使用短语构建语料库

我的文件如下：

 doc1 = very good, very bad, you are great
 doc2 = very bad, good restaurent, nice place to visit

我想让我的语料库与,这样我的最后DocumentTermMatrix变成：

      terms
 docs       very good      very bad        you are great   good restaurent   nice place to visit
  doc1       tf-idf          tf-idf         tf-idf          0                    0
  doc2       0                tf-idf         0                tf-idf             tf-idf

我知道了，怎么计算DocumentTermMatrix单个单词但不知道如何制作语料库separated for each phrase在 R 中的一个解决方案R是首选，但解决方案是Python也受到欢迎。

我尝试过的是：

> library(tm)
> library(RWeka)
> BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 1, max = 3))
> options(mc.cores=1)
> texts <- c("very good, very bad, you are great","very bad, good restaurent, nice place to visit")
> corpus <- Corpus(VectorSource(texts))
> a <- TermDocumentMatrix(corpus, control = list(tokenize = BigramTokenizer))
> as.matrix(a)

我正进入（状态：

                         Docs
  Terms                   1 2
  bad good restaurent   0 1
  bad you are           1 0
  good restaurent nice  0 1
  good very bad         1 0
  nice place to         0 1
  place to visit        0 1
  restaurent nice place 0 1
  very bad good         0 1
  very bad you          1 0
  very good very        1 0
  you are great         1 0

我想要的不是单词的组合，而是我在矩阵中显示的短语。

这是一种使用的方法qdap + tm套餐：

library(qdap); library(tm); library(qdapTools)

dat <- list2df(list(doc1 = "very good, very bad, you are great",
 doc2 = "very bad, good restaurent, nice place to visit"), "text", "docs")

x <- sub_holder(", ", dat$text)

m <- dtm(wfm(x$unhold(gsub(" ", "~~", x$output)), dat$docs) )
weightTfIdf(m)

inspect(weightTfIdf(m))

## A document-term matrix (2 documents, 5 terms)
## 
## Non-/sparse entries: 4/6
## Sparsity           : 60%
## Maximal term length: 19 
## Weighting          : term frequency - inverse document frequency (normalized) (tf-idf)
## 
##       Terms
## Docs   good restaurent nice place to visit very bad very good you are great
##   doc1       0.0000000           0.0000000        0 0.3333333     0.3333333
##   doc2       0.3333333           0.3333333        0 0.0000000     0.0000000

你也可以一举返回DocumentTermMatrix但这可能更难理解：

x <- sub_holder(", ", dat$text)

apply_as_tm(t(wfm(x$unhold(gsub(" ", "~~", x$output)), dat$docs)), 
    weightTfIdf, to.qdap=FALSE)