quanteda.textstats: textstat_simil – R documentation

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textstat_simil

Similarity and distance computation between documents or features

Description

These functions compute matrixes of distances and similarities between documents or features from a dfm() and return a matrix of similarities or distances in a sparse format. These methods are fast and robust because they operate directly on the sparse dfm objects. The output can easily be coerced to an ordinary matrix, a data.frame of pairwise comparisons, or a dist format.

Usage

textstat_simil(
  x,
  y = NULL,
  selection = NULL,
  margin = c("documents", "features"),
  method = c("correlation", "cosine", "jaccard", "ejaccard", "dice", "edice", "hamman",
    "simple matching"),
  min_simil = NULL,
  ...
)

textstat_dist(
  x,
  y = NULL,
  selection = NULL,
  margin = c("documents", "features"),
  method = c("euclidean", "manhattan", "maximum", "canberra", "minkowski"),
  p = 2,
  ...
)

Arguments

`x, y`	a dfm objects; `y` is an optional target matrix matching `x` in the margin on which the similarity or distance will be computed.
`selection`	(deprecated - use `y` instead).
`margin`	identifies the margin of the dfm on which similarity or difference will be computed: `"documents"` for documents or `"features"` for word/term features.
`method`	character; the method identifying the similarity or distance measure to be used; see Details.
`min_simil`	numeric; a threshold for the similarity values below which similarity values will not be returned
`...`	unused
`p`	The power of the Minkowski distance.

Details

textstat_simil options are: "correlation" (default), "cosine", "jaccard", "ejaccard", "dice", "edice", "simple matching", and "hamman".

textstat_dist options are: "euclidean" (default), "manhattan", "maximum", "canberra", and "minkowski".

Value

A sparse matrix from the Matrix package that will be symmetric unless y is specified.

Conversion to other data types

The output objects from textstat_simil() and textstat_dist() can be transformed easily into a list format using as.list(), which returns a list for each unique element of the second of the pairs, a data.frame using as.data.frame(), which returns pairwise scores, as.dist()for a dist object, or as.matrix() to convert it into an ordinary matrix.

Note

If you want to compute similarity on a "normalized" dfm object (controlling for variable document lengths, for methods such as correlation for which different document lengths matter), then wrap the input dfm in [dfm_weight](x, "prop").

Examples

# similarities for documents
library("quanteda")
dfmat <- corpus_subset(data_corpus_inaugural, Year > 2000) %>%
    tokens(remove_punct = TRUE) %>%
    tokens_remove(stopwords("english")) %>%
    dfm()
(tstat1 <- textstat_simil(dfmat, method = "cosine", margin = "documents"))
as.matrix(tstat1)
as.list(tstat1)
as.list(tstat1, diag = TRUE)

# min_simil
(tstat2 <- textstat_simil(dfmat, method = "cosine", margin = "documents", min_simil = 0.6))
as.matrix(tstat2)

# similarities for for specific documents
textstat_simil(dfmat, dfmat["2017-Trump", ], margin = "documents")
textstat_simil(dfmat, dfmat["2017-Trump", ], method = "cosine", margin = "documents")
textstat_simil(dfmat, dfmat[c("2009-Obama", "2013-Obama"), ], margin = "documents")

# compute some term similarities
tstat3 <- textstat_simil(dfmat, dfmat[, c("fair", "health", "terror")], method = "cosine",
                         margin = "features")
head(as.matrix(tstat3), 10)
as.list(tstat3, n = 6)


# distances for documents
(tstat4 <- textstat_dist(dfmat, margin = "documents"))
as.matrix(tstat4)
as.list(tstat4)
as.dist(tstat4)

# distances for specific documents
textstat_dist(dfmat, dfmat["2017-Trump", ], margin = "documents")
(tstat5 <- textstat_dist(dfmat, dfmat[c("2009-Obama" , "2013-Obama"), ], margin = "documents"))
as.matrix(tstat5)
as.list(tstat5)

## Not run: 
# plot a dendrogram after converting the object into distances
plot(hclust(as.dist(tstat4)))

## End(Not run)

quanteda.textstats

Textual Statistics for the Quantitative Analysis of Textual Data

v0.94.1

GPL-3

Authors

Kenneth Benoit [cre, aut, cph] (<https://orcid.org/0000-0002-0797-564X>), Kohei Watanabe [aut] (<https://orcid.org/0000-0001-6519-5265>), Haiyan Wang [aut] (<https://orcid.org/0000-0003-4992-4311>), Jiong Wei Lua [aut], Jouni Kuha [aut] (<https://orcid.org/0000-0002-1156-8465>), European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)

Initial release