sparse_term_matrix: Term matrices and statistics ( document-term-matrix,...
In mlampros/textTinyR: Text Processing for Small or Big Data Files
sparse_term_matrix R Documentation
Term matrices and statistics ( document-term-matrix, term-document-matrix)
Description
Term matrices and statistics ( document-term-matrix, term-document-matrix)
Term matrices and statistics ( document-term-matrix, term-document-matrix)
Usage
# utl <- sparse_term_matrix$new(vector_data = NULL, file_data = NULL,
# document_term_matrix = TRUE)
Details
the Term_Matrix function takes either a character vector of strings or a text file and after tokenization and transformation returns either a document-term-matrix or a term-document-matrix
the triplet_data function returns the triplet data, which is used internally (in c++), to construct the Term Matrix. The triplet data could be usefull for secondary purposes, such as in word vector representations.
the global_term_weights function returns a list of length two. The first sublist includes the terms and the second sublist the global-term-weights. The tf_idf parameter should be set to FALSE and the normalize parameter to NULL. This function is normally used in conjuction with word-vector-embeddings.
the Term_Matrix_Adjust function removes sparse terms from a sparse matrix using a sparsity threshold
the term_associations function finds the associations between the given terms (Terms argument) and all the other terms in the corpus by calculating their correlation. There is also the option to keep a specific number of terms from the output table using the keep_terms parameter.
the most_frequent_terms function returns the most frequent terms of the corpus using the output of the sparse matrix. The user has the option to keep a specific number of terms from the output table using the keep_terms parameter.
Stemming of the english language is done using the porter2-stemmer, for details see https://github.com/smassung/porter2_stemmer
Methods
sparse_term_matrix$new(vector_data = NULL, file_data = NULL, document_term_matrix = TRUE)--------------Term_Matrix(sort_terms = FALSE, to_lower = FALSE, to_upper = FALSE, utf_locale = "", remove_char = "", remove_punctuation_string = FALSE, remove_punctuation_vector = FALSE, remove_numbers = FALSE, trim_token = FALSE, split_string = FALSE, split_separator = " .,;:()?!", remove_stopwords = FALSE, language = "english", min_num_char = 1, max_num_char = Inf, stemmer = NULL, min_n_gram = 1, max_n_gram = 1, skip_n_gram = 1, skip_distance = 0, n_gram_delimiter = " ", print_every_rows = 1000, normalize = NULL, tf_idf = FALSE, threads = 1, verbose = FALSE)--------------triplet_data()--------------global_term_weights()--------------Term_Matrix_Adjust(sparsity_thresh = 1.0)--------------term_associations(Terms = NULL, keep_terms = NULL, verbose = FALSE)--------------most_frequent_terms(keep_terms = NULL, threads = 1, verbose = FALSE)
Methods
Public methods
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Method new()
Usage
sparse_term_matrix$new(
vector_data = NULL,
file_data = NULL,
document_term_matrix = TRUE
)
Arguments
vector_dataeither NULL or a character vector of documents
file_dataeither NULL or a valid character path to a text file
document_term_matrixeither TRUE or FALSE. If TRUE then a document-term-matrix will be returned, otherwise a term-document-matrix
Method Term_Matrix()
Usage
sparse_term_matrix$Term_Matrix(
sort_terms = FALSE,
to_lower = FALSE,
to_upper = FALSE,
utf_locale = "",
remove_char = "",
remove_punctuation_string = FALSE,
remove_punctuation_vector = FALSE,
remove_numbers = FALSE,
trim_token = FALSE,
split_string = FALSE,
split_separator = " \r\n\t.,;:()?!//",
remove_stopwords = FALSE,
language = "english",
min_num_char = 1,
max_num_char = Inf,
stemmer = NULL,
min_n_gram = 1,
max_n_gram = 1,
skip_n_gram = 1,
skip_distance = 0,
n_gram_delimiter = " ",
print_every_rows = 1000,
normalize = NULL,
tf_idf = FALSE,
threads = 1,
verbose = FALSE
)
Arguments
sort_termseither TRUE or FALSE specifying if the initial terms should be sorted ( so that the output sparse matrix is sorted in alphabetical order )
to_lowereither TRUE or FALSE. If TRUE the character string will be converted to lower case
to_uppereither TRUE or FALSE. If TRUE the character string will be converted to upper case
utf_localethe language specific locale to use in case that either the to_lower or the to_upper parameter is TRUE and the text file language is other than english. For instance if the language of a text file is greek then the utf_locale parameter should be 'el_GR.UTF-8' ( language_country.encoding ). A wrong utf-locale does not raise an error, however the runtime of the function increases.
