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lorca-nlp
Advanced tools
Readme
Lorca is a NLP library for Spanish written in javascript. Tokenization, concordance, stemmer, statistics, sentiment analysis, readability, tf-idf and more!
Not suported yet, but soon.
Run:
$ npm install lorca-nlp
Start using the library like:
const lorca = require('lorca-nlp');
var doc = lorca('esto es un test');
doc.words().get();
// [ 'esto', 'es', 'un', 'test' ]
This library lets concatenate different methods to the document you are working on. Each time a method
is executed, its output is stored in the doc object. If you need to execute methods more than once
you have to make use of the load()
method. This will reload the original document in the doc object.
var doc = lorca('En verano hace calor. En invierno hace frío');
// This stores the concordance output in the doc object.
var concordance = doc.concordance().sort().get();
// If you now need the words of your document, you must reload it with load().
var originalWords = doc.load().words().get();
Extract sentences, words or syllables.
var doc = lorca('En verano hace calor. En invierno hace frío');
doc.get();
// En verano hace calor. En invierno hace frío.
doc.sentences().get();
// [ 'En verano hace calor.', ' En invierno hace frío.' ]
doc.words().get();
// [ 'en', 'verano', 'hace', 'calor', 'en', 'invierno', 'hace', 'frío' ]
doc.syllables().get();
// [ 'en', 've', 'ra', 'no', 'ha', 'ce', 'ca', 'lor.', 'en', 'in', 'vier', 'no', 'ha', 'ce','frí', 'o.' ]
doc.uniqueWords().get();
// [ 'en', 'verano', 'hace', 'calor', 'invierno', 'frío' ]
doc.onceWords().get();
// [ 'verano', 'calor', 'invierno', 'frío' ]
Group the output by sentence, word or both.
doc.sentences().words().get();
// [ [ 'en', 'verano', 'hace', 'calor' ],
// [ 'en', 'invierno', 'hace', 'frío' ] ]
doc.sentences().words().syllables().get();
/*
[ [ [ 'en' ],
[ 've', 'ra', 'no' ],
[ 'ha', 'ce' ],
[ 'ca', 'lor' ] ],
[ [ 'en' ],
[ 'in', 'vier', 'no' ],
[ 'ha', 'ce' ],
[ 'frí', 'o' ] ] ]
*/
doc.sentences().syllables().get();
// [ [ 'En ', 've', 'ra', 'no', ' ha', 'ce ', 'ca', 'lor.' ],
// [ ' En', ' in', 'vier', 'no', ' ha', 'ce ', 'frí', 'o.' ] ]
doc.words().syllables().get();
/*
[ [ 'en' ],
[ 've', 'ra', 'no' ],
[ 'ha', 'ce' ],
[ 'ca', 'lor' ],
[ 'en' ],
[ 'in', 'vier', 'no' ],
[ 'ha', 'ce' ],
[ 'frí', 'o' ] ]
*/
Extract prepositions from text, sentences or words.
doc.prepositions().get();
// [ 'en', 'en' ]
doc.sentences().prepositions().get();
// [ [ 'en' ], [ 'en' ] ]
doc.words().prepositions().get();
// [ [ 'en' ], [], [], [], [ 'en' ], [], [], [] ]
Extract pronouns from text, sentences or words.
var doc = lorca('Yo le canto a él. Él se rie.');
doc.pronouns().get();
// [ 'yo', 'él', 'él' ]
doc.sentences().pronouns().get();
// [ [ 'yo', 'él' ], [ 'él' ] ]
doc.words().pronouns().get()
// [ [ 'yo' ], [], [], [], [ 'él' ], [ 'él' ], [], [] ]
doc.pronouns().percentage().get();
// 0.375
doc.sentences().pronouns().percentage().get();
// [ 0.4, 0.3333333333333333 ]
Extract adverbs from text, sentences or words.
var doc = lorca('En verano hace realmente calor. En invierno hace frío');
doc.adverbs().get();
// [ 'realmente' ]
doc.sentences().adverbs().get();
// [ [ 'realmente' ], [] ]
Test whether a sentence is passive.
var doc = lorca('El niño ha sido castigado.');
doc.isPassive().get();
// true
var doc = lorca('El niño ha sido castigado. La madre lo ha castigado.');
doc.sentences().isPassive().get();
// [ true, false ]
Get the word frequency of a document. The concordance method accepts the mode 'relative'
which outputs the relative frequency of the words. It is posible to sort the output by frequency and to shorten the output array with the method sort()
.
var doc = lorca('En verano hace calor. En invierno hace frío');
doc.concordance().get();
// [ en: 2, verano: 1, hace: 2, calor: 1, invierno: 1, 'frío': 1 ]
doc.concordance().sort().get();
// [ en: 2, hace: 2, verano: 1, calor: 1, invierno: 1, 'frío': 1 ]
doc.concordance('relative').sort().get();
// [ en: 0.25, hace: 0.25, verano: 0.125 calor: 0.125, invierno: 0.125, 'frío': 0.125 ]
doc.concordance().sort(3).get();
// [ en: 2, hace: 2, verano: 1 ]
// Get the relative frequency from a huge corpus given by RAE.
