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.
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.
