Chatbots with TensorFlow.js Sentiment Analysis

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CHATBOTS WITH TWILIO
+ TENSORFLOW.JS

© 2019 TWILIO, INC. ALL RIGHTS RESERVED.© 2019 TWILIO, INC. ALL RIGHTS RESERVED.
- Twilio Developer Evangelist
- iOS, web developer
- ML & chatbot enthusiast

‣Difference between
AI/ML/DL
‣Some ML vocabulary
‣TensorFlow 101
‣Sentiment Analysis with
TF.js and Twilio Functions
Overview

AI
ML
DL
AI, ML, DL?

AI
ML
DL
NLP
Natural Language Processing

Computer Vision: AI for Visuals
AI
ML
DL
NLP
CV

Weak AI:
A machine simulates
thinking, human behavior

Strong AI:
A machine reproduces a
human behavior, can think

ML model:
Function with learnable parameters that map an
input to an output.

Neural Networks
Mimicking neurons in the human brain,
NNs are a set of algorithms that work to
recognize connections in a dataset

• OS library released by Google Brain in 2015
• Image classification, NLP, generate music, etc.
• Python, JS, Swift, C, etc.
• TF Lite for IoT devices

•Toxicity model to detect toxic language
•MobileNet for object detection
•BodyPix to detect body parts
•PoseNet to detect body poses
•Node.js modules
TensorFlow.js

const tf = require(‘tensorflow/tfjs’);
const fetch = require(‘node-fetch’);
Initialize libraries

exports.handler = async function(context, event, callback) {
//code
}
Handler method

//code to be added
}
const loadModel = async () => {
const url = ‘https://storage.googleapis.com/tfjs-
models/tfjs/sentiment_cnn_v1/model.json’;
const model = await tf.loadLayersModel(url);
return model;
};

//code to be added
}
//code from last slide
const getMetaData = async () => {
const metadata = await
fetch(`https://storage.googleapis.com/tfjs-
models/tfjs/sentiment_cnn_v1/metadata.json`);
return metadata.json();
}
Prepare, clean, vectorize data

const predict = (text, model, metadata) => {
const trimmed = text.trim().toLowerCase().replace(/(.|,|!)/g,
'').split(' ');
Make a prediction

const sequence = trimmed.map(word => {
const wordIndex = metadata.word_index[word];
if (typeof wordIndex === ‘undefined’) {
return 2; //oov_index
}
return wordIndex + metadata.index_from;
});
Make a prediction

const paddedSequence =
padSequences([sequence],metadata);
const input = tf.tensor2d(paddedSequence, [1,
metadata.max_len]);
const predictOut = model.predict(input);
const score = predictOut.dataSync()[0];
predictOut.dispose();
return score;
}
Make a prediction

const songArrayPos = [
“I was so ahead of the curve, the curve became a sphere”,
“I’ve never been a natural, all I do is try, try, try”
];
const songArrayNeutral = [
“They told me all my cages were mental, so I got wasted like
all my potential.”,
“When I felt like I was an old cardigan under someone’s bed,
you put me on and said I was your favorite."
];
//const songArrayNeg
Songs arrays

const getSentiment = (score) => {
if (score > 0.66) {
return `Score of ${score} is Positive. Here’s a selected Swift
folklore lyric: ${songArrayPos[songArrayPos.length *
Math.random() | 0]}`;
}
else if (score > 0.4) {
return `Score of ${score} is Neutral. Here’s a selected Swift
folklore lyric: ${songArrayNeutral[songArrayNeutral.length *
}
else {
return `Score of ${score} is Negative. Here’s a selected Swift
folklore lyric: ${songArrayNegative[songArrayNegative.length *
}
}
Calculate sentiment

//code to be added
}
Back to the handler method
let twiml = new Twilio.twiml.MessagingResponse();
const prediction = event.Body.toLowerCase().trim();
const model = await loadModel();
const metadata = await getMetaData();
console.log(`${prediction}`);
let perc = predict(prediction, model, metadata);
twiml.message(getSentiment(perc));
callback(null, twiml);
}

Buy a SMS-enabled phone #

Configure a Twilio # w/ Twilio Function
+1 804 375 4995

lsiegle@twilio.com
@lizziepika
promo: INTERNCON

MORE RESOURCES
Workshop
- https://github.com/elizabethsiegle/tensorflowjschatbot_workshop_interncon
- https://www.twilio.com/blog/how-positive-was-your-year-with-tensorflow-js-and-twilio
TensorFlow
-https://opensource.com/article/17/11/intro-tensorflow
- https://www.tensorflow.org/resources/learn-ml
- https://github.com/aymericdamien/TensorFlow-Examples
TensorFlow.js
- https://medium.com/codingthesmartway-com-blog/tensorflow-js-crash-course-machine-learning-for-the-we

HTTPS://I.IMGUR.COM/1ARAF4I.JPG
HTTPS://PBS.TWIMG.COM/MEDIA/DRHP6OTW4AAI2MJ?FORMAT=JPG&NAME=360X360
HTTPS://MIRO.MEDIUM.COM/MAX/1600/1*8GMGAAKFDI-9OHY5CA93XQ.PNG
HTTPS://I.PINIMG.COM/ORIGINALS/C8/AF/54/C8AF5423369B41548D43E6EC2044841C.GIF
Image sources

Chatbots with TensorFlow.js Sentiment Analysis

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