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

1. Amazon Comprehend: The AWS Text Analytics Workload
Niranjan Hira – AWS Solutions Architect

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AWS ML Stack
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex

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Natural Language Data
• News Media
• Brand trends, correlating events
• Customer engagement
• Call center, issue triage, social media analytics
• Records and research
• Actionable document-centric processes, understand patterns

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Text Analytics on AWS
• AWS Platform
• Amazon S3 Data Lake
• Solution integration
• Amazon Data

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Training NLP is Hard and Expensive
NLP Model
Data Collection
and Prep
Training the
model
Data annotation

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Amazon Comprehend: Natural Language
Processing
😃
Sentiment Entities Languages Key phrases Topic modeling
POWERED BY DEEP
LEARNING

7. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A m a z o n . c o m , I n c . i s l o c a t e d
i n S e a t t l e , W A a n d w a s
f o u n d e d J u l y 5 t h , 1 9 9 4 b y
J e f f B e z o s . O u r c u s t o m e r s
l o v e b u y i n g e v e r y t h i n g f r o m
b o o k s t o b l e n d e r s a t g r e a t
p r i c e s .
N a m e d E n t i t i e s
• A m a z o n . c o m : O r g a n i z a t i o n
• S e a t t l e , W A : L o c a t i o n
• J u l y 5 t h , 1 9 9 4 : D a t e
• J e f f B e z o s : P e r s o n
K e y p h r a s e s
• O u r c u s t o m e r s
• b o o k s
• b l e n d e r s
• g r e a t p r i c e s
S e n t i m e n t
• P o s i t i v e
L a n g u a g e
• E n g l i s h
Text Analysis

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Topic Modeling
Document Topic Proportion
Doc.txt 0 .89
Doc.txt 1 .67
Doc.txt 2 .91
Topic Term Weight
0 Washington .89
1 Silicon Valley .67
2 Roasting .91
Keywords Topic Groups Document Relationship to Topics

9. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Most Common Patterns
Content Personalization: Customers are using Comprehend to analyze content and look for trends and
relationships based on entities, phrases or even topic similarities. These capabilities enable content
personalization and recommendation use cases.
Semantic Search: Customers using Amazon Comprehend to index entities and keyphrases, boosting
and ranking search results.
Intelligent data warehouse: Customers are using Amazon Comprehend to query unstructured data in
relational databases, process that data with Comprehend via remote call, then insert them back into the
data warehouse to join and understand trends.
Social Analytics: Customers are using Amazon Comprehend to ingest, process and analyze feedback and
comments from social media posts across Twitter and Facebook.
Information management: Customers are using Amazon Comprehend for analyzing and discovering
related content for enterprise information management, topical organization and support for internal
business processes like compliance.

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Comprehend + AWS = Scale Text Analytics
Amazon Kinesis
Amazon ES
Amazon Redshift
Amazon EMR
• Semantic
• Rich Filtering
• Grouping, Trends
• Joining, Correlating
• Clustering
• Graph, Search
• Near real-time
• Alerts
Amazon S3
Articles, Documents
Social Media, Support
Amazon Aurora

11. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Twitter
Stream API
Amazon
Kinesis
Amazon
S3
Amazon
Athena
Analyze social media postings and comments to organize and classify customer
feedback and look for common patterns.
Visualize results in Amazon QuickSight

12. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Analyzing and joining unstructured text within relational databases. Customer
support and social analytics all within a database.

13. “What do people say about… ?”