The document discusses using natural language processing (NLP) techniques to gain customer insights from unstructured text data. It describes several Amazon NLP services like Amazon Comprehend, Amazon Transcribe, Amazon Translate, and Amazon Polly that can be used to extract entities, key phrases, sentiment and topics from text. It also discusses how these services can be combined with Amazon SageMaker and Amazon ML services to build custom classifiers and analyze customer calls to improve customer experience.

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Improving Customer Experience:
Enhanced Customer Insights Using Natural Language
Processing
Joyjeet Banerjee
Enterprise Solutions Architect
AWS
P U T 3 0 1 - i
Vebhhav Singh
Sr. Solutions Architect
AWS

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Agenda
• NLP Application Services
• Amazon SageMaker – Building Custom Models
• Discovering relationship in text
• Demonstration

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© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning at Amazon: A long heritage
Voice driven
interactions
Fulfillment automation
& inventory management
Personalized
recommendations
Drones
Inventing entirely new
customer experiences

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Tens of thousands of customers running Machine Learning on AWS

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M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N
V I D E O
Vision Speech Language Chatbots &
Contact Centers
M L S E R V I C E S
A M A Z O N
S A G E M A K E R
A M A Z O N E C 2
C 5 I n s t a n c e s
A M A Z O N E C 2
P 3 I n s t a n c e s
F P G A s
Frameworks Interfaces
Infrastructure
TheAmazon Machine Learning stack: Broadest & deepest set of capabilities

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How do you extract insights
from unstructured text?

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Amazon Comprehend
Natural Language Processing
Sentiment Entities LanguagesKey phrases Topic modeling

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Amazon Comprehend
Extract insights from text
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

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Amazon Comprehend
Topic modeling
Fully managed
topic modeling
service
Extract up to 100 topics
from a corpus of
documents
Automatically organizes
documents into the
topics

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Amazon Comprehend
Use cases
Voice of Customer
Analytics
Semantic Search Knowledge Management/Discovery
Analyzing what customers are saying about
your brand, products and services
Making search smarter by searching on
key phrase, sentiment and topic
Organizing documents, categorizing by
topic and personalizing experiences

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Amazon Comprehend – Custom classification
• Analyze and organize unstructured text
• Text can be classified into various categories
• Train your model on the fly – Uses AutoML
• Creates a custom classifier specific to your data
• No machine learning experience required
• Support organizations can automatically categorize support calls by
problem type
• Direct your incoming call to the right person
• Reducing time to reach to the right source

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Transcribe
Automatic speech recognition
Time
stamps
Support for
both regular &
telephony audio
Punctuation
&
formatting
Amazon S3
integration
Recognize
multiple
speakers
Custom
dictionaries
? !

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Amazon Transcribe
Use cases
Call
centers
Subtitles for
VOD
Transcribe meetingsBroadcast closed
captions

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Amazon Translate
Neural machine translation
Real time
translation
Powered by deep
learning
12 language pairs
with more to come
Language
detection

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Amazon Polly
Use cases
Content creation
Mobile & desktop applications
Internet of things (IoT)
Education & e-learning
Telephony
Game development
Key features
52 voices across 25 languages
Lip-syncing & text highlighting
Fine-grained voice control
Custom vocabularies
Available in 14 AWS regions

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Amazon ML Services
Bringing it together: Customer call analysis
Amazon
QuickSight
Twitter Stream
API
Kinesis Translate
Lambda
Transcribe
• Transcription of the call
• Topic being discussed the most, for
example - price
• Drill down into specific time in the
call by topics
• Key words which are improving the
Sales – “Demo”
• Agent talk time % - Improved or
not?

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Amazon SageMaker
Fully managed
hosting with
auto-scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
B U IL D TRA IN & TU N E DEPL O Y
Build, train, tune, and host your own models
Automatic
Model Tuning
End-to-end-encryption
End-to-end VPC support Metadata and experiment management capabilities
Pay as you goCompliance and audit capabilities

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Amazon SageMaker
Collect and
prepare
training data
Choose and
optimize
your ML algorithm
Set up and manage
environments for
training
Train and tune
model
(iterate, iterate)
Scale and manage
the production
environment
Deploy model
in production

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Discovering relationships between words

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Word2Vec
• Search engines tell you about soccer or Barcelona when you search for Messi
• Popular algorithm used for generating dense vector representations of words in large corpora by
using unsupervised learning
• Creating representations for words that capture their meanings, semantic relationships, and the
different types of contexts they’re used in
• The resulting vectors have been shown to capture semantic relationships between the
corresponding words
• The vectors are used extensively for many downstream natural language processing (NLP)
tasks like sentiment analysis, named entity recognition, and machine translation

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Algorithm
• Word2Vec is based on probability of the words occurring
given a condition
I eat an apple every day.
I eat an orange every day.
I like driving my car to work.

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Word2Vec

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Linguistic regularities
Word vector space implicitly encodes many regularities among words

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Dataset

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Linguistic regularities (Wikipedia dataset)

28. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Joyjeet Banerjee (joyjeetb@amazon.com)
Vebhhav Singh (vebhhavs@amazon.com)

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