Weight the feature frequencies in a dfm
dfm_weight(
x,
scheme = c("count", "prop", "propmax", "logcount", "boolean", "augmented", "logave"),
weights = NULL,
base = 10,
k = 0.5,
smoothing = 0.5,
force = FALSE
)
dfm_smooth(x, smoothing = 1)document-feature matrix created by dfm
a label of the weight type:
count\(tf_{ij}\), an integer feature count (default when a dfm is created)
propthe proportion of the feature counts of total feature counts (aka relative frequency), calculated as \(tf_{ij} / \sum_j tf_{ij}\)
propmaxthe proportion of the feature counts of the highest feature count in a document, \(tf_{ij} / \textrm{max}_j tf_{ij}\)
logcounttake the 1 + the logarithm of each count, for the given base, or 0 if the count was zero: \(1 + \textrm{log}_{base}(tf_{ij})\) if \(tf_{ij} > 0\), or 0 otherwise.
booleanrecode all non-zero counts as 1
augmentedequivalent to \(k + (1 - k) *\) dfm_weight(x, "propmax")
logave(1 + the log of the counts) / (1 + log of the average count within document), or $$\frac{1 + \textrm{log}_{base} tf_{ij}}{1 + \textrm{log}_{base}(\sum_j tf_{ij} / N_i)}$$
logsmoothlog of the counts + smooth, or \(tf_{ij} + s\)
if scheme is unused, then weights can be a named
numeric vector of weights to be applied to the dfm, where the names of the
vector correspond to feature labels of the dfm, and the weights will be
applied as multipliers to the existing feature counts for the corresponding
named features. Any features not named will be assigned a weight of 1.0
(meaning they will be unchanged).
base for the logarithm when scheme is "logcount" or
logave
the k for the augmentation when scheme = "augmented"
constant added to the dfm cells for smoothing, default is 1
for dfm_smooth() and 0.5 for dfm_weight()
logical; if TRUE, apply weighting scheme even if the dfm
has been weighted before. This can result in invalid weights, such as as
weighting by "prop" after applying "logcount", or after
having grouped a dfm using dfm_group().
dfm_weight returns the dfm with weighted values. Note the
because the default weighting scheme is "count", simply calling this
function on an unweighted dfm will return the same object. Many users will
want the normalized dfm consisting of the proportions of the feature counts
within each document, which requires setting scheme = "prop".
dfm_smooth returns a dfm whose values have been smoothed by
adding the smoothing amount. Note that this effectively converts a
matrix from sparse to dense format, so may exceed memory requirements
depending on the size of your input matrix.
Manning, C.D., Raghavan, P., & Schütze, H. (2008). An Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf
dfmat1 <- dfm(tokens(data_corpus_inaugural))
dfmat2 <- dfm_weight(dfmat1, scheme = "prop")
topfeatures(dfmat2)
#> the , of and . to in our
#> 3.8380892 2.8259669 2.7178890 2.1188598 2.0425030 1.7929097 1.0882477 0.8916800
#> a we
#> 0.8790146 0.8081806
dfmat3 <- dfm_weight(dfmat1)
topfeatures(dfmat3)
#> the of , and . to in a our we
#> 10183 7180 7173 5406 5155 4591 2827 2292 2224 1827
dfmat4 <- dfm_weight(dfmat1, scheme = "logcount")
topfeatures(dfmat4)
#> the , of and . to in a
#> 185.1899 177.4855 176.2702 170.1605 168.3167 166.0280 153.0303 146.2659
#> our that
#> 143.3759 141.3741
dfmat5 <- dfm_weight(dfmat1, scheme = "logave")
topfeatures(dfmat5)
#> the , of and . to in a
#> 124.00535 118.76293 118.00889 113.83001 112.55676 111.13988 102.31926 97.53391
#> our that
#> 95.57610 94.48517
# combine these methods for more complex dfm_weightings, e.g. as in Section 6.4
# of Introduction to Information Retrieval
head(dfm_tfidf(dfmat1, scheme_tf = "logcount"))
#> Document-feature matrix of: 6 documents, 9,437 features (93.83% sparse) and 4 docvars.
#> features
#> docs fellow-citizens of the senate and house representatives
#> 1789-Washington 0.4920984 0 0 0.8166095 0 1.128984 0.8127846
#> 1793-Washington 0 0 0 0 0 0 0
#> 1797-Adams 0.7268890 0 0 0.8166095 0 0 0.8127846
#> 1801-Jefferson 0.6402348 0 0 0 0 0 0
#> 1805-Jefferson 0 0 0 0 0 0 0
#> 1809-Madison 0.4920984 0 0 0 0 0 0
#> features
#> docs : among vicissitudes
#> 1789-Washington 0.2026503 0.1373836 1.071882
#> 1793-Washington 0.2026503 0 0
#> 1797-Adams 0 0.2200967 0
#> 1801-Jefferson 0.2026503 0.1373836 0
#> 1805-Jefferson 0 0.2534861 0
#> 1809-Madison 0 0 0
#> [ reached max_nfeat ... 9,427 more features ]
# smooth the dfm
dfmat <- dfm(tokens(data_corpus_inaugural))
dfm_smooth(dfmat, 0.5)
#> Document-feature matrix of: 59 documents, 9,437 features (0.00% sparse) and 4 docvars.
#> features
#> docs fellow-citizens of the senate and house
#> 1789-Washington 1.5 71.5 116.5 1.5 48.5 2.5
#> 1793-Washington 0.5 11.5 13.5 0.5 2.5 0.5
#> 1797-Adams 3.5 140.5 163.5 1.5 130.5 0.5
#> 1801-Jefferson 2.5 104.5 130.5 0.5 81.5 0.5
#> 1805-Jefferson 0.5 101.5 143.5 0.5 93.5 0.5
#> 1809-Madison 1.5 69.5 104.5 0.5 43.5 0.5
#> features
#> docs representatives : among vicissitudes
#> 1789-Washington 2.5 1.5 1.5 1.5
#> 1793-Washington 0.5 1.5 0.5 0.5
#> 1797-Adams 2.5 0.5 4.5 0.5
#> 1801-Jefferson 0.5 1.5 1.5 0.5
#> 1805-Jefferson 0.5 0.5 7.5 0.5
#> 1809-Madison 0.5 0.5 0.5 0.5
#> [ reached max_ndoc ... 53 more documents, reached max_nfeat ... 9,427 more features ]