Text Classification in JavaScript

Text classification is a big component of AI/ML. Here are a few examples of text classification use cases that can be implemented in JavaScript:

Sentiment Analysis #

This is a common application of text classification where the goal is to determine the emotional tone of a piece of text, such as a tweet or a product review. There are several JavaScript libraries that can perform sentiment analysis, including Natural, Sentiment, and AFINN. Sentiment Analysis using the Natural library: const natural = require(‘natural’); const classifier = new natural.SentimentAnalyzer(‘English’, natural.PorterStemmer, ‘afinn’);

// Train the classifier on some example data
const trainingData = [
  ['This movie is great!', 'positive'],
  ['I really enjoyed this book', 'positive'],
  ['This restaurant is terrible', 'negative'],
  ['I hated this movie', 'negative']
];

classifier.train(trainingData);

// Classify some text
const text = 'I just saw the best movie ever';
const classification = classifier.getSentiment(text);

console.log(`The sentiment of "${text}" is ${classification}`);

Spam Filtering #

Another common application of text classification is spam filtering, where the goal is to classify emails or messages as either spam or not spam. There are several JavaScript libraries that can perform spam filtering, including Node-Bayes and natural-language-classifier. Spam Filtering using the Node-Bayes library: const bayes = require(‘node-bayes’);

// Train the classifier on some example data
const classifier = bayes();
classifier.learn('buy viagra now', 'spam');
classifier.learn('watch this amazing video', 'spam');
classifier.learn('hey, are you free tonight?', 'ham');
classifier.learn('can you send me that report?', 'ham');

// Classify some text
const text = 'Check out this amazing offer!';
const classification = classifier.categorize(text);

console.log(`"${text}" is classified as ${classification}`);

Language Detection #

Text classification can also be used to detect the language of a piece of text, which can be useful for applications that need to handle multilingual content. There are several JavaScript libraries that can perform language detection, including franc and lang-detector. Language Detection using the franc library: const franc = require(‘franc’);

// Detect the language of some text
const text = 'Bonjour, comment ça va?';
const language = franc(text);

console.log(`The language of "${text}" is ${language}`);

Topic Modeling #

Text classification can be used to identify the topic or theme of a piece of text, such as an article or a blog post. There are several JavaScript libraries that can perform topic modeling, including lda and tm. Topic Modeling using the lda library: const lda = require(‘lda’);

// Perform topic modeling on some text
const text = 'This is some example text to be analyzed';
const documents = [text];

const result = lda(documents, 1, 5, ['en'], 123);

console.log(`The topic of "${text}" is ${result[0].terms}`);

Named Entity Recognition #

Text classification can also be used to identify and classify named entities, such as people, organizations, and locations, within a piece of text. There are several JavaScript libraries that can perform named entity recognition, including ner and stanford-ner. Named Entity Recognition using the ner library: const ner = require(‘ner’);

// Perform named entity recognition on some text
const text = 'John Smith is the CEO of Acme Inc.';
const entities = ner.get_entities(text);

console.log(`The named entities in "${text}" are ${entities}`);