R/textstat_simil.R
textstat_simil.Rd
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.
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, ... ) # S3 method for textstat_proxy as.list(x, sorted = TRUE, n = NULL, diag = FALSE, ...) # S3 method for textstat_proxy as.data.frame( x, row.names = NULL, optional = FALSE, diag = FALSE, upper = FALSE, ... )
x, y | a dfm objects; |
---|---|
selection | (deprecated - use |
margin | identifies the margin of the dfm on which similarity or
difference will be computed: |
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. |
sorted | sort results in descending order if |
n | the top |
diag | logical; if |
row.names |
|
optional | logical. If |
upper | logical; if |
A sparse matrix from the Matrix package that will be symmetric
unless y
is specified.
These 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,
as.dist()
to be transformed into a dist object, or
as.matrix()
to convert it into an ordinary matrix.
as.data.list
for a textstat_simil
or
textstat_dist
object returns a list equal in length to the columns of the
simil or dist object, with the rows and their values as named elements. By default,
this list excludes same-time pairs (when diag = FALSE
) and sorts the values
in descending order (when sorted = TRUE
).
as.data.frame
for a textstat_simil
or
textstat_dist
object returns a data.frame of pairwise combinations
and the and their similarity or distance value.
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"
.
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")
.
# similarities for documents dfmat <- dfm(corpus_subset(data_corpus_inaugural, Year > 2000), remove_punct = TRUE, remove = stopwords("english")) (tstat1 <- textstat_simil(dfmat, method = "cosine", margin = "documents"))#> textstat_simil object; method = "cosine" #> 2001-Bush 2005-Bush 2009-Obama 2013-Obama 2017-Trump #> 2001-Bush 1.000 0.520 0.541 0.556 0.452 #> 2005-Bush 0.520 1.000 0.458 0.516 0.435 #> 2009-Obama 0.541 0.458 1.000 0.637 0.448 #> 2013-Obama 0.556 0.516 0.637 1.000 0.455 #> 2017-Trump 0.452 0.435 0.448 0.455 1.000as.matrix(tstat1)#> 2001-Bush 2005-Bush 2009-Obama 2013-Obama 2017-Trump #> 2001-Bush 1.0000000 0.5204355 0.5411649 0.5561972 0.4518935 #> 2005-Bush 0.5204355 1.0000000 0.4575297 0.5163644 0.4349030 #> 2009-Obama 0.5411649 0.4575297 1.0000000 0.6373318 0.4481950 #> 2013-Obama 0.5561972 0.5163644 0.6373318 1.0000000 0.4546945 #> 2017-Trump 0.4518935 0.4349030 0.4481950 0.4546945 1.0000000as.list(tstat1)#> $`2001-Bush` #> 2013-Obama 2009-Obama 2005-Bush 2017-Trump #> 0.5561972 0.5411649 0.5204355 0.4518935 #> #> $`2005-Bush` #> 2001-Bush 2013-Obama 2009-Obama 2017-Trump #> 0.5204355 0.5163644 0.4575297 0.4349030 #> #> $`2009-Obama` #> 2013-Obama 2001-Bush 2005-Bush 2017-Trump #> 0.6373318 0.5411649 0.4575297 0.4481950 #> #> $`2013-Obama` #> 2009-Obama 2001-Bush 2005-Bush 2017-Trump #> 0.6373318 0.5561972 0.5163644 0.4546945 #> #> $`2017-Trump` #> 2013-Obama 2001-Bush 2009-Obama 2005-Bush #> 0.4546945 0.4518935 0.4481950 0.4349030 #>#> $`2001-Bush` #> 2001-Bush 2013-Obama 2009-Obama 2005-Bush 2017-Trump #> 1.0000000 0.5561972 0.5411649 0.5204355 0.4518935 #> #> $`2005-Bush` #> 2005-Bush 2001-Bush 2013-Obama 2009-Obama 2017-Trump #> 1.0000000 0.5204355 0.5163644 0.4575297 0.4349030 #> #> $`2009-Obama` #> 2009-Obama 2013-Obama 2001-Bush 2005-Bush 2017-Trump #> 1.0000000 0.6373318 0.5411649 0.4575297 0.4481950 #> #> $`2013-Obama` #> 2013-Obama 2009-Obama 2001-Bush 2005-Bush 2017-Trump #> 1.0000000 0.6373318 0.5561972 0.5163644 0.4546945 #> #> $`2017-Trump` #> 2017-Trump 2013-Obama 2001-Bush 2009-Obama 2005-Bush #> 1.0000000 0.4546945 0.4518935 0.4481950 0.4349030 #># min_simil (tstat2 <- textstat_simil(dfmat, method = "cosine", margin = "documents", min_simil = 0.6))#> textstat_simil object; method = "cosine" #> 2001-Bush 2005-Bush 2009-Obama 2013-Obama 2017-Trump #> 2001-Bush 1 . . . . #> 2005-Bush . 1 . . . #> 2009-Obama . . 1.000 0.637 . #> 2013-Obama . . 0.637 1.000 . #> 2017-Trump . . . . 