Text classification using n-grams and cosine similarity
npm install ml-classify-text  
Use machine learning to classify text using n-grams and cosine similarity.
Minimal library that can be used both in the browser and in Node.js, that allows you to train a model with a large amount of text samples (and corresponding labels), and then use this model to quickly predict one or more appropriate labels for new text samples.
Using npm
``
npm install ml-classify-text
Using yarn
`
yarn add ml-classify-text
Import as an ES6 module
`javascript`
import Classifier from 'ml-classify-text'
Import as a CommonJS module
`javascript`
const { Classifier } = require('ml-classify-text')
`javascript`
const classifier = new Classifier()
`javascript
const positive = [
'This is great, so cool!',
'Wow, I love it!',
'It really is amazing'
]
const negative = [
'This is really bad',
'I hate it with a passion',
'Just terrible!'
]
classifier.train(positive, 'positive')
classifier.train(negative, 'negative')
`
`javascript
const predictions = classifier.predict('It sure is pretty great!')
if (predictions.length) {
predictions.forEach((prediction) => {
console.log(${prediction.label} (${prediction.confidence}))`
})
} else {
console.log('No predictions returned')
}
Returning:
`
positive (0.5423261445466404)
The following configuration options can be passed both directly to a new Model, or indirectly by passing it to the Classifier constructor.
#### Options
| Property | Type | Default | Description |
| -------------- | --------------------------- | ------- | ----------------------------------------------------------------------------------------------------- |
| nGramMin | int | 1 | Minimum n-gram size |int
| nGramMax | | 1 | Maximum n-gram size |Array
| vocabulary | \| Set \| false | [] | Terms mapped to indexes in the model data, set to false to store terms directly in the data entries |Object
| data | | {} | Key-value store of labels and training data vectors |
The default behavior is to split up texts by single words (known as a bag of words, or unigrams).
This has a few limitations, since by ignoring the order of words, it's impossible to correctly match phrases and expressions.
In comes n-grams, which, when set to use more than one word per term, act like a sliding window that moves across the text — a continuous sequence of words of the specified amount, which can greatly improve the accuracy of predictions.
#### Example of using n-grams with a size of 2 (bigrams)
`javascript
const classifier = new Classifier({
nGramMin: 2,
nGramMax: 2
})
const tokens = classifier.tokenize('I really dont like it')
console.log(tokens)
`
Returning:
`javascript`
{
'i really': 1,
'really dont': 1,
'dont like': 1,
'like it': 1
}
After training a model with large sets of data, you'll want to store all this data, to allow you to simply set up a new model using this training data at another time, and quickly make predictions.
To do this, simply use the serialize method on your Model, and either save the data structure to a file, send it to a server, or store it in any other way you want.
`javascript
const model = classifier.model
console.log(model.serialize())
`
Returning:
`
{
nGramMin: 1,
nGramMax: 1,
vocabulary: [
'this', 'is', 'great',
'so', 'cool', 'wow',
'i', 'love', 'it',
'really', 'amazing', 'bad',
'hate', 'with', 'a',
'passion', 'just', 'terrible'
],
data: {
positive: {
'0': 1, '1': 2, '2': 1,
'3': 1, '4': 1, '5': 1,
'6': 1, '7': 1, '8': 2,
'9': 1, '10': 1
},
negative: {
'0': 1, '1': 1, '6': 1,
'8': 1, '9': 1, '11': 1,
'12': 1, '13': 1, '14': 1,
'15': 1, '16': 1, '17': 1
}
}
}
- Classifier
- Model
- Vocabulary
- Prediction
Read the contribution guidelines.
Refer to the changelog for a full history of the project.
ClassifyText is licensed under the MIT license.