[Javascript] Classify JSON text data with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well.

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization.

// train data

[{text: 'xxxxxx', label: 'space'}]

// Load train data form the files and train

var natural = require('natural');
var fs = require('fs');
var classifier = new natural.BayesClassifier();

fs.readFile('training_data.json', 'utf-8', function(err, data){
if (err){
console.log(err);
} else {
var trainingData = JSON.parse(data);
train(trainingData);
}
});

function train(trainingData){
console.log("Training");
trainingData.forEach(function(item){
classifier.addDocument(item.text, item.label);
});
var startTime = new Date();
classifier.train();
var endTime = new Date();
var trainingTime = (endTime-startTime)/1000.0;
console.log("Training time:", trainingTime, "seconds");
loadTestData();
}

function loadTestData(){
console.log("Loading test data");
fs.readFile('test_data.json', 'utf-8', function(err, data){
if (err){
console.log(err);
} else {
var testData = JSON.parse(data);
testClassifier(testData);
}
});
}

function testClassifier(testData){
console.log("Testing classifier");
var numCorrect = 0;
testData.forEach(function(item){
var labelGuess = classifier.classify(item.text);
if (labelGuess === item.label){
numCorrect++;
}
});
console.log("Correct %:", numCorrect/testData.length);
　　 saveClassifier(classifier)
}

function saveClassifier(classifier){
classifier.save('classifier.json', function(err, classifier){
if (err){
console.log(err);
} else {
console.log("Classifier saved!");
}
});
}

In a new project, we can test the train result by:

var natural = require('natural');

natural.LogisticRegressionClassifier.load('classifier.json', null, function(err, classifier){
if (err){
console.log(err);
} else {
var testComment = "is this about the sun and moon?";
console.log(classifier.classify(testComment));
}
});