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tm_map
41
Value
tm_filter returns a corpus containing documents where FUN matches, whereas tm_index only
returns the corresponding indices.
Examples
data("crude")
# Full-text search
tm_filter(crude, FUN = function(x) any(grep("co[m]?pany", content(x))))
tm_map
Transformations on Corpora
Description
Interface to apply transformation functions (also denotedas mappings) to corpora.
Usage
## S3 method for class PCorpus
tm_map(x, FUN, ...)
## S3 method for class VCorpus
tm_map(x, FUN, ..., lazy = FALSE)
Arguments
x
Acorpus.
FUN
atransformation function taking a text document as input and returning a text
document. Thefunctioncontent_transformercan be usedtocreate awrapper
to get and set the content of text documents.
...
arguments to FUN.
lazy
alogical. Lazy mappings are mappings whicharedelayeduntilthecontentis ac-
cessed. It is useful for large corpora if only few documents will be accessed. In
such a case it avoids the computationally expensive application of the mapping
to all elements in the corpus.
Value
Acorpus with FUN applied to each document in x. In case of lazy mappings only internal flags
are set. Access of individual documents triggers the execution of the corresponding transformation
function.
Note
Lazy transformations change R’s standard evaluation semantics.
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tm_reduce
See Also
getTransformationsforavailabletransformations.
Examples
data("crude")
## Document access triggers the stemming function
## (i.e., all other documents are not stemmed yet)
tm_map(crude, stemDocument, lazy = TRUE)[[1]]
## Use wrapper to apply character processing function
tm_map(crude, content_transformer(tolower))
## Generate a custom transformation function which takes the heading as new content
headings <- function(x)
PlainTextDocument(meta(x, "heading"),
id = meta(x, "id"),
language = meta(x, "language"))
inspect(tm_map(crude, headings))
tm_reduce
Combine Transformations
Description
Fold multiple transformations (mappings) into a single one.
Usage
tm_reduce(x, tmFuns, ...)
Arguments
x
Acorpus.
tmFuns
Alist of tm transformations.
...
Arguments to the individual transformations.
Value
AsingletmtransformationfunctionobtainedbyfoldingtmFunsfromrighttoleft(viaReduce(..., right = TRUE)).
See Also
Reduce for R’s internal folding/accumulation mechanism, andgetTransformations to list avail-
able transformation (mapping) functions.
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tm_term_score
43
Examples
data(crude)
crude[[1]]
skipWords <- function(x) removeWords(x, c("it", "the"))
funs <- list(stripWhitespace,
skipWords,
removePunctuation,
content_transformer(tolower))
tm_map(crude, FUN = tm_reduce, tmFuns = funs)[[1]]
tm_term_score
Compute Score for Matching Terms
Description
Compute a score based on the number of matching terms.
Usage
## S3 method for class DocumentTermMatrix
tm_term_score(x, terms, FUN = slam::row_sums)
## S3 method for class PlainTextDocument
tm_term_score(x, terms, FUN = function(x) sum(x, na.rm = TRUE))
## S3 method for class term_frequency
tm_term_score(x, terms, FUN = function(x) sum(x, na.rm = TRUE))
## S3 method for class TermDocumentMatrix
tm_term_score(x, terms, FUN = slam::col_sums)
Arguments
x
Either aPlainTextDocument, a term frequency as returned bytermFreq, or a
TermDocumentMatrix.
terms
Acharacter vector of terms to be matched.
FUN
Afunction computing a score from the number of terms matching in x.
Value
Ascore as computed by FUN from the number of matching terms in x.
Examples
data("acq")
tm_term_score(acq[[1]], c("company", "change"))
## Not run: ## Test for positive and negative sentiments
## install.packages("tm.lexicon.GeneralInquirer", repos="http://datacube.wu.ac.at", type="source")
require("tm.lexicon.GeneralInquirer")
sapply(acq[1:10], tm_term_score, terms_in_General_Inquirer_categories("Positiv"))
sapply(acq[1:10], tm_term_score, terms_in_General_Inquirer_categories("Negativ"))
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tokenizer
tm_term_score(TermDocumentMatrix(acq[1:10],
control = list(removePunctuation = TRUE)),
terms_in_General_Inquirer_categories("Positiv"))
## End(Not run)
tokenizer
Tokenizers
Description
Tokenize a document or character vector.
