You're reading from Natural Language Processing with AWS AI Services Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801812535

Length 508 pages

Edition 1st Edition

Languages

Processing

Tools

Amazon Textract

Concepts

Mobile Application Development

Authors (2):

Mona M

Premkumar Rangarajan

View More author details

Table of Contents (23) Chapters

Preface

1. Section 1:Introduction to AWS AI NLP Services

2. Chapter 1: NLP in the Business Context and Introduction to AWS AI Services FREE CHAPTER

3. Chapter 2: Introducing Amazon Textract

4. Chapter 3: Introducing Amazon Comprehend

5. Section 2: Using NLP to Accelerate Business Outcomes

6. Chapter 4: Automating Document Processing Workflows

7. Chapter 5: Creating NLP Search

8. Chapter 6: Using NLP to Improve Customer Service Efficiency

9. Chapter 7: Understanding the Voice of Your Customer Analytics

10. Chapter 8: Leveraging NLP to Monetize Your Media Content

11. Chapter 9: Extracting Metadata from Financial Documents

12. Chapter 10: Reducing Localization Costs with Machine Translation

13. Chapter 11: Using Chatbots for Querying Documents

14. Chapter 12: AI and NLP in Healthcare

15. Section 3: Improving NLP Models in Production

16. Chapter 13: Improving the Accuracy of Document Processing Workflows

17. Chapter 14: Auditing Named Entity Recognition Workflows

18. Chapter 15: Classifying Documents and Setting up Human in the Loop for Active Learning

19. Chapter 16: Improving the Accuracy of PDF Batch Processing

20. Chapter 17: Visualizing Insights from Handwritten Content

21. Chapter 18: Building Secure, Reliable, and Efficient NLP Solutions

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Setting up a Yelp review text analytics workflow

In this section, we will walk you through how you can build this out for a Yelp reviews dataset or any social media analytics dataset by following the steps using a Jupyter notebook and Python APIs:

Setting up to solve the use case
Walking through the solution using a Jupyter notebook

The setup steps will involve the steps to configure Identity and Access Management (IAM) roles and the walkthrough notebook will walk you through the architecture. So, let's get started.

Setting up to solve the use case

If you have not done so in the previous chapters, you will first have to create an Amazon SageMaker Jupyter notebook and set up IAM permissions for that notebook role to access the AWS services we will use in this notebook. After that, you will need to clone the GitHub repository (https://github.com/PacktPublishing/Natural-Language-Processing-with-AWS-AI-Services), go to the Chapter 07 folder, and open the chapter07...

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You're reading from Natural Language Processing with AWS AI Services Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend

Table of Contents (23) Chapters

Setting up a Yelp review text analytics workflow

Setting up to solve the use case

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Authors (2)