Fundamentals of Predictive Text Mining (Texts in Computer Science) 2nd Edition

This acclaimed textbook on predictive text mining offers a cohesive and comprehensive overview of a dynamic and fast-growing field. Bridging data science, machine learning, databases, and computational linguistics, it presents an integrated approach that is both theoretical and hands-on. Serving as both an introduction and a practical guide, the book offers expert insights supported by real-world examples and case studies.

Now in its highly anticipated second edition, the text has been extensively revised and expanded to include new material on deep learning, graph-based models, social media mining, evaluation challenges in big data, Twitter sentiment analysis, and dependency parsing. Core topics such as document classification, information retrieval, clustering, information extraction, web-based data sourcing, prediction, and evaluation have all been thoroughly updated.

Key Features:

• Chapter summaries and exercises to reinforce learning

• Practical application of methods with clear, real-world examples

• Multiple in-depth case studies

• Guidance on avoiding common pitfalls in big data analysis

• Access to free, open-source text mining tools

Ideal for students and professionals alike, this text provides the foundational knowledge and tools needed to succeed in predictive text mining across a wide range of industries.