Usage
MC_tokenizer(x)
scan_tokenizer(x)
Arguments
x
Acharacter vector, or anobjectthatcanbecoercedtocharacter byas.character.
Details
The quality andcorrectness of a tokenization algorithm highly depends on the context and applica-
tionscenario. Relevantfactors arethe languageof theunderlying text and the notions of whitespace
(which can vary with the used encoding and the language) and punctuation marks. Consequently,
for superior results you probably need a custom tokenization function.
scan_tokenizer Relies on scan(..., what = "character").
MC_tokenizer Implements the functionality of the tokenizer in the MC toolkit (http://www.cs.
utexas.edu/users/dml/software/mc/).
Value
Acharacter vector consisting of tokens obtained by tokenization of x.
See Also
getTokenizerstolisttokenizersprovidedbypackagetm.
Regexp_TokenizerfortokenizersusingregularexpressionsprovidedbypackageNLP.
tokenizeforasimpleregularexpressionbasedtokenizerprovidedbypackagetau.
Examples
data("crude")
MC_tokenizer(crude[[1]])
scan_tokenizer(crude[[1]])
strsplit_space_tokenizer <- function(x)
unlist(strsplit(as.character(x), "[[:space:]]+"))
strsplit_space_tokenizer(crude[[1]])
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URISource
45
URISource
Uniform Resource Identifier Source
Description
Create a uniform resource identifier source.
Usage
URISource(x, encoding = "", mode = "text")
Arguments
x
Acharacter vector of uniform resource identifiers (
URI
s.
encoding
Acharacter string describing the current encoding. It is passed toiconv to
convert the input to UTF-8.
mode
acharacter string specifying if and how
URI
sshouldbe readin. Available modes
are:
"" No read. In this casegetElem andpGetElem only deliver
URI
s.
"binary"
URI
sare read in binary raw mode (viareadBin).
"text"
URI
sare read as text (viareadLines).
Details
Auniform resource identifier source interprets each
URI
as a document.
Value
An object inheriting from URISource,SimpleSource, andSource.
See Also
Sourceforbasicinformationonthesourceinfrastructureemployedbypackagetm.
Encodingand iconvonencodings.
Examples
loremipsum <- system.file("texts", "loremipsum.txt", package = "tm")
ovid <- system.file("texts", "txt", "ovid_1.txt", package = "tm")
us <- URISource(sprintf("file://%s", c(loremipsum, ovid)))
inspect(VCorpus(us))
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VCorpus
VCorpus
Volatile Corpora
Description
Create volatile corpora.
Usage
VCorpus(x, readerControl = list(reader = reader(x), language = "en"))
as.VCorpus(x)
Arguments
x
For VCorpus aSource object, and for as.VCorpus an R object.
readerControl a named list of control parameters for reading in content from x.
reader a functioncapable of reading inand processing the format delivered by
x.
language a character givingthe language(preferablyas
IETF
languagetags, see
languageinpackageNLP).ThedefaultlanguageisassumedtobeEnglish
("en").
Details
Avolatile corpus is fully kept in memory and thus all changes only affect the corresponding R
object.
The function Corpus is a convenience alias to VCorpus.
Value
An object inheriting from VCorpus and Corpus.
See Also
Corpusforbasicinformationonthecorpusinfrastructureemployedbypackagetm.
PCorpusprovidesanimplementationwithpermanentstoragesemantics.
Examples
reut21578 <- system.file("texts", "crude", package = "tm")
VCorpus(DirSource(reut21578), list(reader = readReut21578XMLasPlain))
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VectorSource
47
VectorSource
Vector Source
Description
Create a vector source.
Usage
VectorSource(x)
Arguments
x
Avector giving the texts.
Details
Avector source interprets each element of the vector x as a document.
