Machine Learning Workshops at the San Francisco Loft Build Text Analytics Solutions with Amazon Comprehend and Amazon Translate Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker. Level: 200-300 Speaker: Ben Snively - Principal Solutions Architect, Data & Analytics, AWS

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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Build Text Analytics Solutions with AWS ML Services
Ben Snively
Principal Solutions Architect

2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The AWS Machine Learning Stack
AI SERVICES
ML PLATFORMS
ML FRAMEWORKS
VISION
Rekognition
Video
Rekognition
SPEECH
TranscribePolly
LANGUAGE
ComprehendTranslate
CHATBOTS
Lex
AWS DeepLensAmazon SageMaker
TensorFlow MXNet PyTorch Caffe2 Chainer Horvod Gluon Keras
Mechanical Turk

3. Build Text Analytics Solutions with AWS ML Services

4. What are we going to do today?
Easily build a system that collects, translates, understands, and visualizes twitter
information based on your terms
Data
Catalog
Data Lake
• Capture Tweets in real-time
• Machine Translation
• Entity and KeyPhrase
• Sentiment Analysis
• Durably Save results

5. Amazon
Kinesis Firehose
<bucket>/enriche
d
<bucket>/raw
Amazon Translate
Amazon Comprehend
Amazon
Athena
Amazon
QuickSight
AWS Glue
Data Catalog
Reference Architecture
The diagram shows both the ingest (blue) and query (yellow) flows

6. Collection/Storage of Real-time Data
Amazon
Kinesis
Firehose
Amazon
S3
<bucket>/raw
Twitter
Reader
EC2
ECS
Fargate

7. Amazon Kinesis – Real-Time Analytics
Easily collect, process, and analyze video and data streams in real time
Capture, process, and
store video streams
for analytics
Load data streams
into AWS data stores
Analyze data streams
with SQL
Build custom
applications that
analyze data streams
Kinesis Video Streams Kinesis Data Streams Kinesis Data Firehose Kinesis Data Analytics

8. Processing the tweets
Amazon
S3
<bucket>/raw
Process New
Tweets
Amazon Translate
Amazon Comprehend
Amazon
Kinesis Firehose
<bucket>/enriched

9. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Comprehend
Di scover i nsi ghts and rel ati onshi ps i n text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend
Syntax

10. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Amazon.com, Inc. is located in Seattle,
WA and was founded July 5th, 1994
by Jeff Bezos. Our customers love
buying everything from books to
blenders at great prices
• Named Entities
– Amazon.com: Organization
– Seattle, WA : Location
– July 5th,1994: Date
– Jeff Bezos : Person
• Keyphrases
– Our customers
– books
– blenders
– great prices
• Sentiment
– Positive
• Language
– English
Text Analysis

11. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Comprehend
STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEWS ARTICLES
Amazon
Comprehend
Di scover i nsi ghts and rel ati onshi ps i n text

12. Another Example:

13. Amazon Translate
A fully managed and continuously trained neural machine translation service that
translates text from one language to another
English <> Arabic, Simplified
Chinese, French, German,
Spanish and Portuguese
Language detection via
Amazon Comprehend
AWS Security Standards Available in US East (N,
Virginia), US East (Ohio),
US West (Oregon), and EU
(Ireland)
Easy to use and integrate via
CLI or SDKs
Best in class return on
investment
Translated text input
tagged inputs, e.g. HTML
Real-time translation of
~30 words, i.e. <500ms

14. Easy to Use & Integrate
• Use through Command Line Tools or AWS SDKs
• Integrate into your application or call externally
• Python example:
import boto3
Amazon.translate = boto3.client(service_name=‘translate')
result = Amazon.translate(Text="Hello, World",
SourceLanguageCode="en", TargetLanguageCode="de")
print('TranslatedText: ' + result.get('TranslatedText'))

15. Querying our Data Lake
Amazon
S3
<bucket>/raw
<bucket>/enriched
AWS Glue Data
Catalog
Describes
Amazon
Athena
SQL
Uses
Queries

16. Building a dashboard
Amazon
S3
<bucket>/raw
<bucket>/enriche
d
Amazon
Athena
SQL
Queries
Amazon
QuickSight
SPICE

17. Demo
Build a Social Media Dashboard

18. Amazon
Kinesis Firehose
<bucket>/enriche
d
<bucket>/raw
Amazon Translate
Amazon Comprehend
Amazon
Athena
Amazon
QuickSight
AWS Glue
Data Catalog
Recap
The diagram shows both the ingest (blue) and query (yellow) flows

19. Resources
The AWS CloudFormation template will create all the ingestion components shown
in this session, except for the Amazon S3 notification for AWS Lambda (depicted as
the dotted blue line). In the AWS Management Console, launch the CloudFormation
Template. Refer to the blog more details: https://aws.amazon.com/blogs/machine-
learning/build-a-social-media-dashboard-using-machine-learning-and-bi-services/
Webinar: https://www.youtube.com/watch?v=wwOQvjogc7Q
Additional questions about the demo? Reach to snivelyb@amazon.com

21. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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