Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

Fundamentals of Predictive Text Mining (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer London
• Seitenzahl: 239
• Erscheinungstermin: 7. September 2015
• Englisch
• ISBN-13: 9781447167501
• Artikelnr.: 43789492

Autorenporträt

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.

Inhaltsangabe

Overview of Text Mining.- From Textual Information to Numerical Vectors.- Using Text for Prediction.- Information Retrieval and Text Mining.- Finding Structure in a Document Collection.- Looking for Information in Documents.- Data Sources for Prediction: Databases, Hybrid Data and the Web.- Case Studies.- Emerging Directions.

Rezensionen

"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)

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)