The document discusses Amazon Comprehend, a natural language processing service that helps analyze and organize unstructured text. It provides examples of how Comprehend is used for applications like social media analytics, customer service, and food safety. Specifically, it describes how one company used Comprehend to analyze social media data to identify potential food safety issues.

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Nino Bice
Product, Amazon Comprehend
Ben Snively
Principal Service Architect, AWS
BDA 310
Building Advanced Text Analytics Apps

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What’s the unstructured analysis opportunity?
• 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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Amazon Comprehend
A fully managed and continuously trained service that
helps you analyze and organize unstructured text

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Brief Comprehend Overview

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Davis Addy
IT Leader, Food Safety
Chick-fil-A
Chick-fil-A Tech Websites
• medium.com/@cfatechblog
• github.com/chick-fil-a
linkedin.com/in/davisaddy

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Social Media’s Impact to Food Safety
• Public data, widely consumed
• Becoming a more common
customer feedback channel
• Low value as a single data point
• Better value when associated with
other food safety datasets

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The Need for Natural Language Processing (NLP)
GOAL: Develop a process to identify key words, phrases, and
customer sentiment from social media data to help spot potential
food safety related issues.
Overall solution must be:
• Agile
• Scalable
• Cost effective
• Accuracy continuously improves

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Food Safety Text Mining Powered by Amazon Comprehend
<bucket>/raw <bucket>/filtered <bucket>/enriched
Amazon Athena
Amazon
Comprehend
Social
Data
DATA FLOW
1. Ingest review data
2. Apply buzzword filter
3. Extract key text
4. Check sentiment
5. Smart sort (custom logic)
6. Trigger business events2
5
4
6
1
3

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Benefits of Using Amazon Comprehend
• Ease of Use: API easily integrates into
Lambda functions
• Sentiment Analysis: improved issue
identification accuracy
by 30%
• Intelligent: NLP accuracy improves
overtime via machine learning

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Amazon Comprehend
AWS Text Analytics

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Popular Text Analytics Use Cases
Content Personalization: Customers are using Comprehend NLP output to understand related documents
based on entities, phrases, or even topic similarities for trends analysis, to drive content personalization and
recommendations
Semantic Search: Customers using Amazon Comprehend to index entities for boosting and ranking
search results.
Intelligent data warehouse: Customers are using Amazon Comprehend to query unstructured data in
relational databases, processing data within the data lake (Amazon S3) and then inserting it back into the
data warehouse
Social Analytics: Customers are using Amazon Comprehend to ingest, process, and analyze trends from
entities and sentiment from social media posts across Twitter and Facebook.

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

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Intelligent DW/DB
Comprehend and Amazon RDS

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Demo

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Content Personalization and
Recommendation
Comprehend and Neptune

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Demo

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Semantic Search
Comprehend and Elasticsearch

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Demo

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Submit session feedback
1. Tap the Schedule icon.
2. Select the session you attended.
3. Tap Session Evaluation to submit your
feedback.

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