View source: R/textProcessor.R
textProcessor | R Documentation |
Function that takes in a vector of raw texts (in a variety of languages) and performs basic operations. This function is essentially a wrapper tm package where various user specified options can be selected.
textProcessor(
documents,
metadata = NULL,
lowercase = TRUE,
removestopwords = TRUE,
removenumbers = TRUE,
removepunctuation = TRUE,
ucp = FALSE,
stem = TRUE,
wordLengths = c(3, Inf),
sparselevel = 1,
language = "en",
verbose = TRUE,
onlycharacter = FALSE,
striphtml = FALSE,
customstopwords = NULL,
custompunctuation = NULL,
v1 = FALSE
)
documents |
The documents to be processed. A character vector where each entry is the full text of a document (if passed as a different type it will attempt to convert to a character vector). |
metadata |
Additional data about the documents. Specifically a
|
lowercase |
Whether all words should be converted to lower case. Defaults to TRUE. |
removestopwords |
Whether stop words should be removed using the SMART stopword list (in English) or the snowball stopword lists (for all other languages). Defaults to TRUE. |
removenumbers |
Whether numbers should be removed. Defaults to TRUE. |
removepunctuation |
whether punctuation should be removed. Defaults to TRUE. |
ucp |
When TRUE passes |
stem |
Whether or not to stem words. Defaults to TRUE |
wordLengths |
From the tm package. An integer vector of length 2.
Words shorter than the minimum word length |
sparselevel |
Removes terms where at least sparselevel proportion of the entries are 0. Defaults to 1 which effectively turns the feature off. |
language |
Language used for processing. Defaults to English. |
verbose |
If true prints information as it processes. |
onlycharacter |
When TRUE, runs a regular expression substitution to replace all non-alphanumeric characters. These characters can crash textProcessor for some operating systems. May remove foreign characters depending on encoding. Defaults to FALSE. Defaults to FALSE. Runs before call to tm package. |
striphtml |
When TRUE, runs a regular expression substitution to strip html contained within <>. Defaults to FALSE. Runs before call to tm package. |
customstopwords |
A character vector containing words to be removed. Defaults to NULL which does not remove any additional words. This function is primarily for easy removal of application specific stopwords. Note that as with standard stopwords these are removed after converting everything to lower case but before removing numbers, punctuation or stemming. Thus words to be removed should be all lower case but otherwise complete. |
custompunctuation |
A character vector containing any characters to be
removed immediately after standard punctuation removal. This function exists
primarily for easy removal of application specific punctuation not caught by
the punctuation filter (although see also the |
v1 |
A logical which defaults to |
This function is designed to provide a convenient and quick way to process a relatively small volume texts for analysis with the package. It is designed to quickly ingest data in a simple form like a spreadsheet where each document sits in a single cell. If you have texts more complicated than a spreadsheet, we recommend you check out the excellent readtext package.
The processor always strips extra white space but all other processing options are optional. Stemming uses the snowball stemmers and supports a wide variety of languages. Words in the vocabulary can be dropped due to sparsity and stop word removal. If a document no longer contains any words it is dropped from the output. Specifying meta-data is a convenient way to make sure the appropriate rows are dropped from the corresponding metadata file.
When the option sparseLevel
is set to a number other than 1,
infrequently appearing words are removed. When a term is removed from the
vocabulary a message will print to the screen (as long as verbose
has
not been set to FALSE
). The message indicates the number of terms
removed (that is, the number of vocabulary entries) as well as the number of
tokens removed (appearances of individual words). The function
prepDocuments
provides additional methods to prune infrequent
words. In general the functionality there should be preferred.
We emphasize that this function is a convenience wrapper around the excellent tm package functionality without which it wouldn't be possible.
documents |
A list containing the documents in the stm format. |
vocab |
Character vector of vocabulary. |
meta |
Data frame or matrix containing the user-supplied metadata for the retained documents. |
Ingo Feinerer and Kurt Hornik (2013). tm: Text Mining Package. R package version 0.5-9.1.
Ingo Feinerer, Kurt Hornik, and David Meyer (2008). Text Mining Infrastructure in R. Journal of Statistical Software 25(5): 1-54.
readCorpus
head(gadarian)
#Process the data for analysis.
temp<-textProcessor(documents=gadarian$open.ended.response,metadata=gadarian)
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
#Example of custom punctuation removal.
docs <- c("co.rr?ec!t")
textProcessor(docs,custompunctuation=c(".","?","!"),
removepunctuation = FALSE)$vocab
#note that the above should now say "correct"
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