remove_chara string specifying the specific characters that should be removed from a text file. If the remove_char is "" then no removal of characters take place
remove_punctuation_stringeither TRUE or FALSE. If TRUE then the punctuation of the character string will be removed (applies before the split function)
remove_punctuation_vectoreither TRUE or FALSE. If TRUE then the punctuation of the vector of the character strings will be removed (after the string split has taken place)
remove_numberseither TRUE or FALSE. If TRUE then any numbers in the character string will be removed
trim_tokeneither TRUE or FALSE. If TRUE then the string will be trimmed (left and/or right)
split_stringeither TRUE or FALSE. If TRUE then the character string will be split using the split_separator as delimiter. The user can also specify multiple delimiters.
split_separatora character string specifying the character delimiter(s)
remove_stopwordseither TRUE, FALSE or a character vector of user defined stop words. If TRUE then by using the language parameter the corresponding stop words vector will be uploaded.
languagea character string which defaults to english. If the remove_stopwords parameter is TRUE then the corresponding stop words vector will be uploaded. Available languages
are afrikaans, arabic, armenian, basque, bengali, breton, bulgarian, catalan,
croatian, czech, danish, dutch, english, estonian,
finnish, french, galician, german, greek, hausa, hebrew, hindi, hungarian,
indonesian, irish, italian, latvian, marathi,
norwegian, persian, polish, portuguese, romanian, russian, slovak, slovenian,
somalia, spanish, swahili, swedish, turkish, yoruba, zulu
min_num_charan integer specifying the minimum number of characters to keep. If the min_num_char is greater than 1 then character strings with more than 1 characters will be returned
max_num_charan integer specifying the maximum number of characters to keep. The max_num_char should be less than or equal to Inf (in this function the Inf value translates to a word-length of 1000000000)
stemmera character string specifying the stemming method. Available method is the porter2_stemmer. See details for more information.
min_n_graman integer specifying the minimum number of n-grams. The minimum number of min_n_gram is 1.
max_n_graman integer specifying the maximum number of n-grams. The minimum number of max_n_gram is 1.
skip_n_graman integer specifying the number of skip-n-grams. The minimum number of skip_n_gram is 1. The skip_n_gram gives the (max.) n-grams using the skip_distance parameter. If skip_n_gram is greater than 1 then both min_n_gram and max_n_gram should be set to 1.
skip_distancean integer specifying the skip distance between the words. The minimum value for the skip distance is 0, in which case simple n-grams will be returned.
n_gram_delimitera character string specifying the n-gram delimiter (applies to both n-gram and skip-n-gram cases)
print_every_rowsa numeric value greater than 1 specifying the print intervals. Frequent output in the R session can slow down the function in case of big files.
normalizeeither NULL or one of 'l1' or 'l2' normalization.
tf_idfeither TRUE or FALSE. If TRUE then the term-frequency-inverse-document-frequency will be returned
threadsan integer specifying the number of cores to run in parallel
verboseeither TRUE or FALSE. If TRUE then information will be printed out
Method triplet_data()
Usage
sparse_term_matrix$triplet_data()
Method global_term_weights()
Usage
sparse_term_matrix$global_term_weights()
Method Term_Matrix_Adjust()
Usage
sparse_term_matrix$Term_Matrix_Adjust(sparsity_thresh = 1)
Arguments
sparsity_thresha float number between 0.0 and 1.0 specifying the sparsity threshold in the Term_Matrix_Adjust function
Method term_associations()
Usage
sparse_term_matrix$term_associations(
Terms = NULL,
keep_terms = NULL,
verbose = FALSE
)
Arguments
Termsa character vector specifying the character strings for which the associations will be calculated ( term_associations function )
keep_termseither NULL or a numeric value specifying the number of terms to keep ( both in term_associations and most_frequent_terms functions )
verboseeither TRUE or FALSE. If TRUE then information will be printed out
Method most_frequent_terms()
Usage
sparse_term_matrix$most_frequent_terms(
keep_terms = NULL,
threads = 1,
verbose = FALSE
)
Arguments
keep_termseither NULL or a numeric value specifying the number of terms to keep ( both in term_associations and most_frequent_terms functions )
threadsan integer specifying the number of cores to run in parallel
verboseeither TRUE or FALSE. If TRUE then information will be printed out
Method clone()
The objects of this class are cloneable with this method.
Usage
sparse_term_matrix$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## Not run:
library(textTinyR)
sm <- sparse_term_matrix$new(file_data = "/folder/my_data.txt",
document_term_matrix = TRUE)
#--------------
# term matrix :
#--------------
sm$Term_Matrix(sort_terms = TRUE, to_lower = TRUE,
trim_token = TRUE, split_string = TRUE,
remove_stopwords = TRUE, normalize = 'l1',
stemmer = 'porter2_stemmer', threads = 1 )
#---------------
# triplet data :
#---------------
sm$triplet_data()
#----------------------
# global-term-weights :
#----------------------
sm$global_term_weights()
#-------------------------
# removal of sparse terms:
#-------------------------
sm$Term_Matrix_Adjust(sparsity_thresh = 0.995)
#-----------------------------------------------
# associations between terms of a sparse matrix:
#-----------------------------------------------
sm$term_associations(Terms = c("word", "sentence"), keep_terms = 10)
#---------------------------------------------
# most frequent terms using the sparse matrix:
#---------------------------------------------
sm$most_frequent_terms(keep_terms = 10, threads = 1)
## End(Not run)
mlampros/textTinyR documentation built on Jan. 17, 2024, 1:18 a.m.
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| sparse_term_matrix | R Documentation |
Term matrices and statistics ( document-term-matrix, term-document-matrix)
Description
Term matrices and statistics ( document-term-matrix, term-document-matrix)
Term matrices and statistics ( document-term-matrix, term-document-matrix)
Usage
# utl <- sparse_term_matrix$new(vector_data = NULL, file_data = NULL,
# document_term_matrix = TRUE)
Details
the Term_Matrix function takes either a character vector of strings or a text file and after tokenization and transformation returns either a document-term-matrix or a term-document-matrix
the triplet_data function returns the triplet data, which is used internally (in c++), to construct the Term Matrix. The triplet data could be usefull for secondary purposes, such as in word vector representations.
the global_term_weights function returns a list of length two. The first sublist includes the terms and the second sublist the global-term-weights. The tf_idf parameter should be set to FALSE and the normalize parameter to NULL. This function is normally used in conjuction with word-vector-embeddings.
the Term_Matrix_Adjust function removes sparse terms from a sparse matrix using a sparsity threshold
the term_associations function finds the associations between the given terms (Terms argument) and all the other terms in the corpus by calculating their correlation. There is also the option to keep a specific number of terms from the output table using the keep_terms parameter.
the most_frequent_terms function returns the most frequent terms of the corpus using the output of the sparse matrix. The user has the option to keep a specific number of terms from the output table using the keep_terms parameter.
Stemming of the english language is done using the porter2-stemmer, for details see https://github.com/smassung/porter2_stemmer
Methods
sparse_term_matrix$new(vector_data = NULL, file_data = NULL, document_term_matrix = TRUE)--------------Term_Matrix(sort_terms = FALSE, to_lower = FALSE, to_upper = FALSE, utf_locale = "", remove_char = "", remove_punctuation_string = FALSE, remove_punctuation_vector = FALSE, remove_numbers = FALSE, trim_token = FALSE, split_string = FALSE, split_separator = " .,;:()?!", remove_stopwords = FALSE, language = "english", min_num_char = 1, max_num_char = Inf, stemmer = NULL, min_n_gram = 1, max_n_gram = 1, skip_n_gram = 1, skip_distance = 0, n_gram_delimiter = " ", print_every_rows = 1000, normalize = NULL, tf_idf = FALSE, threads = 1, verbose = FALSE)--------------triplet_data()--------------global_term_weights()--------------Term_Matrix_Adjust(sparsity_thresh = 1.0)--------------term_associations(Terms = NULL, keep_terms = NULL, verbose = FALSE)--------------most_frequent_terms(keep_terms = NULL, threads = 1, verbose = FALSE)
Methods
Public methods
Method new()
Usage
sparse_term_matrix$new( vector_data = NULL, file_data = NULL, document_term_matrix = TRUE )
Arguments
vector_dataeither NULL or a character vector of documents
file_dataeither NULL or a valid character path to a text file
document_term_matrixeither TRUE or FALSE. If TRUE then a document-term-matrix will be returned, otherwise a term-document-matrix
Method Term_Matrix()
Usage
sparse_term_matrix$Term_Matrix( sort_terms = FALSE, to_lower = FALSE, to_upper = FALSE, utf_locale = "", remove_char = "", remove_punctuation_string = FALSE, remove_punctuation_vector = FALSE, remove_numbers = FALSE, trim_token = FALSE, split_string = FALSE, split_separator = " \r\n\t.,;:()?!//", remove_stopwords = FALSE, language = "english", min_num_char = 1, max_num_char = Inf, stemmer = NULL, min_n_gram = 1, max_n_gram = 1, skip_n_gram = 1, skip_distance = 0, n_gram_delimiter = " ", print_every_rows = 1000, normalize = NULL, tf_idf = FALSE, threads = 1, verbose = FALSE )
Arguments
sort_termseither TRUE or FALSE specifying if the initial terms should be sorted ( so that the output sparse matrix is sorted in alphabetical order )
to_lowereither TRUE or FALSE. If TRUE the character string will be converted to lower case
to_uppereither TRUE or FALSE. If TRUE the character string will be converted to upper case
utf_localethe language specific locale to use in case that either the to_lower or the to_upper parameter is TRUE and the text file language is other than english. For instance if the language of a text file is greek then the utf_locale parameter should be 'el_GR.UTF-8' ( language_country.encoding ). A wrong utf-locale does not raise an error, however the runtime of the function increases.
remove_chara string specifying the specific characters that should be removed from a text file. If the remove_char is "" then no removal of characters take place
remove_punctuation_stringeither TRUE or FALSE. If TRUE then the punctuation of the character string will be removed (applies before the split function)
remove_punctuation_vectoreither TRUE or FALSE. If TRUE then the punctuation of the vector of the character strings will be removed (after the string split has taken place)
remove_numberseither TRUE or FALSE. If TRUE then any numbers in the character string will be removed
trim_tokeneither TRUE or FALSE. If TRUE then the string will be trimmed (left and/or right)
split_stringeither TRUE or FALSE. If TRUE then the character string will be split using the split_separator as delimiter. The user can also specify multiple delimiters.
split_separatora character string specifying the character delimiter(s)
remove_stopwordseither TRUE, FALSE or a character vector of user defined stop words. If TRUE then by using the language parameter the corresponding stop words vector will be uploaded.
languagea character string which defaults to english. If the remove_stopwords parameter is TRUE then the corresponding stop words vector will be uploaded. Available languages are afrikaans, arabic, armenian, basque, bengali, breton, bulgarian, catalan, croatian, czech, danish, dutch, english, estonian, finnish, french, galician, german, greek, hausa, hebrew, hindi, hungarian, indonesian, irish, italian, latvian, marathi, norwegian, persian, polish, portuguese, romanian, russian, slovak, slovenian, somalia, spanish, swahili, swedish, turkish, yoruba, zulu
min_num_charan integer specifying the minimum number of characters to keep. If the min_num_char is greater than 1 then character strings with more than 1 characters will be returned
max_num_charan integer specifying the maximum number of characters to keep. The max_num_char should be less than or equal to Inf (in this function the Inf value translates to a word-length of 1000000000)
stemmera character string specifying the stemming method. Available method is the porter2_stemmer. See details for more information.
min_n_graman integer specifying the minimum number of n-grams. The minimum number of min_n_gram is 1.
max_n_graman integer specifying the maximum number of n-grams. The minimum number of max_n_gram is 1.
skip_n_graman integer specifying the number of skip-n-grams. The minimum number of skip_n_gram is 1. The skip_n_gram gives the (max.) n-grams using the skip_distance parameter. If skip_n_gram is greater than 1 then both min_n_gram and max_n_gram should be set to 1.
skip_distancean integer specifying the skip distance between the words. The minimum value for the skip distance is 0, in which case simple n-grams will be returned.
n_gram_delimitera character string specifying the n-gram delimiter (applies to both n-gram and skip-n-gram cases)
print_every_rowsa numeric value greater than 1 specifying the print intervals. Frequent output in the R session can slow down the function in case of big files.
normalizeeither NULL or one of 'l1' or 'l2' normalization.
tf_idfeither TRUE or FALSE. If TRUE then the term-frequency-inverse-document-frequency will be returned
threadsan integer specifying the number of cores to run in parallel
verboseeither TRUE or FALSE. If TRUE then information will be printed out
Method triplet_data()
Usage
sparse_term_matrix$triplet_data()
Method global_term_weights()
Usage
sparse_term_matrix$global_term_weights()
Method Term_Matrix_Adjust()
Usage
sparse_term_matrix$Term_Matrix_Adjust(sparsity_thresh = 1)
Arguments
sparsity_thresha float number between 0.0 and 1.0 specifying the sparsity threshold in the Term_Matrix_Adjust function
Method term_associations()
Usage
sparse_term_matrix$term_associations( Terms = NULL, keep_terms = NULL, verbose = FALSE )
Arguments
Termsa character vector specifying the character strings for which the associations will be calculated ( term_associations function )
keep_termseither NULL or a numeric value specifying the number of terms to keep ( both in term_associations and most_frequent_terms functions )
verboseeither TRUE or FALSE. If TRUE then information will be printed out
Method most_frequent_terms()
Usage
sparse_term_matrix$most_frequent_terms( keep_terms = NULL, threads = 1, verbose = FALSE )
Arguments
keep_termseither NULL or a numeric value specifying the number of terms to keep ( both in term_associations and most_frequent_terms functions )
threadsan integer specifying the number of cores to run in parallel
verboseeither TRUE or FALSE. If TRUE then information will be printed out
Method clone()
The objects of this class are cloneable with this method.
Usage
sparse_term_matrix$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## Not run:
library(textTinyR)
sm <- sparse_term_matrix$new(file_data = "/folder/my_data.txt",
document_term_matrix = TRUE)
#--------------
# term matrix :
#--------------
sm$Term_Matrix(sort_terms = TRUE, to_lower = TRUE,
trim_token = TRUE, split_string = TRUE,
remove_stopwords = TRUE, normalize = 'l1',
stemmer = 'porter2_stemmer', threads = 1 )
#---------------
# triplet data :
#---------------
sm$triplet_data()
#----------------------
# global-term-weights :
#----------------------
sm$global_term_weights()
#-------------------------
# removal of sparse terms:
#-------------------------
sm$Term_Matrix_Adjust(sparsity_thresh = 0.995)
#-----------------------------------------------
# associations between terms of a sparse matrix:
#-----------------------------------------------
sm$term_associations(Terms = c("word", "sentence"), keep_terms = 10)
#---------------------------------------------
# most frequent terms using the sparse matrix:
#---------------------------------------------
sm$most_frequent_terms(keep_terms = 10, threads = 1)
## End(Not run)
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