Fundamentals of Predictive Text Mining (Texts in Computer Science), by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
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Fundamentals of Predictive Text Mining (Texts in Computer Science), by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Best Ebook Online Fundamentals of Predictive Text Mining (Texts in Computer Science), by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
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. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.
Fundamentals of Predictive Text Mining (Texts in Computer Science), by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang- Amazon Sales Rank: #1606326 in Books
- Published on: 2015-10-13
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .63" w x 6.14" l, 1.18 pounds
- Binding: Hardcover
- 239 pages
Review
From the reviews:
"This is a practical, up-to-date account of the various techniques for dealing intelligently with free text. It would be an invaluable resource to any advanced undergraduate student interested in information retrieval." (Patrick Oladimeji, Times Higher Education, 26 May 2011)
“This is a well-written and interesting text for information technology (IT) professionals and computer science students. It seems to address all of the topics related to the fields that, when integrated, are known as knowledge engineering. … Without a doubt, the authors’ experience in the field makes this book a successful contribution to the literature that targets the interests of the IT community and beyond.” (Jolanta Mizera-Pietraszko, ACM Computing Reviews, June, 2011)
“This well-written work, which offers a unifying view of text mining through a systematic introduction to solving real-world problems. … The uniqueness of this book is the recourse to the prediction problem, which, by providing practical advice, allows for the integration of related topics. … The book is accompanied by a software implementation of the main algorithmic practices introduced. This is the icing on the cake for both beginners and expert readers … . This is the book … I have always wanted to read.” (Ernesto D’Avenzo, ACM Computing Reviews, August, 2012)
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 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.
About the Author
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.
Where to Download Fundamentals of Predictive Text Mining (Texts in Computer Science), by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Most helpful customer reviews
8 of 8 people found the following review helpful. Text Tech Text By John M. Ford 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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