Simple text profile analyser using brain.js...simple ML

This post will cover - how to create a simple text analyser to classify text based on a training set data into one or another profile.

We will use 'brain.js' for the neural network used to train on the data.

This can be done in the following steps -

1. Create a profile for a particular type of text - provide adequate training data.
2. Create training data with input, output structure.
3. Write a script to injest data splitting in the input & output format & process training data.
4. Train the network to iterate with input & outputs.
5. Execute the algorithm with new input on the trained neural net.
6. Create an html to host the scripts which would be run on the browser console.

Sample training data

const trainingData = [

    {

        input: "Very well, thank you!",

        output: { at: 1 }

    },{

        input: "Inside my baby's room", 

        output: { a1: 1 }

    },

.

.

.

]

Html page - will host a call to script - can be named text-analyser.js - this will run the 'execute function and take an input text - for e.g. - 'my daily activity'.

   -- import js with training data

   -- import js with text-analyser

   -- import brain.js for the neural net.

Text-Analyser.js - this will host the code for - 

1. processing training data

function processTrainingData(data) {

    return data.map(d => {

        return {

            input: encode(d.input),

            output: d.output

        }

    })

}

2. train using the training data 

function train(data) {

    let net = new brain.NeuralNetwork();

    net.train(processTrainingData(data));

    trainedNet = net.toFunction();

};

3. execute the code with the training completed on new input

function execute(input) {

    let results = trainedNet(encode(input));

    console.log(results)

    let output;

    let certainty;

    if (results.at > results.a1) {

        output = 'Type A'

        certainty = Math.floor(results.at * 100)

    } else { 

        output = 'Type B'

        certainty = Math.floor(results.a1 * 100)

    }

    return "I'm " + certainty + "% sure that text was of  " + output;

}

All together below statements will execute the code when the - html page is opened up in browser and output will be visible in console.

train(trainingData);

console.log(execute("This is my sweet child!"));

Reference : my text..ai.