制作词云，但使用组合词? [英] Making a wordcloud, but with combined words?

问题描述

我正在尝试制作出版物关键字的词云.例如:教育数据挖掘；协作学习；计算机科学...等

I am trying to make a word cloud of publications keywords. for example: Educational data mining; collaborative learning; computer science...etc

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，使用此代码，它会自行拆分每个单词，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 1 个短语，而不是正在发生的事情:教育"数据"挖掘".有没有办法将每个复合词保持在一起?分号可能有助于作为分隔符.

However, With this code, it is splitting each word by itself, But what I need is to have a combined words/phrases. For instance: Educational Data Mining is 1 phrase that I need to show instead of what is happening: "Educational" "Data" "Mining". Is there a way to keep each compound of words together? the semi-colon might help as a separator.

谢谢.

推荐答案

这是一个使用不同文本包的解决方案，它允许您根据统计检测到的搭配或仅通过形成所有二元组来形成多词表达式.该软件包名为quanteda.

Here's a solution using a different text package, that allows you to form multi-word expressions from either statistically detected collocations, or just by forming all bi-grams. The package is called quanteda.

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

首先，检测前1500个bigram搭配的方法，并将文本中的这些搭配替换为它们的单标记版本(由_"字符连接).这里我使用的是该软件包内置的美国总统就职演说文本语料库.

First, the method for detecting the top 1,500 bigram collocations, and replacing these collocations in the texts with their single-token versions (concatenated by the "_" character). Here I am using the package's built-in corpus of the US presidential inaugural address texts.

### 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()

这导致了以下情节.注意搭配，例如generation's_task"和fellow_americans".

This results in the following plot. Note the collocations, such as "generation's_task" and "fellow_americans".

由所有二元组形成的版本更容易，但会产生大量的低频二元组特征.对于词云，我选择了一组更大的文本，而不仅仅是 2013 年的奥巴马讲话.

The version formed with all bigrams is easier, but results in a huge number of low frequency bigram features. For the word cloud, I selected a larger set of texts, not just the 2013 Obama address.

### 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:

这篇关于制作词云，但使用组合词?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！