Question

我正在尝试使用预先标识的术语构建文档术语矩阵。语料库在变量cname中标识，具有预先标识的术语的文件被读入术语变量，然后将其转换为列表。当我运行下面的代码时，我得到一个空的DTM。我在下面使用的代码。关于我做错了什么的任何想法？谢谢!!!

汤姆

library(tm) 
library(Rmpfr) 
library(stm)

#Loading Documents
cname <- file.path("", "corpus", "goodsmoklss")
library(tm)
corp <- VCorpus(DirSource(cname))

#Transformations
docs<-tm_map(corp,tolower) #AllLowerCase
docs<-tm_map(corp,removeNumbers) #RemoveNumbers

#Remove Stopwords like is, was, the etc
docs<-tm_map(corp, removeWords, stopwords("english"))

#make Sure it is a PLainTextDocument
documents<-tm_map(docs,PlainTextDocument)


#read in list of preidentified terms
terms=read.delim("C:/corpus/TermList.csv", header=F, stringsAsFactor=F)
tokenizing.phrases <- c(terms)

library("RWeka")

phraseTokenizer <- function(x) {
  require(stringr)

  x <- as.character(x) # extract the plain text from TextDocument object
  x <- str_trim(x)
  if (is.na(x)) return("")

  phrase.hits <- str_detect(x, coll(tokenizing.phrases))


  if (any(phrase.hits)) {
    # only split once on the first hit, so we don't have to worry about    #multiple occurences of the same phrase
    split.phrase <- tokenizing.phrases[which(phrase.hits)[1]] 
    #warning(paste("split phrase:", split.phrase))
    temp <- unlist(str_split(x, coll(split.phrase), 2))
    out <- c(phraseTokenizer(temp[1]), split.phrase, phraseTokenizer(temp[2])) 
  } else {
    #out <- MC_tokenizer(x)
    out <- " "
  }

  # get rid of any extraneous empty strings, which can happen if a phrase occurs just before a punctuation
  out[out != ""]
}

dtm <- DocumentTermMatrix(documents, control = list(tokenize = phraseTokenizer))

Answer 1

我对TM并不熟悉，但在quanteda包中你可以简单地进行子集或过滤。这里应该适用同样的原则。我认为您应该能够构建DTM，然后根据您感兴趣的术语向量进行过滤。首先按上述方法制作DTM。

v <- ("your","terms","here")
to_filter <- colnames(dtm)

#then you can simply filter based on the vector
dtm2 <- dtm[,to_filter %in% v]

您可能需要先考虑先截断字典和语料库。如果语料库有很多术语和文档内存可能是一个问题。

使用术语列表记录术语矩阵

1 个答案: