Book Image

Java for Data Science

By : Richard M. Reese, Jennifer L. Reese
Book Image

Java for Data Science

By: Richard M. Reese, Jennifer L. Reese

Overview of this book

para 1: Get the lowdown on Java and explore big data analytics with Java for Data Science. Packed with examples and data science principles, this book uncovers the techniques & Java tools supporting data science and machine learning. Para 2: The stability and power of Java combines with key data science concepts for effective exploration of data. By working with Java APIs and techniques, this data science book allows you to build applications and use analysis techniques centred on machine learning. Para 3: Java for Data Science gives you the understanding you need to examine the techniques and Java tools supporting big data analytics. These Java-based approaches allow you to tackle data mining and statistical analysis in detail. Deep learning and Java data mining are also featured, so you can explore and analyse data effectively, and build intelligent applications using machine learning. para 4: What?s Inside ? Understand data science principles with Java support ? Discover machine learning and deep learning essentials ? Explore data science problems with Java-based solutions
Table of Contents (19 chapters)
Java for Data Science
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Chapter 9. Text Analysis

Text analysis is a broad topic and is typically referred to as Natural Language Processing (NLP). It is used for many different tasks, including text searching, language translation, sentiment analysis, speech recognition, and classification, to mention a few. The process of analyzing can be difficult due to the particularities and ambiguity found in natural languages. However, there has been a considerable amount of work in this area and there are several Java APIs supporting this effort.

We will start with an introduction to the basic concepts and tasks used in NLP. These include the following:

  • Tokenization: The process of splitting text into individual tokens or words.

  • Stop words: These are words that are common and may not be necessary for processing. They include such words as the, a, and to.

  • Name Entity Recognition (NER): This is the process of identifying elements of text such as people's name, locations, or things.

  • Parts of Speech (POS): This identifies the...