README.md

Vocabulary Classifier

Supervised learning for text classification based on vocabulary analysis for NodeJS. It depends on redis and is supposed to handle huge amounts of data for training.

API

new VocabularyClassifier(redisClient, ngrams)
.normalizeText(text)
.trainLabel(labelName, text, callback)
.classifyText(labelsArray, text, callback)
.removeLabel(labelName, callback)
.getLabelWordCount(labelName, callback)
.getLabels(callback)

Usage

//The classifier depends on redis
const redis = require("redis"); 

//Import the classifier
const VocabularyClassifier = require("./VocabularyClassifier.js"); 

//Create a redis client
const client = redis.createClient();

//Create a instance of the Classifier
let classifier = new VocabularyClassifier(client, 1);

//Wait until redis is connected
client.on('connect', function() {

    //Train the classifier with labeled data
    classifier.trainLabel("german", "dies ist ein deutscher text text", function(){

        //Train it again with different labeld data
        classifier.trainLabel("english", "this is an english text", function(){

            //Classify a new text
            classifier.classifyText(["german", "english"], "dies ist", function(wordForWord, reduced){
                
                //Output the result for every word
                console.log(JSON.stringify(wordForWord, null, 3));
                
                //Output the result for the entire text
                console.log(reduced);
                // > [ { label: 'german', score: 0.875 },
                //     { label: 'english', score: 0.125 } ]

                //Output the trained labels
                classifier.getLabels(function(result){
                    console.log(result);
                    // > [ 'english', 'german' ]

                    //Remove the labels to free the redis database
                    classifier.removeLabel("german");
                    classifier.removeLabel("english");
                });

            });
        });
    });
});

Example result of .classifyText(...)

The word for word result

[
   {
      "word": "text",
      "result": [
         {
            "label": "german",
            "partOfLanguage": 0.3333333333333333,
            "score": 0.625
         },
         {
            "label": "english",
            "partOfLanguage": 0.2,
            "score": 0.375
         }
      ]
   },
   ...
]

The overall text result. This text is obviously german.

[ { label: 'german', score: 0.875 },
  { label: 'english', score: 0.125 } ]

Dependencies

• redis