制作一个wordcloud，但结合了单词？

Question

我正在尝试制作出版物关键词云。例如： 教育数据挖掘;协作学习;计算机科学等等

我目前的代码如下：

KeywordsCorpus <- Corpus(VectorSource(subset(Words$Author.Keywords, Words$Year==2012)))
KeywordsCorpus <- tm_map(KeywordsCorpus, removePunctuation)
KeywordsCorpus <- tm_map(KeywordsCorpus, removeNumbers)

# added tolower
KeywordsCorpus <- tm_map(KeywordsCorpus, tolower)
KeywordsCorpus <- tm_map(KeywordsCorpus, removeWords, stopwords("english"))

# moved stripWhitespace
KeywordsCorpus <- tm_map(KeywordsCorpus, stripWhitespace)  

KeywordsCorpus <- tm_map(KeywordsCorpus, PlainTextDocument)

dtm4 <- TermDocumentMatrix(KeywordsCorpus)
m4 <- as.matrix(dtm4)
v4 <- sort(rowSums(m4),decreasing=TRUE)
d4 <- data.frame(word = names(v4),freq=v4)

但是，使用这段代码，它会将每个单词分开，但我需要的是组合单词/短语。例如：教育数据挖掘是我需要展示的一个短语，而不是正在发生的事情：＆＃34;教育＆＃34; ＆＃34;数据＆＃34; ＆＃34;挖掘＆＃34 ;.有没有办法将每个单词组合在一起？分号可能有助于作为分隔符。

感谢。

Answer 1

这是一个使用不同文本包的解决方案，它允许您从统计检测到的搭配中形成多词表达式，或者仅通过形成所有二元组。该软件包名为 quanteda 。

library(quanteda)
packageVersion("quanteda")
## [1] ‘0.9.5.14’

首先，检测前1,500个bigram搭配的方法，并用单令牌版本（由"_"字符连接）替换文本中的这些搭配。在这里，我使用的是美国总统就职演说文本中包的内置语料库。

### for just the top 1500 collocations
# detect the collocations
colls <- collocations(inaugCorpus, n = 1500, size = 2)

# remove collocations containing stopwords
colls <- removeFeatures(colls, stopwords("SMART"))
## Removed 1,224 (81.6%) of 1,500 collocations containing one of 570 stopwords.

# replace the phrases with single-token versions
inaugCorpusColl2 <- phrasetotoken(inaugCorpus, colls)

# create the document-feature matrix
inaugColl2dfm <- dfm(inaugCorpusColl2, ignoredFeatures = stopwords("SMART"))
## Creating a dfm from a corpus ...
## ... lowercasing
## ... tokenizing
## ... indexing documents: 57 documents
## ... indexing features: 9,741 feature types
## ... removed 430 features, from 570 supplied (glob) feature types
## ... complete. 
## ... created a 57 x 9311 sparse dfm
## Elapsed time: 0.163 seconds.

# plot the wordcloud
set.seed(1000)
png("~/Desktop/wcloud1.png", width = 800, height = 800)
plot(inaugColl2dfm["2013-Obama", ], min.freq = 2, random.order = FALSE, 
     colors = sample(colors()[2:128]))
dev.off()

这导致以下图表。请注意搭配，例如“generation's_task”和“fellow_americans”。

与所有双字母组合形成的版本更容易，但会产生大量的低频双字母功能。对于词云，我选择了更多的文本，而不仅仅是2013年的奥巴马地址。

### version with all bi-grams
inaugbigramsDfm <- dfm(inaugCorpusColl2, ngrams = 2, ignoredFeatures = stopwords("SMART"))
## Creating a dfm from a corpus ...
## ... lowercasing
## ... tokenizing
## ... indexing documents: 57 documents
## ... removed 54,200 features, from 570 supplied (glob) feature types
## ... indexing features: 64,108 feature types
## ... created a 57 x 9908 sparse dfm
## ... complete. 
## Elapsed time: 3.254 seconds.

# plot the bigram wordcloud - more texts because for a single speech, 
# almost none occur more than once
png("~/Desktop/wcloud2.png", width = 800, height = 800)
plot(inaugbigramsDfm[40:57, ], min.freq = 2, random.order = FALSE, 
     colors = sample(colors()[2:128]))
dev.off()

这会产生：

Answer 2

最好的建议是按照短短的五分钟视频（链接如下）：

https://youtu.be/HellsQ2JF2k

如果您想直接使用R代码，请输入：

mycorpus <- Corpus(VectorSource(subset(Words$Author.Keywords,Words$Year==2012)))

文字清理 将文本转换为小写

mycorpus <- tm_map(mycorpus, content_transformer(tolower))

删除号码

mycorpus <- tm_map(mycorpus, removeNumbers)

删除英语常用术语

mycorpus <- tm_map(mycorpus, removeWords, stopwords("english"))

删除标点符号

mycorpus <- tm_map(mycorpus, removePunctuation)

消除额外的空白

mycorpus <- tm_map(mycorpus, stripWhitespace)
as.character(mycorpus[[1]])

Bigrams

minfreq_bigram<-2
token_delim <- " \\t\\r\\n.!?,;\"()"
bitoken <- NGramTokenizer(mycorpus, Weka_control(min=2,max=2, delimiters = token_delim))
two_word <- data.frame(table(bitoken))
sort_two <- two_word[order(two_word$Freq,decreasing=TRUE),]
wordcloud(sort_two$bitoken,sort_two$Freq,random.order=FALSE,scale = c(2,0.35),min.freq = minfreq_bigram,colors = brewer.pal(8,"Dark2"),max.words=150)