Difference between revisions of "R: stopwords"

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Line 41: Line 41:
  
 
  #loading a text file from local computer
 
  #loading a text file from local computer
  newdata<- readlines(filepath)
+
  newdata <- readlines(filepath)
 +
newdata <- readtext("filename.pdf")
 +
 
 
  #Load data as corpus
 
  #Load data as corpus
 
  #VectorSource() creates character vectors
 
  #VectorSource() creates character vectors
Line 58: Line 60:
 
  # remove stopwords
 
  # remove stopwords
 
  mydata <- tm_map(mydata, removeWords, stopwords("english"))
 
  mydata <- tm_map(mydata, removeWords, stopwords("english"))
 +
mydata <- tm_map(mydata, removeWords, stopwords::stopwords("id", source = "stopwords-iso"))
 
  #u can create custom stop words using the code below.
 
  #u can create custom stop words using the code below.
 
  #myStopwords <- c(setdiff(stopwords('english'), c("r", "big")),"use", "see", "used", "via", "amp")
 
  #myStopwords <- c(setdiff(stopwords('english'), c("r", "big")),"use", "see", "used", "via", "amp")

Latest revision as of 13:05, 1 November 2018


install.packages("stopwords")
# atau
install.packages("devtools")
devtools::install_github("quanteda/stopwords")


head(stopwords::stopwords("de", source = "snowball"), 20)
head(stopwords::stopwords("id", source = "stopwords-iso"), 20)
stopwords::stopwords_getsources()
stopwords::stopwords_getlanguages("snowball")
stopwords::stopwords_getlanguages("stopwords-iso")


Contoh 1

documents = c("She had toast for breakfast",
   "The coffee this morning was excellent", 
   "For lunch let's all have pancakes", 
   "Later in the day, there will be more talks", 
   "The talks on the first day were great", 
   "The second day should have good presentations too")
library(tm)
documents <- Corpus(VectorSource(documents))
documents = tm_map(documents, content_transformer(tolower))
documents = tm_map(documents, removePunctuation)
documents = tm_map(documents, removeWords, stopwords("english"))
documents


Contoh 2

#downloading and installing the package from CRAN
install.packages("tm")
#loading tm
library(tm)
#loading a text file from local computer
newdata <- readlines(filepath)
newdata <- readtext("filename.pdf")
#Load data as corpus
#VectorSource() creates character vectors
mydata <- Corpus(VectorSource(newdata))
# convert to lower case
mydata <- tm_map(mydata, content_transformer(tolower))
#remove ������ what would be emojis
mydata<-tm_map(mydata, content_transformer(gsub), pattern="\\W",replace=" ")
# remove URLs
removeURL <- function(x) gsub("http[^[:space:]]*", "", x)
mydata <- tm_map(mydata, content_transformer(removeURL))
# remove anything other than English letters or space
removeNumPunct <- function(x) gsub("[^[:alpha:][:space:]]*", "", x)
mydata <- tm_map(mydata, content_transformer(removeNumPunct))
# remove stopwords
mydata <- tm_map(mydata, removeWords, stopwords("english"))
mydata <- tm_map(mydata, removeWords, stopwords::stopwords("id", source = "stopwords-iso"))
#u can create custom stop words using the code below.
#myStopwords <- c(setdiff(stopwords('english'), c("r", "big")),"use", "see", "used", "via", "amp")
#mydata <- tm_map(mydata, removeWords, myStopwords)
# remove extra whitespace
mydata <- tm_map(mydata, stripWhitespace)
# Remove numbers
mydata <- tm_map(mydata, removeNumbers)
# Remove punctuations
mydata <- tm_map(mydata, removePunctuation)


# stemmimg
library(SnowballC)
mydata <- tm_map(mydata, stemDocument)
#create a term matrix and store it as dtm
dtm <- TermDocumentMatrix(mydata)

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