Search icon CANCEL
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Scala Machine Learning Projects

You're reading from  Scala Machine Learning Projects

Product type Book
Published in Jan 2018
Publisher Packt
ISBN-13 9781788479042
Pages 470 pages
Edition 1st Edition
Languages

Table of Contents (17) Chapters

Title Page
Packt Upsell
Contributors
Preface
1. Analyzing Insurance Severity Claims 2. Analyzing and Predicting Telecommunication Churn 3. High Frequency Bitcoin Price Prediction from Historical and Live Data 4. Population-Scale Clustering and Ethnicity Prediction 5. Topic Modeling - A Better Insight into Large-Scale Texts 6. Developing Model-based Movie Recommendation Engines 7. Options Trading Using Q-learning and Scala Play Framework 8. Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks 9. Fraud Analytics Using Autoencoders and Anomaly Detection 10. Human Activity Recognition using Recurrent Neural Networks 11. Image Classification using Convolutional Neural Networks 1. Other Books You May Enjoy Index

Topic modeling and text clustering


In TM, a topic is defined by a cluster of words, with each word in the cluster having a probability of occurrence for the given topic, and different topics having their respective clusters of words along with corresponding probabilities. Different topics may share some words, and a document can have more than one topic associated with it. So in short, we have a collection of text datasets—that is, a set of text files. Now the challenging part is finding useful patterns about the data using LDA.

There is a popular TM approach, based on LDA, where each document is considered a mixture of topics and each word in a document is considered randomly drawn from a document's topics. The topics are considered hidden and must be uncovered via analyzing joint distributions to compute the conditional distribution of hidden variables (topics), given the observed variables and words in documents. The TM technique is widely used in the task of mining text from a large collection...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €14.99/month. Cancel anytime}