Consumers today freely express their satisfaction or frustration with a company or product online through social media, blogs, and review platforms. Sentiment analysis can help companies better understand their customers' opinions and needs, and make more informed business decisions. In this workshop, learn how to use Amazon Comprehend to analyze sentiment. Also learn how to build a serverless data processing pipeline that consumes raw Amazon product reviews from Amazon S3, cleans the dataset, extracts sentiment from each review, and writes the output back to Amazon S3.

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Build An ETL Pipeline To Analyze
Customer Data
Jean-Pierre Dodel
Product Manager – Amazon Comprehend
AWS
A I M 4 1 6
Roy Hasson
Manager, Global Business Development – Analytics
AWS

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Agenda
Introduction to Amazon Comprehend
Introduction to AWS Glue
Workshop use case and architecture
Getting started

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Amazon Comprehend
D i s c o v e r i n s i g h t s a n d r e l a t i o n s h i p s i n t e x t
WORLD SERIES WASHINGTON
Entities
Key phrases
Language
Sentiment
Syntax
STORM STOCK MARKET
LIBRARY OF
NEWS ARTICLES

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Amazon Comprehend
Amazon.com, Inc. is located in
Seattle, WA and was founded
July 5, 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 5,1994: Date
- Jeff Bezos: Person
Key phrases
- our customers
- books
- blenders
- great prices
Sentiment
Positive
Language
English
E x t r a c t i n g i n s i g h t f r o m t e x t

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Amazon Comprehend
U s e c a s e s
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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Automatically discover and categorize your data,
making it immediately searchable and queryable
across data sources
Generate code to clean, enrich, and reliably move data
between various data sources. Easily customize this
code or bring your own.
Run your jobs on a serverless, fully managed, scale-
out environment. No compute resources to provision
or manage.
Discover
Develop
Deploy
AWS Glue enables you to …

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What is AWS Glue Data Catalog?
Unified metadata repository across relational databases, Amazon Relational
Database Service (Amazon RDS), Amazon Redshift, and Amazon Simple Storage
Service (Amazon S3) … with support for more coming soon!
Single searchable view into your data, no matter where it is stored
Ability to automatically crawl and classify your data
Augment technical metadata with business metadata for tables
Track data evolution using schema versioning
Apache Hive metastore compatible and integrated with AWS analytics services

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What is a crawler ?
Crawlers automatically build your AWS Glue Data Catalog and keep it in sync
Scan your data stored in various data stores, extract metadata and data
statistics, and add table definitions to your Data Catalog
• Classify data using built-in and custom classifiers
• You can write your own using Grok expressions
Discover new data, extracts schema definitions
• Detect schema changes and version tables
• Detect Apache Hive style partitions on Amazon S3
Run ad hoc or on a schedule; serverless – only pay when crawler runs

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What is AWS Glue ETL ?
1.Customize the mappings
2.AWS Glue generates transformation graph and Python or Scala code
3.Connect your notebook to development endpoints to customize your code

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AWS Glue utilizes the power of Apache Spark
Spark core: RDDs
SparkSQL
DataFrames Dynamic Frames
AWS Glue ETL

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Workshop use case
• Ingest Amazon product reviews
• Perform NLP using Amazon Comprehend & AWS Glue
• Sentiment analysis
• Entity recognition
• Key phrase extraction
• Language detection
• Enrich product reviews dataset
• Query and visualize using Amazon Athena & Amazon QuickSight

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Workshop architecture
Amazon
Reviews Dataset
Data CatalogAWS Glue Crawler
1
2
3
4
5
AWS Glue Crawler
6
7

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Getting started
1. Configure AWS Glue crawler: s3://amazon-reviews-pds/parquet/
2. Query crawled table in Amazon Athena
3. Create AWS Glue ETL job from the console. Select “A new script to be
authored by you.”
4. Once job is created, copy/paste PySpark script from
https://github.com/rhasson/reinvent2018_aim416
5. Follow instructions in the script to complete each section

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Bonus–Predict future sentiment
1. Use Amazon Comprehend to add sentiment labels to review dataset
2. Select relevant features (columns)
3. Select possible computed features
4. Split data into train & test
5. Save train & test datasets to Amazon S3 in CSV format
6. Experiment and build your model using Amazon SageMaker
7. Profit!

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20. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Jean-Pierre Dodel
Roy Hasson