weightSMART: SMART Weightings
In tm: Text Mining Package

Description Usage Arguments Details Value References Examples

Weight a term-document matrix according to a combination of weights specified in SMART notation.

1	weightSMART(m, spec = "nnn", control = list())

`m`	A `TermDocumentMatrix` in term frequency format.
`spec`	a character string consisting of three characters. The first letter specifies a term frequency schema, the second a document frequency schema, and the third a normalization schema. See Details for available built-in schemata.
`control`	a list of control parameters. See Details.

Formally this function is of class WeightingFunction with the additional attributes name and acronym.

The first letter of spec specifies a weighting schema for term frequencies of m:

"n": (natural) \mathit{tf}_{i,j} counts the number of occurrences n_{i,j} of a term t_i in a document d_j. The input term-document matrix m is assumed to be in this standard term frequency format already.
"l": (logarithm) is defined as 1 + \log_2(\mathit{tf}_{i,j}).
"a": (augmented) is defined as 0.5 + \frac{0.5 * \mathit{tf}_{i,j}}{\max_i(\mathit{tf}_{i,j})}.
"b": (boolean) is defined as 1 if \mathit{tf}_{i,j} > 0 and 0 otherwise.
"L": (log average) is defined as \frac{1 + \log_2(\mathit{tf}_{i,j})}{1+\log_2(\mathrm{ave}_{i\in j}(\mathit{tf}_{i,j}))}.

The second letter of spec specifies a weighting schema of document frequencies for m:

"n": (no) is defined as 1.
"t": (idf) is defined as \log_2 \frac{N}{\mathit{df}_t} where \mathit{df}_t denotes how often term t occurs in all documents.
"p": (prob idf) is defined as \max(0, \log_2(\frac{N - \mathit{df}_t}{\mathit{df}_t})).

The third letter of spec specifies a schema for normalization of m:

"n": (none) is defined as 1.
"c": (cosine) is defined as √{\mathrm{col\_sums}(m ^ 2)}.
"u": (pivoted unique) is defined as \mathit{slope} * √{\mathrm{col\_sums}(m ^ 2)} + (1 - \mathit{slope}) * \mathit{pivot} where both slope and pivot must be set via named tags in the control list.
"b": (byte size) is defined as \frac{1}{\mathit{CharLength}^α}. The parameter α must be set via the named tag alpha in the control list.

The final result is defined by multiplication of the chosen term frequency component with the chosen document frequency component with the chosen normalization component.

The weighted matrix.

Christopher D. Manning and Prabhakar Raghavan and Hinrich Schütze (2008). Introduction to Information Retrieval. Cambridge University Press, ISBN 0521865719.

data("crude")
TermDocumentMatrix(crude,
                   control = list(removePunctuation = TRUE,
                                  stopwords = TRUE,
                                  weighting = function(x)
                                  weightSMART(x, spec = "ntc")))

Loading required package: NLP
<<TermDocumentMatrix (terms: 1000, documents: 20)>>
Non-/sparse entries: 1678/18322
Sparsity           : 92%
Maximal term length: 16
Weighting          : SMART ntc (SMART)