocialMediaLab")

myapikey =''
myapisecret =''
myaccesstoken = ''
myaccesstokensecret = ''

tweets <- Authenticate("twitter",
                       apiKey = myapikey,
                       apiSecret = myapisecret,
                       accessToken = myaccesstoken,
                       accessTokenSecret = myaccesstokensecret) %>%
Collect(searchTerm="#trump", numTweets = 100,writeToFile=FALSE,verbose=TRUE)
g_twitter_actor <- tweets %>% Create("Actor", writeToFile=FALSE)
c <- igraph::components(g_twitter_actor, mode = 'weak')
subCluster <- induced.subgraph(g_twitter_actor, V(g_twitter_actor)[which(c$membership == which.max(c$csize))])

如何访问子图的text属性以执行文本分析？

E（子集群）$text

不起作用

E（子集群）$text

不起作用，因为创建时，

tweets$text

的值未添加到图形中。因此，您必须手动执行此操作。这有点痛苦，但可行。需要对

tweets

数据帧进行一些子集设置，并基于用户名进行匹配

首先，请注意边缘类型是按特定顺序排列的：转发、提及、回复。来自特定用户的相同文本可以应用于这三个方面。所以我认为连续添加文本是有意义的

> unique(E(g_twitter_actor)$edgeType)
[1] "Retweet" "Mention" "Reply"  

使用

dplry

和

restrape2

可以简化此操作

library(reshape2); library(dplyr)
#Make data frame for retweets, mentions, replies
rts <- tweets %>% filter(!is.na(retweet_from))
ms <- tweets %>% filter(users_mentioned!="character(0)")
rpls <- tweets %>% filter(!is.na(reply_to))

现在，通过匹配边缘类型，将其中的每一个作为边缘属性添加到网络中

E(g_twitter_actor)$text[E(g_twitter_actor)$edgeType %in% "Retweet"] <- rts$text
E(g_twitter_actor)$text[E(g_twitter_actor)$edgeType %in% "Mention"] <- ms$text
E(g_twitter_actor)$text[E(g_twitter_actor)$edgeType %in% "Reply"] <- rpls$text

---

E（g_twitter_actor）$text[E（g_twitter_actor）$edgeType%in%“Retweet”]非常感谢，你知道我如何解决这个问题吗？
#Name each element in the users_mentioned list after the user who mentioned
names(ms$users_mentioned) <- ms$screen_name
ms <- melt(ms$users_mentioned) #melting creates a data frame for each user and the users they mention

#Add the text
ms$text <- tweets[match(ms$L1,tweets$screen_name),1]

E(g_twitter_actor)$text[E(g_twitter_actor)$edgeType %in% "Retweet"] <- rts$text
E(g_twitter_actor)$text[E(g_twitter_actor)$edgeType %in% "Mention"] <- ms$text
E(g_twitter_actor)$text[E(g_twitter_actor)$edgeType %in% "Reply"] <- rpls$text

subCluster <- induced.subgraph(g_twitter_actor, 
                           V(g_twitter_actor)[which(c$membership == which.max(c$csize))])