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text mining utilities for node.js


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' );


The fundamental data type in the text-miner module 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.


The following methods and properties are part of the Corpus class:



Add a single document to the corpus. Has to be a string.


Adds a collection of documents (in form of an array of strings) to the corpus.


Strips extra whitespace from all documents, leaving only at most one whitespace between any two other characters.


Applies the function supplied to fun to each document in the corpus and maps each document to the result of its respective function call.


Removes interpunctuation characters (! ? . , ; -) from all documents.


Removes newline characters (\n) from all documents.

.removeWords(words[, case_insensitive])

Removes all words in the supplied words array 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 STOPWORDS object of the module.

Currently, stopwords for the following languages are included:


As a concrete example, we could remove all english stopwords from corpus my_corpus as follows:

my_corpus.removeWords( tm.STOPWORDS.EN )

The second (optional) parameter of the function case_insensitive expects a Boolean indicating whether to ignore cases or not. The default value is false.


Removes any digits occuring in the texts.


Removes all characters which are unknown or unrepresentable in Unicode.


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 type parameter of the function ensured that the latter one is used.


Converts all characters in the documents to lower-case.


Converts all characters in the documents to upper-case.


Strips off whitespace at the beginning and end of each document.

DocumentTermMatrix / TermDocumentMatrix

We can pass a corpus to the constructor DocumentTermMatrix in 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 DocumentTermMatrix has documents on its rows and columns corresponding to words, whereas a TermDocumentMatrix has rows corresponding to words and columns to documents.

var terms = new tm.DocumentTermMatrix( my_corpus );

An instance of either DocumentTermMatrix or TermDocumentMatrix has the following properties:



An array holding all the words occuring in the corpus, in order corresponding to the column entries of the document-term matrix.


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 data holds 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.


The number of documents in the term matrix


The number of distinct words appearing in the documents


.findFreqTerms( n )

Returns all terms in alphabetical ordering which appear n or more times in the corpus. The return value is an array of objects of the form {word: "<word>", count: <number>}.

.removeSparseTerms( percent )

Remove all words from the document-term matrix which appear in less than percent of the documents.

.weighting( fun )

Apply a weighting scheme to the entries of the document-term matrix. The weighting method 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.


Turn the document-term matrix dtm into a non-sparse matrix by replacing each value which is undefined by zero and save the result.


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 terms by term frequency - inverse document frequency. Mutates the input DocumentTermMatrix or TermDocumentMatrix object.



An object with four keys: DE, EN, ES and IT, each of which is an array of stopwords for the German, English, Spanish and Italian language, respectively.

	"EN": [
		// (...)  
	"DE": [
		// (...)
	// (...)


The keys of the CONTRACTIONS object are the contracted expressions and the corresponding values are arrays of 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


MIT license.

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