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

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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

22. Other Books You May Enjoy

Chapter 7: Understanding the Voice of Your Customer Analytics

In the previous chapters, to see improving customer service in action, we built an AI solution that uses the AWS NLP service Amazon Comprehend to first analyze historical customer service records to derive key topics using Amazon Comprehend Topic Modeling and train a custom classification model that will predict routing topics for call routing using Amazon Comprehend Custom Classification. Finally, we used Amazon Comprehend detect sentiment to understand the emotional aspect of the customer feedback.

In this chapter, we are going to focus more on the emotional aspect of the customer feedback, which could be an Instagrammer, Yelp reviewer, or your aunt posting comments about your business on Facebook, and so on and so forth.

Twenty years back, it was extremely tough to find out as soon as possible what people felt about your products and get meaningful feedback to improve them. With globalization and the invention...

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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

Chapter 7: Understanding the Voice of Your Customer Analytics

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Authors (2)