Question

我试图编写构建表格的代码，该表格显示语料库中所有单词之间的所有相关性。

我知道我可以在findAssocs包中使用tm来查找单个词的所有单词相关性，即findAssocs(dtm, "quick", 0.5) - 会给我所有与之相关的单词字＃＆＃34;快速＆＃34;高于0.5，但我不想手动为我的文本中的每个单词做这个。

#Loading a .csv file into R
file_loc <- "C:/temp/TESTER.csv"
x <- read.csv(file_loc, header=FALSE)
require (tm)
corp <- Corpus(DataframeSource(x))
dtm <- DocumentTermMatrix(corp)

#Clean up the text
corp <- tm_map(corp, content_transformer(tolower))
corp <- tm_map(corp, removeNumbers)
corp <- tm_map(corp, removePunctuation)
corp <- tm_map(corp, content_transformer(stripWhitespace))
dtm <- DocumentTermMatrix(corp)

从这里我可以找到单个词的相关词：

findAssocs(dtm, "quick", 0.4)

但我希望找到所有这样的相关性：

       quick  easy   the   and 
quick   1.00  0.54  0.72  0.92     
 easy   0.54  1.00  0.98  0.54   
  the   0.72  0.98  1.00  0.05  
  and   0.92  0.54  0.05  1.00

有什么建议吗？

＆＃34; TESTER.csv＆＃34;数据文件（从单元格A1开始）

[1] I got my question answered very quickly
[2] It was quick and easy to find the information I needed
[3] My question was answered quickly by the people at stack overflow
[4] Because they're good at what they do
[5] They got it dealt with quickly and didn't mess around
[6] The information I needed was there all along
[7] They resolved it quite quickly

Answer 1

您可以使用as.matrix和cor。 findAssocs的下限为0：

(cor_1 <- findAssocs(dtm, colnames(dtm)[1:2], 0))
#               all along
#  there       1.00  1.00
#  information 0.65  0.65
#  needed      0.65  0.65
#  the         0.47  0.47
#  was         0.47  0.47

cor可以获得所有与皮尔森相关的信息，以及它的价值：

cor_2 <- cor(as.matrix(dtm))
cor_2[c("there", "information", "needed", "the", "was"), c("all", "along")]
#                   all     along
# there       1.0000000 1.0000000
# information 0.6454972 0.6454972
# needed      0.6454972 0.6454972
# the         0.4714045 0.4714045
# was         0.4714045 0.4714045

上述代码：

x <- readLines(n = 7)
[1] I got my question answered very quickly
[2] It was quick and easy to find the information I needed
[3] My question was answered quickly by the people at stack overflow
[4] Because they're good at what they do
[5] They got it dealt with quickly and didn't mess around
[6] The information I needed was there all along
[7] They resolved it quite quickly
library(tm)
corp <- Corpus(VectorSource(x))
dtm <- DocumentTermMatrix(corp)
corp <- tm_map(corp, content_transformer(tolower))
corp <- tm_map(corp, removeNumbers)
corp <- tm_map(corp, removePunctuation)
corp <- tm_map(corp, content_transformer(stripWhitespace))
dtm <- DocumentTermMatrix(corp)

使用findAssocs构建R中所有单词组合的相关矩阵

1 个答案: