text mining utilities for node.js
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text-miner
text mining utilities for node.js
Introduction
The text-miner package can be easily installed via npm:
npm install text-miner
To require the module in a project, we can use the expression
var tm = require( 'text-miner' );
Corpus
The fundamental data type in the
text-minermodule is the Corpus. An instance of this class wraps a collection of documents and provides several methods to interact with this collection and perform post-processing tasks such as stemming, stopword removal etc.
A new corpus is created by calling the constructor
var my_corpus = new tm.Corpus([]);
where
[]is an array of text documents which form the data of the corpus. The class supports method chaining, such that mutliple methods can be invoked after each other, e.g.
my_corpus
.trim()
.toLower()
The following methods and properties are part of the Corpus class:
Methods
.addDoc(doc)
Add a single document to the corpus. Has to be a string.
.addDocs(docs)
Adds a collection of documents (in form of an array of strings) to the corpus.
.clean()
Strips extra whitespace from all documents, leaving only at most one whitespace between any two other characters.
.map(fun)
Applies the function supplied to
funto each document in the corpus and maps each document to the result of its respective function call.
.removeInterpunctuation()
Removes interpunctuation characters (! ? . , ; -) from all documents.
.removeNewlines()
Removes newline characters (\n) from all documents.
.removeWords(words[, case_insensitive])
Removes all words in the supplied
wordsarray from all documents. This function is usually invoked to remove stopwords. For convenience, the text-miner package ships with a list of stopwords for different languages. These are stored in the
STOPWORDSobject of the module.
Currently, stopwords for the following languages are included:
STOPWORDS.DE STOPWORDS.EN STOPWORDS.ES STOPWORDS.IT
As a concrete example, we could remove all english stopwords from corpus
my_corpusas follows:
my_corpus.removeWords( tm.STOPWORDS.EN )
The second (optional) parameter of the function
case_insensitiveexpects a Boolean indicating whether to ignore cases or not. The default value is
false.
.removeDigits()
Removes any digits occuring in the texts.
.removeInvalidCharacters()
Removes all characters which are unknown or unrepresentable in Unicode.
.stem(type)
Performs stemming of the words in each document. Two stemmers are supported: Porter and Lancaster. The former is the default option. Passing "Lancaster" to the
typeparameter of the function ensured that the latter one is used.
.toLower()
Converts all characters in the documents to lower-case.
.toUpper()
Converts all characters in the documents to upper-case.
.trim()
Strips off whitespace at the beginning and end of each document.
DocumentTermMatrix / TermDocumentMatrix
We can pass a corpus to the constructor
DocumentTermMatrixin order to create a document-term-matrix or a term-document matrix. Objects derived from either share the same methods, but differ in how the underlying matrix is represented: A
DocumentTermMatrixhas documents on its rows and columns corresponding to words, whereas a
TermDocumentMatrixhas rows corresponding to words and columns to documents.
var terms = new tm.DocumentTermMatrix( my_corpus );
An instance of either
DocumentTermMatrixor
TermDocumentMatrixhas the following properties:
Properties
.vocabulary
An array holding all the words occuring in the corpus, in order corresponding to the column entries of the document-term matrix.
.data
The document-term or term-document matrix, implemented as a nested array in JavaScript. Rows correspond to individual documents, while each column index corresponds to the respective word in
vocabulary. Each entry of
dataholds the number of counts the word appears in the respective documents. The array is sparse, such that each entry which is undefined corresponds to a value of zero.
.nDocs
The number of documents in the term matrix
.nTerms
The number of distinct words appearing in the documents
Methods
.findFreqTerms( n )
Returns all terms in alphabetical ordering which appear
nor more times in the corpus. The return value is an array of objects of the form
{word: "", count: }.
.removeSparseTerms( percent )
Remove all words from the document-term matrix which appear in less than
percentof the documents.
.weighting( fun )
Apply a weighting scheme to the entries of the document-term matrix. The
weightingmethod expects a function as its argument, which is then applied to each entry of the document-term matrix. Currently, the function
weightTfIdf, which calculates the term-frequency inverse-document-frequency (TfIdf) for each word, is the only built-in weighting function.
.fill_zeros()
Turn the document-term matrix
dtminto a non-sparse matrix by replacing each value which is
undefinedby zero and save the result.
Utils
The module exports several other utility functions.
.expandContractions( str )
Replaces all occuring English contractions by their expanded equivalents, e.g. "don't" is changed to "do not". The resulting string is returned.
.weightTfIdf( terms )
Weights document-term or term-document matrix
termsby term frequency - inverse document frequency. Mutates the input
DocumentTermMatrixor
TermDocumentMatrixobject.
Data
.STOPWORDS
An object with four keys:
DE,
EN,
ESand
IT, each of which is an
arrayof stopwords for the German, English, Spanish and Italian language, respectively.
{
"EN": [
"a",
"a's",
"able",
"about",
"above",
// (...)
],
"DE": [
// (...)
],
// (...)
}
.CONTRACTIONS
The keys of the
CONTRACTIONSobject are the contracted expressions and the corresponding values are
arraysof the possible expansions.
{
"ain't": ["am not", "are not", "is not", "has not","have not"],
"aren't": ["are no", "am not"],
"can't": ["cannot"],
// (...)
}
Unit Tests
Run tests via the command
npm test