如何在R中的DTM中找到词频? [英] How to find term frequency within a DTM in R?

问题描述

我一直在使用 tm 包来创建一个 DocumentTerm 矩阵，如下所示:

I've been using the tm package to create a DocumentTerm Matrix as follows:

library(tm)
library(RWeka)
library(SnowballC)
src <- DataframeSource(data.frame(data3$JobTitle))

# create a corpus and transform data
# Sets the default number of threads to use
options(mc.cores=1)
c_copy <- c <- Corpus(src)
c <- tm_map(c, content_transformer(tolower), mc.cores=1)
c <- tm_map(c,content_transformer(removeNumbers), mc.cores=1)
c <- tm_map(c,removeWords, stopwords("english"), mc.cores=1)
c <- tm_map(c,content_transformer(stripWhitespace), mc.cores=1)

#make DTM
dtm <- DocumentTermMatrix(c, control = list(tokenize = BigramTokenizer))

现在，DTM 运行良好 - 我想要做的是获取 DTM 中频繁项的频率.显然，我可以使用 findFreqTerms 来获取术语本身，而不是实际频率.termFreq 仅适用于 TextDocument，不适用于 DTM 或 TDM - 有什么想法吗?

Now, the DTM comes out fine - what I want to do is get the frequencies of the frequent terms within the DTM. Obviously, I can use findFreqTerms to get the terms themselves, but not the actual frequencies. termFreq only works on TextDocument, not a DTM or TDM - any ideas?

来自 str 的输出 - 频繁项在 $ 条款中:

Output from str - the frequent terms are in $ Terms:

> str(dtm)
List of 6
 $ i       : int [1:190] 1 2 3 4 5 6 7 8 9 10 ...
 $ j       : int [1:190] 1 2 3 4 5 6 7 8 9 10 ...
 $ v       : num [1:190] 1 1 1 1 1 1 1 1 1 1 ...
 $ nrow    : int 119
 $ ncol    : int 146
 $ dimnames:List of 2
  ..$ Docs : chr [1:119] "1" "2" "3" "4" ...
  ..$ Terms: chr [1:146] "account administrator" "account assistant" "account director" "account executive" ...
 - attr(*, "class")= chr [1:2] "DocumentTermMatrix" "simple_triplet_matrix"
 - attr(*, "weighting")= chr [1:2] "term frequency" "tf"

推荐答案

感谢 NicE 的建议 - 它运作良好.添加加权参数可以让我在检查 DTM 时得到词频.简单的事情就是对每列求和.

Thanks to NicE for the advice - it works well. Adding in the weighting argument allows me to get out the term frequencies when I inspect the DTM. Simple matter then of summing up per column.

dtm <- DocumentTermMatrix(c, control = list(tokenize = BigramTokenizer, weighting=weightTf))
freqs <- as.data.frame(inspect(dtm))
colSums(freqs)

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