Fundamentals of Predictive Text Mining, 2nd Edition

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.

Table of Contents

Chapter 1 Overview of Text Mining
Chapter 2 From Textual Information to Numerical Vectors
Chapter 3 Using Text for Prediction
Chapter 4 Information Retrieval and Text Mining
Chapter 5 Finding Structure in a Document Collection
Chapter 6 Looking for Information in Documents
Chapter 7 Data Sources for Prediction: Databases, Hybrid Data and the Web
Chapter 8 Case Studies
Chapter 9 Emerging Directions