dfm_weight: Weight the feature frequencies in a dfm

Description

Weight the feature frequencies in a dfm

Usage

dfm_weight(
  x,
  scheme = c("count", "prop", "propmax", "logcount", "boolean", "augmented", "logave"),
  weights = NULL,
  base = 10,
  K = 0.5,
  force = FALSE
)
dfm_smooth(x, smoothing = 1)

Arguments

document-feature matrix created by dfm

scheme

a label of the weight type:

count: $tf_{ij}$, an integer feature count (default when a dfm is created)
prop: the proportion of the feature counts of total feature counts (aka relative frequency), calculated as $tf_{ij} / \sum_j tf_{ij}$
propmax: the proportion of the feature counts of the highest feature count in a document, $tf_{ij} / \textrm{max}_j tf_{ij}$
logcount: take 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.
boolean: recode all non-zero counts as 1
augmented: equivalent to $K + (1 - K) *$ dfm_weight(x, "propmax")
logave: 1 + the log of the counts) / (1 + log of the counts / the average count within document), or $$\frac{1 + \textrm{log}_{base} tf_{ij}}{1 + \textrm{log}_{base}(\sum_j tf_{ij} / N_i)}$$

weights

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

base for the logarithm when scheme is "logcount" or logave

the K for the augmentation when scheme = "augmented"

force

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.

smoothing

constant added to the dfm cells for smoothing, default is 1

Value

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.

References

Manning, C.D., Raghavan, P., & Sch<U+00FC>tze, H. (2008). An Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

Examples

Run this code

# NOT RUN {
dfmat1 <- dfm(data_corpus_inaugural)

dfmat2 <- dfm_weight(dfmat1, scheme = "prop")
topfeatures(dfmat2)
dfmat3 <- dfm_weight(dfmat1)
topfeatures(dfmat3)
dfmat4 <- dfm_weight(dfmat1, scheme = "logcount")
topfeatures(dfmat4)
dfmat5 <- dfm_weight(dfmat1, scheme = "logave")
topfeatures(dfmat5)

# 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"))

# apply numeric weights
str <- c("apple is better than banana", "banana banana apple much better")
(dfmat6 <- dfm(str, remove = stopwords("english")))
dfm_weight(dfmat6, weights = c(apple = 5, banana = 3, much = 0.5))

# smooth the dfm
dfmat <- dfm(data_corpus_inaugural)
dfm_smooth(dfmat, 0.5)
# }

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