About this book
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.
- DOI https://doi.org/10.1007/978-1-4471-6750-1
- Copyright Information Springer-Verlag London 2015
- Publisher Name Springer, London
- eBook Packages Computer Science
- Print ISBN 978-1-4471-6749-5
- Online ISBN 978-1-4471-6750-1
- Series Print ISSN 1868-0941
- Series Online ISSN 1868-095X
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