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Build Text Analytics Solutions with Amazon Comprehend and Amazon Translate

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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.

Level: 200-300

Speaker: Gopal Wunnava - Principal Solutions Architect, AWS

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Build Text Analytics Solutions with Amazon Comprehend and Amazon Translate

  1. 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pop-up Loft Build Text Analytics Solutions with AWS ML Services
  2. 2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The AWS Machine Learning Stack AI SERVICES ML PLATFORMS ML FRAMEWORKS VISION Rekognition Video Rekognition SPEECH TranscribePolly LANGUAGE ComprehendTranslate CHATBOTS Lex AWS DeepLensAmazon SageMaker TensorFlow MXNet PyTorch Caffe2 Chainer Horvod Gluon Keras Mechanical Turk
  3. 3. Build Text Analytics Solutions with AWS ML Services
  4. 4. What are we going to do today? Easily build a system that collects, translates, understands, and visualizes twitter information based on your terms Data Catalog Data Lake • Capture Tweets in real-time • Machine Translation • Entity and KeyPhrase • Sentiment Analysis • Durably Save results
  5. 5. Amazon Kinesis Firehose <bucket>/enriche d <bucket>/raw Amazon Translate Amazon Comprehend Amazon Athena Amazon QuickSight AWS Glue Data Catalog Reference Architecture The diagram shows both the ingest (blue) and query (yellow) flows
  6. 6. Collection/Storage of Real-time Data Amazon Kinesis Firehose Amazon S3 <bucket>/raw Twitter Reader EC2 ECS Fargate
  7. 7. Amazon Kinesis – Real-Time Analytics Easily collect, process, and analyze video and data streams in real time Capture, process, and store video streams for analytics Load data streams into AWS data stores Analyze data streams with SQL Build custom applications that analyze data streams Kinesis Video Streams Kinesis Data Streams Kinesis Data Firehose Kinesis Data Analytics
  8. 8. Processing the tweets Amazon S3 <bucket>/raw Process New Tweets Amazon Translate Amazon Comprehend Amazon Kinesis Firehose <bucket>/enriched
  9. 9. Amazon Comprehend A fully managed and continuously trained service that helps you extract insights from unstructured text. Sentiment Entities LanguagesKey phrases Topic modeling
  10. 10. Another Example:
  11. 11. Amazon Translate A fully managed and continuously trained neural machine translation service that translates text from one language to another English <> Arabic, Simplified Chinese, French, German, Spanish and Portuguese Language detection via Amazon Comprehend AWS Security Standards Available in US East (N, Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) Easy to use and integrate via CLI or SDKs Best in class return on investment Translated text input tagged inputs, e.g. HTML Real-time translation of ~30 words, i.e. <500ms
  12. 12. Easy to Use & Integrate • Use through Command Line Tools or AWS SDKs • Integrate into your application or call externally • Python example: import boto3 Amazon.translate = boto3.client(service_name=‘translate') result = Amazon.translate(Text="Hello, World", SourceLanguageCode="en", TargetLanguageCode="de") print('TranslatedText: ' + result.get('TranslatedText'))
  13. 13. Querying our Data Lake Amazon S3 <bucket>/raw <bucket>/enriched AWS Glue Data Catalog Describes Amazon Athena SQL Uses Queries
  14. 14. Building a dashboard Amazon S3 <bucket>/raw <bucket>/enriche d Amazon Athena SQL Queries Amazon QuickSight SPICE
  15. 15. Demo Build a Social Media Dashboard
  16. 16. Amazon Kinesis Firehose <bucket>/enriche d <bucket>/raw Amazon Translate Amazon Comprehend Amazon Athena Amazon QuickSight AWS Glue Data Catalog Recap The diagram shows both the ingest (blue) and query (yellow) flows
  17. 17. Resources The AWS CloudFormation template will create all the ingestion components shown in this session, except for the Amazon S3 notification for AWS Lambda (depicted as the dotted blue line). In the AWS Management Console, launch the CloudFormation Template. Refer to the blog more details: https://aws.amazon.com/blogs/machine- learning/build-a-social-media-dashboard-using-machine-learning-and-bi-services/ Webinar: https://www.youtube.com/watch?v=wwOQvjogc7Q Additional questions about the demo? Reach to snivelyb@amazon.com
  18. 18. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pop-up Loft aws.amazon.com/activate Everything and Anything Startups Need to Get Started on AWS

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