我想将我的表转换为 DTM 并维护元数据。
每行都应该是一个文档。但是为了使用 cast_dtm()，需要有一个计数变量。为了“投射”，它需要采用“文档、术语、计数”格式。
如何将我的数据转换为“文档、术语、计数”数据框？从那里，很容易转换成 DTM，然后做我需要的。
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2 回答
2
您也可以使用quanteda包。
要重新创建您的 data.frame：
df <- data.frame(Date = c("2015-01-01", "2015-01-01", "2015-01-03", "2015-01-01"),
Group = "Cars",
Reporting = c(rep("A", 3), "B"),
Comments = c(rep("This car is awesome", 3), "No comments"),
stringsAsFactors = FALSE)
df
# Date Group Reporting Comments
# 1 2015-01-01 Cars A This car is awesome
# 2 2015-01-01 Cars A This car is awesome
# 3 2015-01-03 Cars A This car is awesome
# 4 2015-01-01 Cars B No comments
文档术语矩阵的捷径：
dfm(df$Comments)
# Document-feature matrix of: 4 documents, 6 features (41.7% sparse).
# 4 x 6 sparse Matrix of class "dfmSparse"
# features
# docs this car is awesome no comments
# text1 1 1 1 1 0 0
# text2 1 1 1 1 0 0
# text3 1 1 1 1 0 0
# text4 0 0 0 0 1 1
通往dfm的路很长：
制作一个语料库，包括文档变量：
require(quanteda)
myCorpus <- corpus(df, text_field = "Comments")
summary(myCorpus)
# Corpus consisting of 4 documents.
#
# Text Types Tokens Sentences Date Group Reporting
# text1 4 4 1 2015-01-01 Cars A
# text2 4 4 1 2015-01-01 Cars A
# text3 4 4 1 2015-01-03 Cars A
# text4 2 2 1 2015-01-01 Cars B
#
# Source: /Users/kbenoit/Dropbox (Personal)/GitHub/quanteda/* on x86_64 by kbenoit
# Created: Wed Jun 21 23:34:35 2017
# Notes:
然后：
dfm(myCorpus)
# Document-feature matrix of: 4 documents, 6 features (41.7% sparse).
# 4 x 6 sparse Matrix of class "dfmSparse"
# features
# docs this car is awesome no comments
# text1 1 1 1 1 0 0
# text2 1 1 1 1 0 0
# text3 1 1 1 1 0 0
# text4 0 0 0 0 1 1
于 2017-06-21T21:39:33.003 回答
2
尝试这个
library(tm)
myCorpus <- Corpus(VectorSource(df))
dtm <- DocumentTermMatrix(myCorpus)
我已将上述代码用于文本挖掘项目，但我将 df 替换为 df$column
于 2017-06-21T17:52:41.643 回答