Value
An object inheriting from VectorSource,SimpleSource, andSource.
See Also
Sourceforbasicinformationonthesourceinfrastructureemployedbypackagetm.
Examples
docs <- c("This is a text.", "This another one.")
(vs <- VectorSource(docs))
inspect(VCorpus(vs))
weightBin
Weight Binary
Description
Binary weight a term-document matrix.
Usage
weightBin(m)
Arguments
m
ATermDocumentMatrix in term frequency format.
48
WeightFunction
Details
Formally this function is of class WeightingFunction with the additional attributes Name and
Acronym.
Value
The weighted matrix.
WeightFunction
Weighting Function
Description
Construct a weighting function for term-document matrices.
Usage
WeightFunction(x, name, acronym)
Arguments
x
Afunction which takes aTermDocumentMatrix with term frequencies as input,
weights the elements, and returns the weighted matrix.
name
Acharacter naming the weighting function.
acronym
Acharacter giving an acronym for the name of the weighting function.
Value
An object of class WeightFunction which extends the class function representing a weighting
function.
Examples
weightCutBin <- WeightFunction(function(m, cutoff) m > cutoff,
"binary with cutoff", "bincut")
weightSMART
49
weightSMART
SMART Weightings
Description
Weight a term-document matrix according to a combination of weights specified in SMART nota-
tion.
Usage
weightSMART(m, spec = "nnn", control = list())
Arguments
m
ATermDocumentMatrix in term frequency format.
spec
acharacter string consisting of three characters. The first letter specifies a term
frequency schema, the second a document frequency schema, and the third a
normalization schema. See Details for available built-in schemata.
control
alist of control parameters. See Details.
Details
Formally this function is of class WeightingFunction with the additional attributes Name and
Acronym.
The first letter of spec specifies a weighting schema for term frequencies of m:
"n" (natural) tf
i;j
counts the number of occurrences n
i;j
of a term t
i
in a document d
j
.The input
term-document matrix mis assumed to be in this standard term frequency format already.
"l" (logarithm) is defined as 1+ log
2
(tf
i;j
).
"a" (augmented) is defined as 0:5+
0:5tf
i;j
max
i
(tf
i;j
)
.
"b" (boolean) is defined as 1 if tf
i;j
>0 and 0 otherwise.
"L" (log average) is defined as
1+log
2
(tf
i;j
)
1+log
2
(ave
i2j
(tf
i;j
))
.
The second letter of spec specifies a weighting schema of document frequencies for m:
"n" (no) is defined as 1.
"t" (idf) is defined as log
2
N
df
t
where df
t
denotes how often term t occurs in all documents.
"p" (prob idf) is defined as max(0;log
2
(
N df
t
df
t
)).
The third letter of spec specifies a schema for normalization of m:
"n" (none) is defined as 1.
"c" (cosine) is defined as
p
col_sums(m2).
50
weightTf
"u" (pivotedunique) is definedas slope
p
col_sums(m2)+(1 slope)pivot where both slope
and pivot must be set via named tags in the control list.
"b" (byte size) is defined as
1
CharLength
. The parameter  must be set via the named tag alpha
in the control list.
Thefinalresultis definedbymultiplicationof the chosenterm frequencycomponentwiththe chosen
document frequency component with the chosen normalization component.
Value
The weighted matrix.
References
Christopher D. Manning and Prabhakar Raghavan and Hinrich Schütze (2008). Introduction to
Information Retrieval. Cambridge University Press, ISBN 0521865719.
Examples
data("crude")
TermDocumentMatrix(crude,
control = list(removePunctuation = TRUE,
stopwords = TRUE,
weighting = function(x)
weightSMART(x, spec = "ntc")))
weightTf
Weight by Term Frequency
Description
Weight a term-document matrix byterm frequency.
Usage
weightTf(m)
Arguments
m
ATermDocumentMatrix in term frequency format.
Details
Formally this function is of class WeightingFunction with the additional attributes Name and
Acronym.
This functionacts as the identity functionsince the inputmatrix is alreadyinterm frequencyformat.
Value
The weighted matrix.
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