Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data

Chapter 6. Naive Bayes and Sentiment Analysis

A few years back one of my friends and I built a forum where developers could post useful tips regarding the technology they were using. I wished I knew about the Naive Bayes machine learning algorithm then. It could have helped me to filter objectionable content that was posted on that forum. In the previous chapter, we saw two algorithms that can be used to predict continuous values or to classify between discrete sets of values. Both the approaches predicted a definite value (whether it was continuous or discrete), but they did not give us a probability of occurrences of our best guesses. Naive Bayes gives us the predicted results with a probability attached to it, so in a set of results for same category we can pick the one with the highest probability.

In this chapter, we will cover:

  • General concepts about probability and conditional probability. This section will be basic and users who already know this can skip this section.

  • We will cover...