stanfordClassifier.train(string)
stanfordClassifier.trainAll(array)
stanfordClassifier.syncClassifier()
stanfordClassifier.getDataArray()
stanfordClassifier.getValuesArray()
stanfordClassifier.getFeatureCounts()
stanfordClassifier.getLabelsArray()
stanfordClassifier.getLabelsArray()
stanfordClassifier.trimData()
stanfordClassifier.trimLabels()
stanfordClassifier.trimToSize(int)
stanfordClassifier.numClass()
stanfordClassifier.numDatumsPerLabel()
stanfordClassifier.numFeatures()
stanfordClassifier.numFeatureTokens()
stanfordClassifier.numFeatureTypes()
stanfordClassifier.printSparseFeatureMatrix()
stanfordClassifier.printSVMLightFormat()
stanfordClassifier.randomize(int)
stanfordClassifier.size()
stanfordClassifier.summaryStatistics()
Install the stanford-classifier Node.js module from the npm repository. The stanford-classifier Node.js module uses Stanford Classifier v3.5.2 internally and has node-java as a dependency. Your environment should have Java properly configured to work with node-java. You can learn more about node-java configurations here. To install the stanford-classifier run the following in the terminal:
npm install stanford-classifier --save
The module will appear in the projects root node_modules directory. The Node.js module can be viewed in the npm repository https://www.npmjs.com/package/stanford-classifier.
The classifier needs to be trained with pre-trained data. Without trained data, the classifier will not work as expected and will not be accurate. Regardless of which classification algorithm is being used, the classifier needs a robust dataset to yield accurate classifications. I built a small dataset that contains organization and band Twitter descriptions. The dataset can be used to train the stanford-classifier. It can be downloaded here.
The train() method is used to train the stanford-classifier with a pre-trained dataset. Here is an example of how to use the train() method to train the stanford-classifier.
Example :
/// Dependencies
var stanfordClassifier = require('stanford-classifier');
var byline = require('byline');
var fs = require('fs');
/// Initialize the Stanford Classifier
var sc = new stanfordClassifier();
var mem = [];
/// Create a stream to read the dataset
var stream = byline(fs.createReadStream('dataset.txt', {
encoding: 'utf8'
}));
/// Push each line into memory
stream.on('data', function(line) {
mem.push(line);
});
/// Use the training dataset in memory to train the classifier dataset
stream.on('end', function() {
for (var i = 0; i < mem.length; i++) {
var line = mem[i];
sc.train(line);
}
/// Sync the classifier with the classifiers dataset
sc.syncClassifier();
/// Use the classifier
console.log(sc.classify('Our Twitter run by the band and crew to give you an inside look into our lives on the road'));
/// BAND
});After the classifier has been trained use the syncClassifier() method to sync the trained dataset with the classifier.
Options can be sent directly to the classifier when initializing the stanford-classifier instance.
var sc = new stanfordClassifier(options);
The options can either be a path to a property file or an object. The default options are the following :
#
# Features
#
useClassFeature=true
1.useNGrams=true
1.usePrefixSuffixNGrams=true
1.maxNGramLeng=4
1.minNGramLeng=1
1.binnedLengths=10,20,30
#
# Printing
#
# printClassifier=HighWeight
printClassifierParam=200
#
# Mapping
#
goldAnswerColumn=0
displayedColumn=1
#
# Optimization
#
intern=true
sigma=3
useQN=true
QNsize=15
tolerance=1e-4
other options that can be used are here.
NPM Repository :
https://www.npmjs.com/package/stanford-classifier
Github Repository:
https://github.com/mbejda/Nodejs-Stanford-Classifier
Dataset:
https://gist.github.com/mbejda/e57d29c887cbda0b5a8f#file-band-or-organization
Blog:
http://www.mbejda.com/using-the-stanford-classifier-with-node/
If you need any help, send me a tweet on twitter @notmilobejda.
