r-DocumentTermMatrix需要有一个术语频率加权误差

Question

我正试图在一个相当大的数据集上使用topicmodels包中的LDA（）。在尝试修复以下错误"在nr*nc:NA中由整数溢出产生"one_answers"输入矩阵的每一行都需要包含至少一个非零条目"后，我最终出现了这个错误。

ask<- read.csv('askreddit201508.csv', stringsAsFactors = F)    
myDtm <- create_matrix(as.vector(ask$title), language="english", removeNumbers=TRUE, stemWords=TRUE, weighting=weightTf)
myDtm2 = removeSparseTerms(myDtm,0.99999)
myDtm2 <- rollup(myDtm2, 2, na.rm=TRUE, FUN = sum)
rowTotals <- apply(myDtm2 , 1, sum)
myDtm2   <- myDtm2[rowTotals> 0, ]  
LDA2 <- LDA(myDtm2,100)
Error in LDA(myDtm2, 100) : 
  The DocumentTermMatrix needs to have a term frequency weighting

Answer 1

问题的一部分是，您正在通过tf idf对文档术语矩阵进行加权，但LDA需要术语计数。此外，这种删除稀疏术语的方法似乎是在创建一些已经删除了所有术语的文档。

使用quanteda包可以更容易地从文本到主题模型。方法如下：

require(quanteda)
myCorpus <- corpus(textfile("http://homepage.stat.uiowa.edu/~thanhtran/askreddit201508.csv",
                            textField = "title"))
myDfm <- dfm(myCorpus, stem = TRUE)
## Creating a dfm from a corpus ...
##    ... lowercasing
##    ... tokenizing
##    ... indexing documents: 160,707 documents
##    ... indexing features: 39,505 feature types
##    ... stemming features (English), trimmed 12563 feature variants
##    ... created a 160707 x 26942 sparse dfm
##    ... complete. 
# remove infrequent terms: see http://stats.stackexchange.com/questions/160539/is-this-interpretation-of-sparsity-accurate/160599#160599
sparsityThreshold <- round(ndoc(myDfm) * (1 - 0.99999))
myDfm2 <- trim(myDfm, minDoc = sparsityThreshold)
## Features occurring in fewer than 1.60707 documents: 12579
nfeature(myDfm2)
## [1] 14363
# fit the LDA model
require(topicmodels)
LDA2 <- LDA(quantedaformat2dtm(myDfm2), 100)

Answer 2

all.dtm <- DocumentTermMatrix(corpus,
                          control = list(weighting=weightTf)) ; inspect(all.dtm) 
tpc.mdl.LDA <- LDA(all.dtm ,k=the.number.of.topics)