Fundamentals Of Predictive Text Mining

Fundamentals of Predictive Text Mining
Publisher Springer
Release Date
Category Computers
Total Pages 239
ISBN 9781447167501
Rating 4/5 from 21 reviews
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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.

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Fundamentals of Predictive Text Mining
  • Author : Sholom M. Weiss,Nitin Indurkhya,Tong Zhang
  • Publisher : Springer
  • Release Date : 2015-09-07

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

GET BOOK
Fundamentals of Predictive Text Mining
  • Author : Sholom M. Weiss,Nitin Indurkhya,Tong Zhang
  • Publisher : Springer
  • Release Date : 2010-11-08

One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining – the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents. Developed from the authors’ highly successful

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Fundamentals of Predictive Analytics with JMP  Second Edition
  • Author : Ron Klimberg,B. D. McCullough
  • Publisher : SAS Institute
  • Release Date : 2017-12-19

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on

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Text Mining
  • Author : Sholom M. Weiss,Nitin Indurkhya,Tong Zhang,Fred Damerau
  • Publisher : Springer Science & Business Media
  • Release Date : 2010-01-08

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information.

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Text Mining with R
  • Author : Julia Silge,David Robinson
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2017-06-12

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind

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Text Mining and Analysis
  • Author : Dr. Goutam Chakraborty,Murali Pagolu,Satish Garla
  • Publisher : SAS Institute
  • Release Date : 2014-11-22

Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics.

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Practical Text Mining with Perl
  • Author : Roger Bilisoly
  • Publisher : John Wiley & Sons
  • Release Date : 2011-09-20

Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.perl.org). It covers mining ideas from several perspectives--statistics, data mining, linguistics, and

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An Introduction to Text Mining
  • Author : Gabe Ignatow,Rada Mihalcea
  • Publisher : SAGE Publications
  • Release Date : 2017-09-22

This is the ideal introduction for students seeking to collect and analyze textual data from online sources. It covers the most critical issues that they must take into consideration at all stages of their research projects.

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Text Mining and Visualization
  • Author : Markus Hofmann,Andrew Chisholm
  • Publisher : CRC Press
  • Release Date : 2016-01-05

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a

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Fundamentals of Machine Learning for Predictive Data Analytics  second edition
  • Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
  • Publisher : MIT Press
  • Release Date : 2020-10-20

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment,

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Supervised Machine Learning for Text Analysis in R
  • Author : Emil Hvitfeldt,Julia Silge
  • Publisher : CRC Press
  • Release Date : 2021-10-22

Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance

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The Elements of Statistical Learning
  • Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-11-11

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the

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Practical Predictive Analytics and Decisioning Systems for Medicine
  • Author : Linda Miner,Pat Bolding,Joseph Hilbe,Mitchell Goldstein,Thomas Hill,Robert Nisbet,Nephi Walton,Gary Miner
  • Publisher : Academic Press
  • Release Date : 2014-09-27

With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning

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Fundamentals of Image Data Mining
  • Author : Dengsheng Zhang
  • Publisher : Springer
  • Release Date : 2019-05-13

This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is

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Practical Text Mining and Statistical Analysis for Non structured Text Data Applications
  • Author : Gary Miner,John Elder IV,Andrew Fast,Thomas Hill,Robert Nisbet,Dursun Delen
  • Publisher : Academic Press
  • Release Date : 2012-01-25

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book

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