Fundamentals of Predictive Text Mining

by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

Overview

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.

Product Details

ISBN-13:	9781447167501
Publisher:	Springer-Verlag New York, LLC
Publication date:	09/07/2015
Series:	Texts in Computer Science
Sold by:	Barnes & Noble
Format:	eBook
File size:	3 MB

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.

Table of Contents

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.

Editorial Reviews

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 Publisher