textreg {textreg}

Sparse regression of labeling vector onto all phrases in a corpus.

Description

Given a labeling and a corpus, find phrases that predict this labeling. This function calls a C++ function that builds a tree of phrases and searches it using greedy coordinate descent to solve the optimization problem associated with the associated sparse regression.

Usage

textreg(corpus, labeling, banned = NULL, objective.function = 2,
  C = 1, a = 1, maxIter = 40, verbosity = 1,
  step.verbosity = verbosity, positive.only = FALSE,
  binary.features = FALSE, no.regularization = FALSE,
  positive.weight = 1, Lq = 2, min.support = 1, min.pattern = 1,
  max.pattern = 100, gap = 0, token.type = "word",
  convergence.threshold = 1e-04)

Arguments

corpus

A list of strings or a corpus from the tm package.

labeling

A vector of +1/-1 or TRUE/FALSE indicating which documents are considered relevant and which are baseline. The +1/-1 can contain 0 whcih means drop the document.

banned

List of words that should be dropped from consideration.

objective.function

2 is hinge loss. 0 is something. 1 is something else.

C

The regularization term. 0 is no regularization.

a

What percent of regularization should be L1 loss (a=1) vs L2 loss (a=0)

maxIter

Number of gradient descent steps to take (not including intercept adjustments)

verbosity

Level of output. 0 is no printed output.

step.verbosity

Level of output for line searches. 0 is no printed output.

positive.only

Disallow negative features if true

binary.features

Just code presence/absence of a feature in a document rather than count of feature in document.

no.regularization

Do not renormalize the features at all. (Lq will be ignored.)

positive.weight

Scale weight pf all positively marked documents by this value. (1, i.e., no scaling) is default) NOT FULLY IMPLEMENTED

Lq

Rescaling to put on the features (2 is standard). Can be from 1 up. Values above 10 invoke an infinity-norm.

min.support

Only consider phrases that appear this many times or more.

min.pattern

Only consider phrases this long or longer

max.pattern

Only consider phrases this short or shorter

gap

Allow phrases that have wildcard words in them. Number is how many wildcards in a row.

token.type

"word" or "character" as tokens.

convergence.threshold

How to decide if descent has converged. (Will go for three steps at this threshold to check for flatness.)

Details

See the bathtub vignette for more complete discussion of this method and the options you might pass to it.

Value

A textreg.result object.

Examples

data( testCorpora )
textreg( testCorpora$testI$corpus, testCorpora$testI$labelI, c(), C=1, verbosity=1 )