stylo (version 0.5.7)

classify: Machine-learning classification

Description

Function that performs a number of machine-learning methods for classification used in computational stylistics: Delta (Burrows, 2002), k-Nearest Neighbors, Support Vector Machines, Naive Bayes, and Nearest Shrunken Centroids (Jockers and Witten, 2010). Most of the options are derived from the stylo function.

Usage

classify(gui = TRUE, training.frequencies = NULL, test.frequencies = NULL,
         training.corpus = NULL, test.corpus = NULL, features = NULL, 
         path = NULL, training.corpus.dir = "primary_set",
         test.corpus.dir = "secondary_set", ...)

Arguments

gui

an optional argument; if switched on, a simple yet effective graphical user interface (GUI) will appear. Default value is TRUE.

training.frequencies

using this optional argument, one can load a custom table containing frequencies/counts for several variables, e.g. most frequent words, across a number of text samples (for the training set). It can be either an R object (matrix or da

test.frequencies

using this optional argument, one can load a custom table containing frequencies/counts for the test set). Further details: immediately above.

training.corpus

another option is to pass a pre-processed corpus as an argument (here: the training set). It is assumed that this object is a list, each element of which is a vector containing one tokenized sample. The example shown below will give you some

test.corpus

if training.corpus is used, then you should also prepare a similar R object containing the test set.

features

usually, a number of the most frequent features (words, word n-grams, character n-grams) are extracted automatically from the corpus, and they are used as variables for further analysis. However, in some cases it makes sense to use a set of ta

path

if not specified, the current directory will be used for input/output procedures (reading files, outputting the results).

training.corpus.dir

the subdirectory (within the current working directory) that contains the training set, or the collection of texts used to exemplify the differences between particular classes (e.g. authors or genres). The discriminating features extracted from t

test.corpus.dir

the subdirectory (within the working directory) that contains the test set, or the collection of texts that are used to test the effectiveness of the discriminative features extracted from the training set. In the case of authorship attribut

...

any variable as produced by stylo.default.settings() can be set here to overwrite the default values.

Value

• The function returns an object of the class stylo.results: a list of variables, including tables of word frequencies, vector of features used, a distance table and some more stuff. Additionally, depending on which options have been chosen, the function produces a number of files used to save the results, features assessed, generated tables of distances, etc.

Details

There are numerous additional options that are passed to this function; so far, they are all loaded when stylo.default.settings() is executed (it will be invoked automatically from inside this function); the user can set/change them in the GUI.

References

Eder, M. Kestemont, M. and Rybicki, J. (2013). Stylometry with R: a suite of tools. In: "Digital Humanities 2013: Conference Abstracts". University of Nebraska-Lincoln, Lincoln, NE, pp. 487-89.

Burrows, J. F. (2002). "Delta": a measure of stylistic difference and a guide to likely authorship. "Literary and Linguistic Computing", 17(3): 267-87.

Jockers, M. L. and Witten, D. M. (2010). A comparative study of machine learning methods for authorship attribution. "Literary and Linguistic Computing", 25(2): 215-23.

Argamon, S. (2008). Interpreting Burrows's Delta: geometric and probabilistic foundations. "Literary and Linguistic Computing", 23(2): 131-47.

See Also

stylo, rolling.delta, oppose

Examples

# standard usage (it builds a corpus from a collection of text files):
classify()


# loading word frequencies from two tab-delimited files:
classify(training.frequencies = "table_with_training_frequencies.txt",
         test.frequencies = "table_with_test_frequencies.txt")

         
# using two existing sub-corpora (a list containing tokenized texts):
txt1 = c("now", "i", "am", "alone", "o", "what", "a", "slave", "am", "i")
txt2 = c("what", "do", "you", "read", "my", "lord")
  setTRAIN = list(txt1, txt2)
  names(setTRAIN) = c("hamlet_sample1","polonius_sample1")
txt4 = c("to", "be", "or", "not", "to", "be")
txt5 = c("though", "this", "be", "madness", "yet", "there", "is", "method")
txt6 = c("the", "rest", "is", "silence")
  setTEST = list(txt4, txt5, txt6)
  names(setTEST) = c("hamlet_sample2", "polonius_sample2", "uncertain_1")
classify(training.corpus = setTRAIN, test.corpus = setTEST)


# using a custom set of features (words, n-grams) to be analyzed:
my.selection.of.function.words = c("the", "and", "of", "in", "if", "into", 
                                   "within", "on", "upon", "since")
classify(features = my.selection.of.function.words)


# loading a custom set of features (words, n-grams) from a file:
classify(features = "wordlist.txt")


# batch mode, custom name of corpus directories:
my.test = classify(gui = FALSE, training.corpus.dir = "TrainingSet",
       test.corpus.dir = "TestSet")
summary(my.test)


# batch mode, character 3-grams requested:
classify(gui = FALSE, analyzed.features = "c", ngram.size = 3)