doc.words().corpusFrequency().get();
// [ 0.02775516,
// 0.00008453,
// 0.00075057,
// 0.00007254,
// 0.02775516,
// 0.00004106,
// 0.00075057,
// 0.00007199 ]
doc.sentences().words().corpusFrequency().get();
// [ [ 0.02775516, 0.00008453, 0.00075057, 0.00007254 ],
// [ 0.02775516, 0.00004106, 0.00075057, 0.00007199 ] ]
Get basic statistics of a text.
var doc = lorca('En verano hace calor. En invierno hace frío');
doc.words().get().length;
// 8
doc.sentences().get().length;
// 2
doc.wordsPerSentence().get();
// 4
doc.syllablesPerWord().get()
// 2
doc.syllablesPerSentence().get()
// 8
doc.uniqueWords().percentage().get();
// 0.75
doc.onceWords().percentage().get();
// 0.5
doc.prepositions().percentage().get();
// 0.25
doc.sentences().prepositions().percentage().get();
// [ 0.25, 0.25 ]
doc.ifsz().get();
// 78
doc.ifsz().grade().get();
// Bastante fácil
Disclaimer: It uses a semi-automated translation of the original AFINN list. The list only contains words that are inside the 10.000 most used words. It has a total of 885 words. The sentiment()
method calculates the relative value of each sentece and then it returns the relative values of those sentences.
Positive values mean a positive sentiment and negative values mean negative sentiment.
The sentiment algorithm stems the text input in order to reach a wider spectrum of words.
var doc = lorca('El plátano está malo.');
doc.sentiment();
// -0.75
var doc = lorca('Me gusta la navidad.');
doc.sentiment();
// 0.5
var doc = lorca('El plátano está muy bueno. Me gusta la navidad. Esto no ha sido magnífico.');
doc.sentences().sentiment();
// [ 0.6, 0.5, -1.2 ]
doc.words().sentiment();
// [ 0, 0, 0, 0, 3, 0, 2, 0, 0, 0, -1, 0, 0, 5 ]
doc.sentences().words().sentiment()
// [ [ 0, 0, 0, 0, 3 ], [ 0, 2, 0, 0 ], [ 0, -1, 0, 0, 5 ] ]
doc.sentiment();
// -0.03333333333333329
This method uses a powerful lemma-level lexicon created in 2014 by Fermín L. Cruz, José A. Troyano, Beatriz Pontes and F. Javier Ortega.
var doc = lorca('El plátano está malo.');
doc.sentiment('senticon');
// -0.09375
Get the stem of any word in Spanish. The stemmer is based on the Porter algorithm. You can also get the stemed concordance of your document.
doc.stem('recomendaciones');
// recomend
var doc = lorca('Los niños juegan con las pelotas');
doc.words().stem().get();
// [ 'los', 'niñ', 'jueg', 'con', 'las', 'pelot' ]
var doc = lorca('Los niños juegan con las pelotas. Los profesores hablan del tiempo.');
doc.sentences().words().stem().get();
// [ [ 'los', 'niñ', 'jueg', 'con', 'las', 'pelot' ],
// [ 'los', 'profesor', 'habl', 'del', 'tiemp' ] ]
var doc = lorca('Los niños juegan con las niñas.');
doc.words().stem().concordance().sort().get();
// { 'niñ': 2, los: 1, jueg: 1, con: 1, las: 1 }
The tf-idf has batteries included. It leverages a big frequency list to get the main words of any text.
var doc = lorca('En verano hace calor. En invierno hace frío. El verano me gusta');
doc.tfidf().sort().get();
// { verano: 4117.747963402167,
// invierno: 2660.600722826124,
// frío: 2192.690048750771,
// calor: 2186.3476209394835,
// gusta: 2068.4919519114765,
// hace: 478.20393517589827,
// me: -748.0756841870855,
// el: -2833.037926222993,
// en: -5539.036281420974 }
doc.tfidf().sort(2).get();
// { verano: 4117.747963402167,
// invierno: 2660.600722826124 }
Get the reading time of a text in seconds. You can pass a reading speed as
an argument in the readingtime(400)
in words per minute. If no reading
speed is given, it will use default value of 220 wpm.
var doc = lorca('El niño ha sido castigado. La madre lo ha castigado.');
doc.readingTime();
// 2.72
Search any word in the text. You can use Regex too.
var doc = lorca('En verano hace calor. En invierno hace frío');
doc.find("verano");
// [ 'verano' ]
doc.sentences().find("verano");
// [ [ 'verano' ], [] ]
$ npm test
Vic - for his Spanish hyphenator in javascript. Cruz, Fermín L., José A. Troyano, Beatriz Pontes, F. Javier Ortega. Building layered, multilingual sentiment lexicons at synset and lemma levels, Expert Systems with Applications, 2014. For their senticon list in sentiment analysis.
FAQs
NLP for Spanish
The npm package lorca-nlp receives a total of 32 weekly downloads. As such, lorca-nlp popularity was classified as not popular.
We found that lorca-nlp demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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