This document describes building a real-time sentiment analysis dashboard using Twitter data streamed to AWS Kinesis. A Node.js application acts as a producer, streaming English tweets to a Kinesis stream. A consumer pulls the tweet data from the stream and calculates sentiment scores, which are returned to a front-end dashboard using Rickshaw graphs to display the sentiment over time. The architecture involves one producer, one Kinesis stream with one shard, and one consumer dashboard application.

1. Real Time Sentiment Analysis
Using Twitter Stream API & AWS
Kinesis
Armando Padilla
mandopadilla81@gmail.com

2. A little about me
● NodeJS & AWS enthusiast
● 15 years building technology solutions
● 4 years leading and building distributed and
colocated teams
● BS/MS in Computer Science
● Father & Husband :-)

3. So, what are we building?

4. Real Time Sentiment Dashboard using english Tweets.

5. Architecture & Tech

6. 1 Producer - Takes in Twitter Stream data and sends it to Kinesis.
1 Kinesis Stream - 1 shard to handle incoming flow of data.
1 Consumer - Dashboard.

7. Tech
Twitter Stream API AWS Kinesis Streams NodeJS
Rickshaw (Graphs Lib), sentiment (npm package)

8. Let’s get to it!

9. Producer
A producer injects data into a Kinesis
Stream.
2 ways to inject data.
1. PutRecord - Single use .
2. PutRecords. - Batched Injection.
const AWS = require('aws-sdk');
const kinesis = new AWS.Kinesis({
accessKeyId: 'ACCESS_KEY',
secretAccessKey: 'SECRET_KEY',
region: 'us-east-1'
});
const params = {
Data: STRING_TO_SAVE,
StreamName: AWS_STREAM_NAME,
PartitionKey: PARTITION_KEY_OF_YOUR_CHOSING,
}
kinesis.putRecord(params, function (err, data) {
if (err) throw err;
console.log("data", data); // For login.
})

10. Twitter Stream Integration
Continues random stream of live
tweets.
API: statuses/sample
Filters: language=en
const Twitter = require('twitter');
const AWS = require('aws-sdk');
const client = new Twitter({}) //See docs for props to use.
client.stream('statuses/sample', {language: 'en'}, function(stream){
stream.on('data', function(event){
const text = event.text;
// Producer Slide Content Here
})
stream.on('error', function(e){
throw e;
})
})

11. Kinesis Streams
Collects and processes large streams of
data in real time.
Shards
● Supports 1MB/Sec
● 1000 writes/sec
● 2MB/sec Reads

12. Consumer
Reads data from a stream.
Our example the consumer is an endpoint used by the front-end graph which pulls
data and calculates sentiment.
2 steps:
1. Fetch iterator
2. Fetch data

13. Consumer - cont.
Fetch Iterator
A shard iterator allows us to pull
data from the stream from a
specific point along the stream.
const AWS = require('aws-sdk');
const kinesis = new AWS.Kinesis({
accessKeyId: AWS_KINESIS_ACCESS_KEY_ID,
secretAccessKey: AWS_KINESIS_SECRET_ACCESS_KEY,
region: AWS_KINESIS_REGION,
});
//Fetch initial iterator.
var params = {
ShardId: SHARD_ID,
StreamName: AWS_KINESIS_STREAMNAME,
ShardIteratorType: 'LATEST'
}
kinesis.getShardIterator(params, (err, data) => {
if (err) return reject(err);
const iterator = data.ShardIterator;
}

14. Consumer - cont.
Fetch Data
With an iterator, fetch the data using
getRecords. Data needed is in the
Records property of the response.
If you want to start from this point in
the stream in the next call, the
NextShardIterator contains the iterator
to use.
params = { ShardIterator: shardIterator }
kinesis.getRecords(params, (err, data) => {
if (err) throw err;
if (data.Records) {
data.Records.forEach((record) => {
const content = record.Data.toString();
console.log(‘content’, content); // Loggin
});
}
// Get the next iterator.
if (!data.NextShardIterator) {
shardIterator = null;
} else {
shardIterator = data.NextShardIterator;
}

15. Consumer - Sentiment Add On
Calculate Sentiment - Naive
Implementation
Calculate the average sentiment for the
set of tweets returned.
Increment/Decrement by 1 since we want
to take 0 into account.
This value is returned to the Front-End
// Grab sentiment.
var avgMood = 0;
if (data.Records) {
data.Records.forEach((record) => {
const content = record.Data.toString();
var mood = sentiment(content).score;
if (mood >= 0) mood += 1;
if (mood < 0) mood = mood-1;
avgMood += mood;
});
}
if (data.Records.length != 0) avgMood = avgMood/data.Records.length;
// Get the next iterator code here
res.status(200).json({
nextIterator: shardIterator,
Mood: avgMood
})

16. Front End Dashboard
Init Rickshaw Graph
var timeInterval = 1000;
var url = "http://localhost:3000/data";
// instantiate our graph!
var graph = new Rickshaw.Graph({
element: document.getElementById("chart"),
width: 900,
height: 500,
renderer: 'line',
series: new Rickshaw.Series.FixedDuration([{ name: 'Mood', color: 'steelblue' }],
undefined, {
timeInterval: timeInterval,
maxDataPoints: 100,
}),
min: -10,
max: 10,
});
graph.render();
Make Up
1. Basic HTML
2. Rickshaw to build Graph
3. JQuery to make call to API

17. Front End Dashboard
Fetch Data every X seconds
// Fetch data every X seconds.
var nextIterator = null;
setInterval(function() {
$.getJSON(url+'?nextIterator='+encodeURIComponent(nextIterator), function(data) {
nextIterator = data.nextIterator;
graph.series.addData({
Mood: data.Mood
});
graph.render();
});
}, timeInterval);

18. Next Steps & Conclusion
1. Move the sentiment analysis to Kinesis Analytics.
2. Store Calculated sentiment scores to fetch historical data.
3. Update Sentiment Algo used.

19. References
App Code
https://github.com/armandopadilla/twitter_sentiment_public
Sentiment Package
https://www.npmjs.com/package/sentiment
AWS Kinesis Docs
https://aws.amazon.com/documentation/kinesis/
Rickshaw Graphs
http://code.shutterstock.com/rickshaw/