Question

您好我有一个文档术语矩阵，我使用tidy()函数对其进行了转换，它完美无缺。我想根据单词的频率绘制一个词云。所以我的转换表看起来像这样：

> head(Wcloud.Data)
# A tibble: 6 x 3
  document term       count
  <chr>    <chr>      <dbl>
1 1        accept         1
2 1        access         1
3 1        accomplish     1
4 1        account        4
5 1        accur          2
6 1        achiev         1

我有33,647,383次观测，因此它是一个非常大的数据帧。如果我使用max()函数，我得到一个非常高的数字（64116），但我的数据框中没有单词的频率为64116.此外，如果我用wordcloud()绘制闪亮的数据帧，它会绘制相同的单词几次。此外，如果我想对我的专栏count进行排序，则该专栏无效 - sort(Wcloud.Data$count,decreasing = TRUE)。所以有些事情是不正确的，但我不知道，有什么以及如何解决它。有人有什么想法吗？

我的文档术语矩阵的摘要，然后将其转换为数据帧：

> observations.tf
<<DocumentTermMatrix (documents: 76717, terms: 4234)>>
Non-/sparse entries: 33647383/291172395
Sparsity           : 90%
Maximal term length: 15
Weighting          : term frequency (tf)

更新：我添加了数据框的图片

Answer 1

使用dplyr，您可以执行以下操作：

library("tm")
library("SnowballC")
library("wordcloud")
library("RColorBrewer")

Wcloud.Data<- data.frame(Document= c(rep(1,6)), 
                         term = c("accept", "access","accomplish", "account", "accur", "achiev"),
                         count = c(1,1,1,4,2,1))

Data<-Wcloud.Data %>% 
  group_by(term) %>% 
  summarise(Frequency = sum(count))
set.seed(1234)
wordcloud(words = Data$term, freq = Data$Frequency, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

另一方面，图书馆quanteda和tibble可以帮助您创建术语频率矩阵。我将举例说明如何使用它：

library(tibble)
library(quanteda)
Data <- data_frame(text = c("Chinese Beijing Chinese",
                              "Chinese Chinese Shanghai",
                              "this is china",
                              "china is here",
                              'hello china',
                              "Chinese Beijing Chinese",
                              "Chinese Chinese Shanghai",
                              "this is china",
                              "china is here",
                              'hello china',
                              "Kyoto Japan",
                              "Tokyo Japan Chinese",
                              "Kyoto Japan",
                              "Tokyo Japan Chinese",
                              "Kyoto Japan",
                              "Tokyo Japan Chinese",
                              "Kyoto Japan",
                              "Tokyo Japan Chinese",
                              'japan'))
DocTerm <- quanteda::dfm(Data$text)
DocTerm
# Document-feature matrix of: 19 documents, 11 features (78.5% sparse).
# 19 x 11 sparse Matrix of class "dfm"
# features
# docs     chinese beijing shanghai this is china here hello kyoto japan tokyo
# text1        2       1        0    0  0     0    0     0     0     0     0
# text2        2       0        1    0  0     0    0     0     0     0     0
# text3        0       0        0    1  1     1    0     0     0     0     0
# text4        0       0        0    0  1     1    1     0     0     0     0
# text5        0       0        0    0  0     1    0     1     0     0     0
# text6        2       1        0    0  0     0    0     0     0     0     0
# text7        2       0        1    0  0     0    0     0     0     0     0
# text8        0       0        0    1  1     1    0     0     0     0     0
# text9        0       0        0    0  1     1    1     0     0     0     0
# text10       0       0        0    0  0     1    0     1     0     0     0
# text11       0       0        0    0  0     0    0     0     1     1     0
# text12       1       0        0    0  0     0    0     0     0     1     1
# text13       0       0        0    0  0     0    0     0     1     1     0
# text14       1       0        0    0  0     0    0     0     0     1     1
# text15       0       0        0    0  0     0    0     0     1     1     0
# text16       1       0        0    0  0     0    0     0     0     1     1
# text17       0       0        0    0  0     0    0     0     1     1     0
# text18       1       0        0    0  0     0    0     0     0     1     1
# text19       0       0        0    0  0     0    0     0     0     1     0

Mat<-quanteda::convert(DocTerm,"data.frame")[,2:ncol(DocTerm)] # Converting to a Dataframe without taking into account the text variable
Result<- colSums(Mat) # This is what you are interested in
names(Result)<-colnames(Mat)
# > Result
# chinese  beijing shanghai     this       is    china     here    hello    kyoto    japan 
# 24        4        4        4        8       12        4        4        8       18

如何在R中转换文档术语矩阵？

1 个答案: