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)

dfm_smooth(x, smoothing = 1)

## Arguments

x 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 logarithm of 1 + each count, for the given base: $$\textrm{log}_{base}(1 + tf_{ij})$$ booleanrecode all non-zero counts as 1 augmentedequivalent to $$K + (1 - K) *$$ dfm_weight(x, "propmax") logave1 + the log of the counts) / (1 + log of the counts / the average count within document), or $$\frac{1 + \textrm{log}_{base} tf_{ij}}{1 + \textrm{log}_{base}(\sum_j tf_{ij} / N_i)}$$ 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

## 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, Christopher D., Prabhakar Raghavan, and Hinrich Schutze. Introduction to Information Retrieval. Vol. 1. Cambridge: Cambridge University Press, 2008.

## Examples

my_dfm <- dfm(data_corpus_inaugural) x <- apply(my_dfm, 1, function(tf) tf/max(tf)) topfeatures(my_dfm)
norm_dfm <- dfm_weight(my_dfm, "prop")
topfeatures(norm_dfm)
max_tf_dfm <- dfm_weight(my_dfm) topfeatures(max_tf_dfm)
log_tf_dfm <- dfm_weight(my_dfm, scheme = "logcount") topfeatures(log_tf_dfm)