Planeshifter/ text-miner

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Planeshifter

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Description

text mining utilities for node.js

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Philipp Burckhardt Planeshifter

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text-miner

Name: text-miner
Brand: text-miner
SKU: //Planeshifter/text-miner
Rating: 2.085 (134 reviews)

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-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.

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

fun

to 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

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:

STOPWORDS.DE
STOPWORDS.EN
STOPWORDS.ES
STOPWORDS.IT

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

.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

type

parameter 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

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

TermDocumentMatrix

has 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

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.

.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

or 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

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.

.fill_zeros()

Turn the document-term matrix

dtm

into a non-sparse matrix by replacing each value which is

undefined

by 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

terms

by term frequency - inverse document frequency. Mutates the input

DocumentTermMatrix

TermDocumentMatrix

object.

Data

.STOPWORDS

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": [
        "a",
        "a's",
        "able",
        "about",
        "above",
        // (...)  
    ],
    "DE": [
        // (...)
    ],
    // (...)
}

.CONTRACTIONS

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

License

MIT license.