04947nam 22008055 450 991029920300332120230810155913.01-4471-6750-310.1007/978-1-4471-6750-1(CKB)3710000000521510(SSID)ssj0001584257(PQKBManifestationID)16263235(PQKBTitleCode)TC0001584257(PQKBWorkID)14865495(PQKB)11308608(DE-He213)978-1-4471-6750-1(MiAaPQ)EBC5587508(MiAaPQ)EBC6314665(Au-PeEL)EBL5587508(OCoLC)920470703(PPN)190532513(EXLCZ)99371000000052151020150907d2015 u| 0engurnn#008mamaatxtccrFundamentals of Predictive Text Mining /by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang2nd ed. 2015.London :Springer London :Imprint: Springer,2015.1 online resource (XIII, 239 p. 115 illus.)Texts in Computer Science,1868-095XBibliographic Level Mode of Issuance: Monograph1-4471-6749-X Includes bibliographical references and index.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.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.Texts in Computer Science,1868-095XData miningNatural language processing (Computer science)Information technologyManagementInformation storage and retrieval systemsDatabase managementData Mining and Knowledge DiscoveryNatural Language Processing (NLP)Computer Application in Administrative Data ProcessingInformation Storage and RetrievalDatabase ManagementData mining.Natural language processing (Computer science).Information technologyManagement.Information storage and retrieval systems.Database management.Data Mining and Knowledge Discovery.Natural Language Processing (NLP).Computer Application in Administrative Data Processing.Information Storage and Retrieval.Database Management.006.312Weiss Sholom Mauthttp://id.loc.gov/vocabulary/relators/aut56491Indurkhya Nitinauthttp://id.loc.gov/vocabulary/relators/autZhang Tongauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910299203003321Fundamentals of Predictive Text Mining2514368UNINA