Book Image

Java Data Science Cookbook

By : Rushdi Shams
Book Image

Java Data Science Cookbook

By: Rushdi Shams

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (16 chapters)
Java Data Science Cookbook
About the Author
About the Reviewer
Customer Feedback

Extracting topics from text documents using Mallet

With an ever-increasing amount of documents in text format nowadays, an important task for any data scientist is to get an overview of a large number of articles with abstracts, summaries, or a list of abstract topics, not because this saves time to read through the articles but to do clustering, classification, semantic relatedness measurement, sentiment analysis, and so on.

In machine learning and natural language processing domain, topic modelling refers to retrieving abstract topics or keywords from text articles using statistical models. In this recipe, we will be using a sophisticated Java-based machine learning and natural language processing library named Mallet, which is an acronym for Machine Learning for Language Toolkit (see Mallet is widely used in the academia as well as in the industry for the following:

  • document classification,

  • clustering,

  • topic modelling, and

  • information extraction.

However, the scope...