quanteda (version 0.9.7-17)

similarity: compute similarities between documents and/or features

Description

Compute similarities between documents and/or features from a dfm. Uses the similarity measures defined in simil. See pr_DB for available distance measures, or how to create your own.

Usage

similarity(x, selection = NULL, n = NULL, margin = c("documents", "features"), method = "correlation", sorted = TRUE, normalize = FALSE)
"similarity"(x, selection = NULL, n = NULL, margin = c("documents", "features"), method = "correlation", sorted = TRUE, normalize = FALSE)
"as.matrix"(x, ...)
"print"(x, digits = 4, ...)

Arguments

x

a dfm object

selection

character or character vector of document names or feature labels from the dfm

n

the top n most similar items will be returned, sorted in descending order. If n is NULL, return all items.

margin

identifies the margin of the dfm on which similarity will be computed: documents for documents or features for word/term features.

method

a valid method for computing similarity from pr_DB

sorted

sort results in descending order if TRUE

normalize

a deprecated argument retained (temporarily) for legacy reasons. If you want to compute similarity on a "normalized" dfm objects (e.g. x), wrap it in weight(x, "relFreq").

...

unused

digits

decimal places to display similarity values

Value

a named list of the selection labels, with a sorted named vector of similarity measures.

Examples

# create a dfm from inaugural addresses from Reagan onwards
presDfm <- dfm(subset(inaugCorpus, Year > 1980), ignoredFeatures = stopwords("english"),
               stem = TRUE)

# compute some document similarities
(tmp <- similarity(presDfm, margin = "documents"))
# output as a matrix
as.matrix(tmp)
# for specific comparisons
similarity(presDfm, "1985-Reagan", n = 5, margin = "documents")
similarity(presDfm, c("2009-Obama" , "2013-Obama"), n = 5, margin = "documents")
similarity(presDfm, c("2009-Obama" , "2013-Obama"), margin = "documents")
similarity(presDfm, c("2009-Obama" , "2013-Obama"), margin = "documents", method = "cosine")
similarity(presDfm, "2005-Bush", margin = "documents", method = "eJaccard", sorted = FALSE)

# compute some term similarities
similarity(presDfm, c("fair", "health", "terror"), method="cosine", margin = "features", 20)

## Not run: 
# # compare to tm
# require(tm)
# data("crude")
# crude <- tm_map(crude, content_transformer(tolower))
# crude <- tm_map(crude, removePunctuation)
# crude <- tm_map(crude, removeNumbers)
# crude <- tm_map(crude, stemDocument)
# tdm <- TermDocumentMatrix(crude)
# findAssocs(tdm, c("oil", "opec", "xyz"), c(0.75, 0.82, 0.1))
# # in quanteda
# quantedaDfm <- new("dfmSparse", Matrix::Matrix(t(as.matrix(tdm))))
# similarity(quantedaDfm, c("oil", "opec", "xyz"), margin = "features", n = 14)
# corMat <- as.matrix(proxy::simil(as.matrix(quantedaDfm), by_rows = FALSE))
# round(head(sort(corMat[, "oil"], decreasing = TRUE), 14), 2)
# round(head(sort(corMat[, "opec"], decreasing = TRUE), 9), 2)
# ## End(Not run)