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Fundamentals of Predictive Text Mining (Texts in Computer Science)
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Review
“Fundamentals of predictive text mining is a second edition that is designed as a textbook, with questions and exercises in each chapter. … The book can be used with data mining software for hands-on experience for students. … The book will be very useful for people planning to go into this field or to learn techniques that could be used in a big data environment.†(S. Srinivasan, Computing Reviews, February, 2016)
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From the Back Cover
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 miningIncludes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapterExplores the application and utility of each method, as well as the optimum techniques for specific scenariosProvides several descriptive case studies that take readers from problem description to systems deployment in the real worldDescribes 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 materialFundamentals 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.
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Product details
Series: Texts in Computer Science
Hardcover: 239 pages
Publisher: Springer; 2nd ed. 2015 edition (October 13, 2015)
Language: English
ISBN-10: 144716749X
ISBN-13: 978-1447167495
Product Dimensions:
6.1 x 0.6 x 9.2 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
1 customer review
Amazon Best Sellers Rank:
#1,128,698 in Books (See Top 100 in Books)
Sholom Weiss, Nitkin Indurkhya, and Tong Zhang are well qualified to write about text mining. Their book is "...aimed at IT professionals and managers as well as advanced undergraduate computer science students and beginning graduate students." Readers are given a password to download Data-Miner Pty. Ltd.'s riktext and tmsk programs which can be used to perform many of the analyses described in the book.The book presents the essentials of the statistical approach to modeling with text data. This approach has eclipsed linguistically-oriented natural language processing (NLP) approaches, so the book makes only passing references to them. (See Clark, Fox and Lappin's Handbook of Computational Linguistics and Natural Language Processing for a comprehensive treatment of what was left out.) The book's opening chapters introduce text processing concepts, transforming unstructured text to numerical data, statistical techniques for predictive modeling, and text mining's roots in information retrieval. Core chapters address clustering documents by similarity, finding information in documents, and working with different sources of text data. The closing chapters explore case studies in depth and highlight emerging techniques.This is a readable book with well-written chapter summaries, useful end-of-chapter exercises, and historical notes with references to additional readings. There are detailed explanations of key formulas and algorithms, but the book is structured so these can be skipped by less tech-savvy readers. Apart from an appendix, there is little direct linkage with the suggested software. But this is appropriate when the intent is to support other software alternatives. An online class taught by one of the authors encourages students to use R's tm package if they have R experience. The book supports this option reasonably well.I recommend the book either as a course text or as a self-study aid. It is the current go-to book for learning about text mining. If you work in this field, you should have it, read it, and refer others to it.
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