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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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Toc

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

Understanding the application's architecture

Every application has its own unique structure, or architecture. This architecture provides the overarching organization or framework for the application. For this application, we combine the three classes using a Java 8 stream in the ApplicationDriver class. This class consists of three methods:

  • ApplicationDriver: Contains the applications' user input
  • performAnalysis: Performs the analysis
  • main: Creates the ApplicationDriver instance

The class structure is shown next. The three instance variables are used to control the processing:

public class ApplicationDriver { 
    private String topic; 
    private String subTopic; 
    private int numberOfTweets; 
 
    public ApplicationDriver() { ... } 
    public void performAnalysis() { ...     } 
 
    public static void main(String[] args) { 
        new ApplicationDriver(); 
    } 
} 

The ApplicationDriver constructor follows. A Scanner instance is created and the sentiment analysis model...

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