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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.

Fundamentals of Predictive Text Mining

Overview of Text Mining

From Textual Information to Numerical Vectors

Using Text for Prediction

Information Retrieval and Text Mining

Finding Structure in a Document Collection

Looking for Information in Documents

Data Sources for Prediction: Databases, Hybrid Data and the Web

Case Studies

Emerging Directions