LEADER 04947nam 22008055 450 001 9910299203003321 005 20230810155913.0 010 $a1-4471-6750-3 024 7 $a10.1007/978-1-4471-6750-1 035 $a(CKB)3710000000521510 035 $a(SSID)ssj0001584257 035 $a(PQKBManifestationID)16263235 035 $a(PQKBTitleCode)TC0001584257 035 $a(PQKBWorkID)14865495 035 $a(PQKB)11308608 035 $a(DE-He213)978-1-4471-6750-1 035 $a(MiAaPQ)EBC5587508 035 $a(MiAaPQ)EBC6314665 035 $a(Au-PeEL)EBL5587508 035 $a(OCoLC)920470703 035 $a(PPN)190532513 035 $a(EXLCZ)993710000000521510 100 $a20150907d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aFundamentals of Predictive Text Mining /$fby Sholom M. Weiss, Nitin Indurkhya, Tong Zhang 205 $a2nd ed. 2015. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2015. 215 $a1 online resource (XIII, 239 p. 115 illus.) 225 1 $aTexts in Computer Science,$x1868-095X 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4471-6749-X 320 $aIncludes bibliographical references and index. 327 $aOverview 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. 330 $aThis successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, and errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Topics and features: Presents a comprehensive, practical and easy-to-read introduction to text mining Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter Explores the application and utility of each method, as well as the optimum techniques for specific scenarios Provides several descriptive case studies that take readers from problem description to systems deployment in the real world Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) Contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. 410 0$aTexts in Computer Science,$x1868-095X 606 $aData mining 606 $aNatural language processing (Computer science) 606 $aInformation technology$xManagement 606 $aInformation storage and retrieval systems 606 $aDatabase management 606 $aData Mining and Knowledge Discovery 606 $aNatural Language Processing (NLP) 606 $aComputer Application in Administrative Data Processing 606 $aInformation Storage and Retrieval 606 $aDatabase Management 615 0$aData mining. 615 0$aNatural language processing (Computer science). 615 0$aInformation technology$xManagement. 615 0$aInformation storage and retrieval systems. 615 0$aDatabase management. 615 14$aData Mining and Knowledge Discovery. 615 24$aNatural Language Processing (NLP). 615 24$aComputer Application in Administrative Data Processing. 615 24$aInformation Storage and Retrieval. 615 24$aDatabase Management. 676 $a006.312 700 $aWeiss$b Sholom M$4aut$4http://id.loc.gov/vocabulary/relators/aut$056491 702 $aIndurkhya$b Nitin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZhang$b Tong$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299203003321 996 $aFundamentals of Predictive Text Mining$92514368 997 $aUNINA