R dtm稀疏度因tf/tfidf不同而不同，语料库相同

有人能解释一下吗

我的理解是：

tf >= 0 (absolute frequency value)

tfidf >= 0 (for negative idf, tf=0)



sparse entry = 0

nonsparse entry > 0

因此，在使用以下代码创建的两个DTM中，精确的稀疏/非解析比例应该相同

library(tm)
data(crude)

dtm <- DocumentTermMatrix(crude, control=list(weighting=weightTf))
dtm2 <- DocumentTermMatrix(crude, control=list(weighting=weightTfIdf))
dtm
dtm2

library（tm）
数据（原油）
dtm dtm2
**非/稀疏条目：2215/23105**
稀疏度：91%
最大学期长度：17
权重：术语频率-逆文档频率（标准化）（tf idf）

稀疏度可能不同。如果TF为零或IDF为零，则TF-IDF值将为零；如果每个文档中都出现一个术语，则IDF值将为零。考虑下面的例子：

txts <- c("super World", "Hello World", "Hello super top world")
library(tm)
tf <- TermDocumentMatrix(Corpus(VectorSource(txts)), control=list(weighting=weightTf))
tfidf <- TermDocumentMatrix(Corpus(VectorSource(txts)), control=list(weighting=weightTfIdf))

inspect(tf)
# <<TermDocumentMatrix (terms: 4, documents: 3)>>
# Non-/sparse entries: 8/4
# Sparsity           : 33%
# Maximal term length: 5
# Weighting          : term frequency (tf)
# 
#        Docs
# Terms   1 2 3
#   hello 0 1 1
#   super 1 0 1
#   top   0 0 1
#   world 1 1 1

inspect(tfidf)
# <<TermDocumentMatrix (terms: 4, documents: 3)>>
# Non-/sparse entries: 5/7
# Sparsity           : 58%
# Maximal term length: 5
# Weighting          : term frequency - inverse document frequency (normalized) (tf-idf)
# 
#        Docs
# Terms           1         2         3
#   hello 0.0000000 0.2924813 0.1462406
#   super 0.2924813 0.0000000 0.1462406
#   top   0.0000000 0.0000000 0.3962406
#   world 0.0000000 0.0000000 0.0000000

术语world在文档3中出现1次，共有4个术语，并且在所有3个文档中都出现：

1/2 * log2(3/2)
# [1] 0.2924813

1/4 * log2(3/3) # 1/4 * 0
# [1] 0

1/4 * log2(3/3) # 1/4 * 0
# [1] 0