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.
new VocabularyClassifier(redisClient, ngrams) .normalizeText(text) .trainLabel(labelName, text, callback) .classifyText(labelsArray, text, callback) .removeLabel(labelName, callback) .getLabelWordCount(labelName, callback) .getLabels(callback)//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"); }); }); }); }); });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 } ]- redis