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https://github.com/planeshifter/text-miner

text mining utilities for Node.js
https://github.com/planeshifter/text-miner

nlp text-mining

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text mining utilities for Node.js

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README 

        
[![NPM version][npm-image]][npm-url]

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[![Coverage Status][codecov-image]][codecov-url]



text-miner

==========



> text mining utilities for node.js



# Introduction



The text-miner package can be easily installed via npm:



``` bash

npm install text-miner

```



To require the module in a project, we can use the expression



``` javascript

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



``` javascript

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.



``` javascript

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:



``` javascript

STOPWORDS.DE

STOPWORDS.EN

STOPWORDS.ES

STOPWORDS.IT

```



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



``` javascript

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.



``` javascript

var terms = new tm.DocumentTermMatrix( my_corpus );

```



An instance of either `DocumentTermMatrix` or `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 `n` 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` or `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.



``` javascript

{

	"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.



``` javascript

{

	"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](http://opensource.org/licenses/MIT).



[npm-image]: https://badge.fury.io/js/text-miner.svg

[npm-url]: http://badge.fury.io/js/text-miner



[travis-image]: https://travis-ci.org/Planeshifter/text-miner.svg

[travis-url]: https://travis-ci.org/Planeshifter/text-miner



[codecov-image]: https://img.shields.io/codecov/c/github/Planeshifter/text-miner/master.svg

[codecov-url]: https://codecov.io/github/Planeshifter/text-miner?branch=master