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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

Chapter 5. Topic Modeling - A Better Insight into Large-Scale Texts

Topic modeling (TM) is a technique widely used in mining text from a large collection of documents. These topics can then be used to summarize and organize documents that include the topic terms and their relative weights. The dataset that will be used for this project is just in plain unstructured text format.

We will see how effectively we can use the Latent Dirichlet Allocation (LDA) algorithm for finding useful patterns in the data. We will compare other TM algorithms and the scalability power of LDA. In addition, we will utilize Natural Language Processing (NLP) libraries, such as Stanford NLP.

In a nutshell, we will learn the following topics throughout this end-to-end project:

  • Topic modelling and text clustering
  • How does LDA algorithm work?
  • Topic modeling with LDA, Spark MLlib, and Standard NLP
  • Other topic models and the scalability testing of LDA
  • Model deployment
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