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Java for Data Science

Java for Data Science

By : Richard M. Reese, Reese
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Java for Data Science

Java for Data Science

By: Richard M. Reese, 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 (13 chapters)
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Sentiment analysis

Sentiment analysis involves the evaluation and classification of words based on their context, meaning, and emotional implications. Typically, if we were to look up a word in a dictionary we will find a meaning or definition for the word but, taken out of the context of a sentence, we may not be able to ascribe detailed and precise meaning to the word.

For example, the word toast could be defined as simply a slice of heated and browned bread. But in the context of the sentence He's toast!, the meaning changes completely. Sentiment analysis seeks to derive meanings of words based on their context and usage.

It is important to note that advanced sentiment analysis will expand beyond simple positive or negative classification and ascribe detailed emotional meaning to words. It is far simpler to classify words as positive or negative but far more useful to classify them as happy, furious, indifferent, or anxious.

This type of analysis falls into the category of effective...

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