Fundamentals of Predictive Text Mining

Nonfiction, Computers, Database Management, Advanced Computing, General Computing
Cover of the book Fundamentals of Predictive Text Mining by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Springer London
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Author: Sholom M. Weiss, Nitin Indurkhya, Tong Zhang ISBN: 9781447167501
Publisher: Springer London Publication: September 7, 2015
Imprint: Springer Language: English
Author: Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
ISBN: 9781447167501
Publisher: Springer London
Publication: September 7, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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