Book Image

Mastering Spark for Data Science

By : Andrew Morgan, Antoine Amend, Matthew Hallett, David George
Book Image

Mastering Spark for Data Science

By: Andrew Morgan, Antoine Amend, Matthew Hallett, David George

Overview of this book

Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Table of Contents (22 chapters)
Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Building a graph of persons


We previously used NLP entity recognition to identify persons from an HTML raw text format. In this chapter, we move to a lower level by trying to infer relations between these entities and detect the possible communities surrounding them.

Contact chaining

Within the context of news articles, we first need to ask ourselves a fundamental question. What defines a relation between two entities? The most elegant answer would probably be to study words using the Stanford NLP libraries described in Chapter 6, Scraping Link-Based External Data. Given the following input sentence, which is taken from http://www.ibtimes.co.uk/david-bowie-yoko-ono-says-starmans-death-has-left-big-empty-space-1545160:

"Yoko Ono said she and late husband John Lennon shared a close relationship with David Bowie"

We could easily extract the syntactic tree, a structure that linguists use to model how sentences are grammatically built and where each element is reported with its type such as a noun...