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我正在使用 R 做一篇关于文本挖掘的学期论文。我们的任务是猜测一篇文章的语气（正面/负面）。文章存储在各自的文件夹中。我需要创建一个分类系统，该系统将通过训练样本进行学习。我重用了来自http://www.youtube.com/watch?v=j1V2McKbkLo 的代码 除了最后一行之外的整个代码都成功执行了。以下是代码。 

tone<- c("Positive", "Negative")
folderpath <- "C:/Users/Tanmay/Desktop/R practice/Week8"

options(stringAsFactors = FALSE)

corpus<-Corpus(DirSource(folderpath))
corpuscopy<-corpus
summary(corpus)
inspect(corpus)

#Clean data
CleanCorpus <- function(corpus){

  corpustemp <- tm_map(corpus, removeNumbers)
  corpustemp <- tm_map(corpus, removePunctuation)
  corpustemp <- tm_map(corpus, tolower)
  corpustemp <- tm_map(corpus, removeWords, stopwords("english"))
  corpustemp <- tm_map(corpus, stemDocument,language="english")
  corpustemp <- tm_map(corpus, stripWhitespace)

  return(corpustemp )
}


#Document term matrix
generateTDM <- function(tone,path) {

  corpusdir <- sprintf("%s/%s",path,tone)
  corpus<- Corpus(DirSource( directory=corpusdir ,encoding = "ANSI"))
  corpustemp <- CleanCorpus(corpus)
  corpusclean <- DocumentTermMatrix(corpustemp)
  corpusclean <- removeSparseTerms(corpusclean , 0.7)
  result <- list(Tone = tone, tdm = corpusclean) 
}

tdm <- lapply(tone,generateTDM,path=folderpath)

#Attach tone
ToneBindTotdm <- function(tdm){
  temp.mat <- data.matrix(tdm[["tdm"]])
  temp.df <- as.data.frame(temp.mat)
  temp.df <- cbind(temp.df,rep(tdm[["Tone"]]),nrow(temp.df))
  colnames(temp.df)[ncol(temp.df)] <- "PredictTone"
  return(temp.df)
}
Tonetdm <- lapply(tdm,ToneBindTotdm)


#Stack
Stacktdm <- do.call(rbind.fill,Tonetdm)
Stacktdm[is.na(Stacktdm)] <- 0


#Holdout

trainid <- sample(nrow(Stacktdm),ceiling(nrow(Stacktdm) * 0.7))
testid <- (1:nrow(Stacktdm)) [- trainid]

#knn
tdmone <- Stacktdm[,"PredictTone"]
tdmone.nl <- Stacktdm[, !colnames(Stacktdm) %in% "PredictTone"]

knnPredict <- knn(tdmone.nl[trainid,],tdmone.nl[testid,],tdmone[trainid],k=5)

当我尝试执行此操作时，最后一行 (knn) 出现错误：

**Error in knn(tdmone.nl[trainid, ], tdmone.nl[testid, ], tdmone[trainid],  : 
  NA/NaN/Inf in foreign function call (arg 6)
In addition: Warning messages:
1: In knn(tdmone.nl[trainid, ], tdmone.nl[testid, ], tdmone[trainid],  :
  NAs introduced by coercion
2: In knn(tdmone.nl[trainid, ], tdmone.nl[testid, ], tdmone[trainid],  :
  NAs introduced by coercion**

谁能帮帮我。另外，如果还有其他更简单更好的分类方法，请指点我。感谢和抱歉，很长的帖子。

4

1 回答 1

1

我被困在同一个问题上。但是我修改了它以删除所有 NA 值。您可以检查我的代码并比较代码中可能存在的问题。

#init
libs <- c("tm" , "plyr" , "class")
lapply(libs,require, character.only=TRUE)

#set options
options(stringsAsFactors = FALSE)

#set parameters

candidates <- c("user1" , "user2" ,"test")
pathname <- "C:/Users/prabhjot.rai/Documents/Project_r/textMining"

#clean text

cleanCorpus <- function(corpus)
{
  corpus.tmp <- tm_map(corpus, removePunctuation)
  corpus.tmp <- tm_map(corpus.tmp, stripWhitespace)
  corpus.tmp <- tm_map(corpus.tmp, content_transformer(tolower))
  corpus.tmp <- tm_map(corpus.tmp, removeWords, stopwords("english"))
  corpus.tmp <- tm_map(corpus.tmp, PlainTextDocument)
}

#build TDM

generateTDM <- function(cand,path)
{
  s.dir <- sprintf("%s/%s", path, cand)
  s.cor <- Corpus(DirSource(directory = s.dir))
  s.cor.cl <- cleanCorpus(s.cor)
  s.tdm <- TermDocumentMatrix(s.cor.cl)

  s.tdm <- removeSparseTerms(s.tdm, 0.7)
  result <- list(name = cand , tdm = s.tdm)
}



tdm <- lapply(candidates, generateTDM, path = pathname)


test <- t(data.matrix(tdm[[1]]$tdm))
rownames(test) <- c(1:nrow(test))

#attach name and convert to dataframe
makeMatrix <- function(thisTDM){

  test <- t(data.matrix(thisTDM$tdm))
  rownames(test) <- c(1:nrow(test))
  test <- as.data.frame(test, stringsAsFactors = F , na.rm = T)
  test$candidateName <- thisTDM$name
  test <- as.data.frame(test, stringsAsFactors = F , na.rm = T)
}

candTDM <- lapply(tdm, makeMatrix)

# stack all the speeches together

tdm.stack <- do.call(rbind.fill, candTDM)
tdm.stack[is.na(tdm.stack)] <- as.numeric(0)

#testing and training sets
train <- tdm.stack[ tdm.stack$candidateName!= 'test' ,  ]
train <- train[, names(train) != 'candidateName']
test <- tdm.stack[ tdm.stack$candidateName == 'test' , ]
test <- test[, names(test) != 'candidateName']
classes <- tdm.stack [ tdm.stack$candidateName != 'test' , 'candidateName']
classes <- as.factor(classes)

myknn <- knn(train=train, test = test , cl = classes , k=1)
myknn

在 user1 和 user2 文件夹旁边的 test 文件夹中保留一个测试文件，以检查此算法的输出。并将k的值保持为演讲次数的平方根，最好是奇数。并且忽略测试和训练集分配的冗余。它在我的机器上没有一行工作，所以分两行工作。

于 2015-08-21T06:23:13.060 回答