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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

Defining the purpose and scope of our application

The application will prompt the user for a set of selection criteria, which include topic and sub-topic areas, and the number of tweets to process. The analysis performed will simply compute and display the number of positive and negative tweets for a topic and sub-topic. We used a generic sentiment analysis model, which will affect the quality of the sentiment analysis. However, other models and more analysis can be added.

We will use a Java 8 stream to structure the processing of tweet data. It is a stream of TweetHandler objects, as we will describe shortly.

We use several classes in this application. They are summarized here:

  • TweetHandler: This class holds the raw tweet text and specific fields needed for the processing including the actual tweet, username, and similar attributes.
  • TwitterStream: This is used to acquire the application's data. Using a specific class separates the acquisition of the data from its processing. The class...
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