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Natural Language Processing with AWS AI Services

Natural Language Processing with AWS AI Services

By : M, Premkumar Rangarajan
5 (21)
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Natural Language Processing with AWS AI Services

Natural Language Processing with AWS AI Services

5 (21)
By: M, Premkumar Rangarajan

Overview of this book

Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production. To start with, you'll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic. Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.
Table of Contents (23 chapters)
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1
Section 1:Introduction to AWS AI NLP Services
5
Section 2: Using NLP to Accelerate Business Outcomes
15
Section 3: Improving NLP Models in Production

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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