我有一个语料库，包含3个变量（ID、标题、摘要）的15个观察结果的期刊数据。使用R Studio，我从.csv文件中读取数据（每次观察一行）。在执行一些文本挖掘操作时，我在使用stemCompletion方法时遇到了一些麻烦。在应用stemCompletion后，我观察到.csv的每个词干行都会提供三次结果。所有其他tm方法（例如stemDocument）只产生一个结果。我想知道为什么会发生这种情况，以及我如何解决问题
我使用了以下代码：
data.corpus <- Corpus(DataframeSource(data))
data.corpuscopy <- data.corpus
data.corpus <- tm_map(data.corpus, stemDocument)
data.corpus <- tm_map(data.corpus, stemCompletion, dictionary=data.corpuscopy)
应用stemDocument后的单一结果是例如
"> data.corpus[[1]]
physic environ sourc innov investig attribut innov space
investig physic space intersect innov innov relev attribut physic space innov reflect chang natur innov technolog advanc servic mean chang argu develop innov space similar embodi divers set valu collabor open sustain use literatur review interview benchmark examin relationship physic environ innov literatur review interview underlin innov communic human centr process result five attribut innov space present collabor enabl modifi smart attract reflect provid perspect challeng support innov creation develop physic space add conceptu develop innov space outlin physic space innov servic"
使用stemCompletion后，重复出现三次：
"$`1`
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service"
以下是一个可复制示例：
一个.csv文件，包含三个变量的三个观测值：
ID;Text A;Text B
1;Below is the first title;Innovation and Knowledge Management
2;And now the second Title;Organizational Performance and Learning are very important
3;The third title;Knowledge plays an important rule in organizations
下面是我使用的堵塞方法
data = read.csv2("Test.csv")
data[,2]=as.character(data[,2])
data[,3]=as.character(data[,3])
corpus <- Corpus(DataframeSource(data))
corpuscopy <- corpus
corpus <- tm_map(corpus, stemDocument)
corpus[[1]]
corpus <- tm_map(corpus, stemCompletion, dictionary=corpuscopy)
inspect(corpus[1:3])
在我看来，这取决于.csv中使用的变量数量，但我不知道为什么。
stemCompletion函数似乎有些奇怪。如何在tm版本0.6中使用stemCompletion并不明显。这里有一个很好的变通方法，我已经使用了这个答案。
首先，制作您拥有的CSV文件：
dat <- read.csv2( text =
"ID;Text A;Text B
1;Below is the first title;Innovation and Knowledge Management
2;And now the second Title;Organizational Performance and Learning are very important
3;The third title;Knowledge plays an important rule in organizations")
write.csv2(dat, "Test.csv", row.names = FALSE)
把它读进去，转换成语料库，然后词干：
data = read.csv2("Test.csv")
data[,2]=as.character(data[,2])
data[,3]=as.character(data[,3])
corpus <- Corpus(DataframeSource(data))
corpuscopy <- corpus
library(SnowballC)
corpus <- tm_map(corpus, stemDocument)
看看它是否有效：
inspect(corpus)
<<VCorpus (documents: 3, metadata (corpus/indexed): 0/0)>>
[[1]]
<<PlainTextDocument (metadata: 7)>>
1
Below is the first titl
Innovat and Knowledg Manag
[[2]]
<<PlainTextDocument (metadata: 7)>>
2
And now the second Titl
Organiz Perform and Learn are veri import
[[3]]
<<PlainTextDocument (metadata: 7)>>
3
The third titl
Knowledg play an import rule in organ
以下是使stemCompletion工作的好方法：
stemCompletion_mod <- function(x,dict=corpuscopy) {
PlainTextDocument(stripWhitespace(paste(stemCompletion(unlist(strsplit(as.character(x)," ")),dictionary=dict, type="shortest"),sep="", collapse=" ")))
}
检查输出，查看阀杆是否完成正常：
lapply(corpus, stemCompletion_mod)
[[1]]
<<PlainTextDocument (metadata: 7)>>
1 Below is the first title Innovation and Knowledge Management
[[2]]
<<PlainTextDocument (metadata: 7)>>
2 And now the second Title Organizational Performance and Learning are NA important
[[3]]
<<PlainTextDocument (metadata: 7)>>
3 The third title Knowledge plays an important rule in organizations
成功！