Book Image

Natural Language Processing with AWS AI Services

By : Mona M, Premkumar Rangarajan
Book Image

Natural Language Processing with AWS AI Services

By: Mona 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)
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 8: Leveraging NLP to Monetize Your Media Content

As we have seen in this book so far, AI, and specifically NLP, has a wide range of uses in areas hitherto considered traditional IT spurred on by the rapid proliferation of data and the democratization of machine learning (ML) with cloud computing. In the previous chapter, we saw a cool example of how you can bring color to social media reviews and other forms of textual data by running voice of the customer analytics with sentiment detection. We saw how you can use AWS Glue to crawl raw data from Amazon S3, use Amazon Athena to interactively query this data, transform the raw data using PySpark (http://spark.apache.org/docs/latest/api/python/index.html) in an AWS Glue job to call Amazon Comprehend APIs (which provide ready-made intelligence with pre-trained NLP models) to get sentiment analysis on the review, convert the data into Parquet, and partition it (https://docs.aws.amazon.com/athena/latest/ug/partitions.html) by sentiment...