Serverless Text Analytics with Amazon Comprehend
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The document discusses the importance and applications of text analytics using Amazon Comprehend, highlighting its capabilities in sentiment analysis, entity extraction, and topic modeling. It explains how various industries can leverage these features for tasks such as customer engagement, media analysis, and knowledge management. Personalization and social analytics are also emphasized as significant use cases for extracting insights from unstructured data.
Serverless Text Analytics with Amazon Comprehend
- 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Donnie Prakoso, MSc AWS Technology Evangelist, ASEAN Text Analytics Workload with Amazon Comprehend AWS User Group Meetup @donnieprakoso
- 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Hello, World. Donnie Prakoso, MSc AWS Technology Evangelist, ASEAN @donnieprakoso donnieprakoso • Speak in Go and Python • Machine Learning and Serverless • I AWS User Groups
- 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Let’s Discuss Something • Why do we need text analytics? • What are the examples of the use cases? • How Amazon Comprehend can help you? • Demo + API and architecture diagram example https://bit.ly/aws-donnie-serverless-text-analytics
- 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Why do we need text analytics?
- 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Enabling The Possibility for These Use Cases • News Media • Brand trends, correlating events • Customer engagement • Call center, issue triage, social media analytics • Records and research • Actionable document-centric processes, understand patterns
- 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Machine Learning Services
- 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. FRAMEWORKS AND INTERFACES AI for data scientists KERAS Frameworks Interfaces APPLICATION SERVICES AI for everyone P O L L Y R E K O G N I T I O N C O M P R E H E N DL E X R E K O G N I T I O N V I D E O T R A N S C R I B E T R A N S L A T E PLATFORM SERVICES AI for engineers AMAZON SAGEMAKER
- 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. G e t S t a r t e d w i t h A W S M L A p p l i c a t i o n S e r v i c e s POLLY REKOGNITION COMPREHENDLEX REKOGNITION VIDEO TRANSCRIBE TRANSLATE
- 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Sentiment Entities Languages Key phrases Topic modeling POWERED BY DEEP LEARNING Amazon Comprehend Discover insights and relationships in text
- 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Entities Key Phrases Language Sentiment Amazon Comprehend Amazon Comprehend Discover insights and relationships in text
- 11. © 2017, 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
- 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Synchronous • DetectDominantLanguage and BatchDetectDominantLanguage – to detect the dominant language in a document. We can detect up to 100 languages. • DetectEntities and Batch DetectEntities – to detect the entities, such as persons or places, in the document. • DetectKeyPhrases and Batch DetectKeyPhrases – to detect key noun phrases that are most indicative of the content. • DetectSentiment and Batch DetectSentiment – to detect the emotional sentiment, positive, negative, mixed, or neutral, of a document. API Summary - Synchronous
- 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demo Twitter Sentiment Analysis w/ Amazon Comprehend
- 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Twitter Search API Analyze social media postings and comments AWS Lambda Amazon S3 AWS IoT Amazon DynamoDB
- 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Twitter Stream API Organize and classify customer feedback and look for common patterns. Visualize results in Amazon QuickSight Amazon Kinesis Amazon Athena Amazon S3 AWS Lambda
- 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Comprehend Customer Feedback Analytics
- 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Comprehend STORM WORLD SERIES STOCK MARKET WASHINGTON LIBRARY OF NEWS ARTICLES Amazon Comprehend Topic Modelling
- 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
- 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Asynchronous • StartTopicDetection – to start a topic modeling job • ListTopicDetection – to list all your submitted jobs • DescribeTopicDetection –to get progress status and information about each submitted job API Summary - Asynchronous
- 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demo Classifying Customer Feedback
- 21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Comprehend + AWS = Scale Text Analytics Amazon Kinesis Amazon EMR 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
- 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Comprehend Knowledge Management and Discovery
- 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Ideas Are Welcome 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 (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. Information management: Customers are using Amazon Comprehend for indexing and finding related content for enterprise information management and various internal business processes including compliance and IT.
- 24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Lex Scale Business Logic SecurityAnalytics Text to Speech Speech to Intent End to End Native support & maintains context One-click deployment Completely managed service Native integration with AWS Lambda Encrypted data in transit & at rest Monitor and improve Amazon Polly integrated into API ASR + NLU integrated into one API Dialog Management Deployment
- 25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Lex Informational Bots Chatbots for everyday consumer requests Application Bots Build powerful interfaces to mobile applications News updates Weather information Game scores …. Book tickets Order food Manage bank accounts …. Enterprise Productivity Bots Streamline enterprise work activities and improve efficiencies Check sales numbers Marketing performance Inventory status …. Internet of Things (IoT) Bots Enable conversational interfaces for device interactions Wearables Appliances Auto …. Contact Center Bots Chatbots for customer service IVR Account inquiries Bill payment Service update …. Use cases
- 26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Intents A particular goal that the user wants to achieve Utterances Spoken or typed phrases that invoke your intent Slots Data the user must provide to fulfill the intent Prompts Questions that ask the user to input data Fulfillment The business logic required to fulfill the user’s intent BookHotel
- 27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Lex Customers
- 28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Build On! Donnie Prakoso @donnieprakoso