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, and 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.

Topics and features:

Presents a comprehensive, practical and easy-to-read introduction to text mining
Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter
Explores the application and utility of each method, as well as the optimum techniques for specific scenarios
Provides several descriptive case studies that take readers from problem description to systems deployment in the real world
Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English)
Contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.

Autorentext

Dr. Sholom M. Weiss is a Professor Emeritus of Computer Science at Rutgers University, a Fellow of the Association for the Advancement of Artificial Intelligence, and co-founder of AI Data-Miner LLC, New York.

Dr. Nitin Indurkhya is faculty member at the School of Computer Science and Engineering, University of New South Wales, Australia, and the Institute of Statistical Education, Arlington, VA, USA. He is also a co-founder of AI Data-Miner LLC, New York.

Dr. Tong Zhang is a Professor of Statistics and Biostatistics at Rutgers University.

Inhalt

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