1as.matrix(tstat2)#> 2001-Bush 2005-Bush 2009-Obama 2013-Obama 2017-Trump #> 2001-Bush 1 NA NA NA NA #> 2005-Bush NA 1 NA NA NA #> 2009-Obama NA NA 1.0000000 0.6373318 NA #> 2013-Obama NA NA 0.6373318 1.0000000 NA #> 2017-Trump NA NA NA NA 1# similarities for for specific documents textstat_simil(dfmat, dfmat["2017-Trump", ], margin = "documents")#> textstat_simil object; method = "correlation" #> 2017-Trump #> 2001-Bush 0.364 #> 2005-Bush 0.344 #> 2009-Obama 0.343 #> 2013-Obama 0.361 #> 2017-Trump 1.000textstat_simil(dfmat, dfmat["2017-Trump", ], method = "cosine", margin = "documents")#> textstat_simil object; method = "cosine" #> 2017-Trump #> 2001-Bush 0.452 #> 2005-Bush 0.435 #> 2009-Obama 0.448 #> 2013-Obama 0.455 #> 2017-Trump 1.000#> textstat_simil object; method = "correlation" #> 2009-Obama 2013-Obama #> 2001-Bush 0.439 0.468 #> 2005-Bush 0.337 0.420 #> 2009-Obama 1.000 0.550 #> 2013-Obama 0.550 1.000 #> 2017-Trump 0.343 0.361# compute some term similarities tstat3 <- textstat_simil(dfmat, dfmat[, c("fair", "health", "terror")], method = "cosine", margin = "features") head(as.matrix(tstat3), 10)#> fair health terror #> president 0.4670994 0.5606119 0.1348400 #> clinton 0.4714045 0.4629100 0.0000000 #> distinguished 0.6666667 0.6546537 0.0000000 #> guests 0.6666667 0.6546537 0.0000000 #> fellow 0.6299408 0.7423075 0.2182179 #> citizens 0.7084919 0.6667367 0.0766965 #> peaceful 0.5773503 0.5669467 0.0000000 #> transfer 0.4082483 0.2672612 0.0000000 #> authority 0.8164966 0.5345225 0.0000000 #> rare 0.5773503 0.3779645 0.0000000#> $fair #> continue chance raging differences turn dangers #> 1 1 1 1 1 1 #> #> $health #> can generations upon work without greater #> 0.9971765 0.9799579 0.9759001 0.9590244 0.9561829 0.9538210 #> #> $terror #> bestowed sacrifices ancestors generosity cooperation forty-four #> 1 1 1 1 1 1 #># distances for documents (tstat4 <- textstat_dist(dfmat, margin = "documents"))#> textstat_dist object; method = "euclidean" #> 2001-Bush 2005-Bush 2009-Obama 2013-Obama 2017-Trump #> 2001-Bush 0 52.8 49.9 48.3 47.6 #> 2005-Bush 52.8 0 60.8 56.9 57.4 #> 2009-Obama 49.9 60.8 0 48.0 54.9 #> 2013-Obama 48.3 56.9 48.0 0 53.7 #> 2017-Trump 47.6 57.4 54.9 53.7 0as.matrix(tstat4)#> 2001-Bush 2005-Bush 2009-Obama 2013-Obama 2017-Trump #> 2001-Bush 0.00000 52.84884 49.94997 48.31149 47.61302 #> 2005-Bush 52.84884 0.00000 60.84406 56.85948 57.41080 #> 2009-Obama 49.94997 60.84406 0.00000 47.98958 54.91812 #> 2013-Obama 48.31149 56.85948 47.98958 0.00000 53.73081 #> 2017-Trump 47.61302 57.41080 54.91812 53.73081 0.00000as.list(tstat4)#> $`2001-Bush` #> 2005-Bush 2009-Obama 2013-Obama 2017-Trump #> 52.84884 49.94997 48.31149 47.61302 #> #> $`2005-Bush` #> 2009-Obama 2017-Trump 2013-Obama 2001-Bush #> 60.84406 57.41080 56.85948 52.84884 #> #> $`2009-Obama` #> 2005-Bush 2017-Trump 2001-Bush 2013-Obama #> 60.84406 54.91812 49.94997 47.98958 #> #> $`2013-Obama` #> 2005-Bush 2017-Trump 2001-Bush 2009-Obama #> 56.85948 53.73081 48.31149 47.98958 #> #> $`2017-Trump` #> 2005-Bush 2009-Obama 2013-Obama 2001-Bush #> 57.41080 54.91812 53.73081 47.61302 #>as.dist(tstat4)#> 2001-Bush 2005-Bush 2009-Obama 2013-Obama #> 2005-Bush 52.84884 #> 2009-Obama 49.94997 60.84406 #> 2013-Obama 48.31149 56.85948 47.98958 #> 2017-Trump 47.61302 57.41080 54.91812 53.73081# distances for specific documents textstat_dist(dfmat, dfmat["2017-Trump", ], margin = "documents")#> textstat_dist object; method = "euclidean" #> 2017-Trump #> 2001-Bush 47.6 #> 2005-Bush 57.4 #> 2009-Obama 54.9 #> 2013-Obama 53.7 #> 2017-Trump 0#> textstat_dist object; method = "euclidean" #> 2009-Obama 2013-Obama #> 2001-Bush 49.9 48.3 #> 2005-Bush 60.8 56.9 #> 2009-Obama 0 48.0 #> 2013-Obama 48.0 0 #> 2017-Trump 54.9 53.7as.matrix(tstat5)#> 2009-Obama 2013-Obama #> 2001-Bush 49.94997 48.31149 #> 2005-Bush 60.84406 56.85948 #> 2009-Obama 0.00000 47.98958 #> 2013-Obama 47.98958 0.00000 #> 2017-Trump 54.91812 53.73081as.list(tstat5)#> $`2009-Obama` #> 2005-Bush 2017-Trump 2001-Bush 2013-Obama #> 60.84406 54.91812 49.94997 47.98958 #> #> $`2013-Obama` #> 2005-Bush 2017-Trump 2001-Bush 2009-Obama #> 56.85948 53.73081 48.31149 47.98958 #>