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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science FREE CHAPTER 2. Data Acquisition 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

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...
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