Practical text mining and statistical analysis for non-structured text data applications

• Front Cover; Practical Text Miningand Statistical Analysisfor Non-structuredText Data Applications; Copyright; Dedication; Contents; Endorsements for Practical Text Mining & Statistical Analysis for Non-structured Text Data Applications; Foreword 1; Foreword 2; Foreword 3; Acknowledgments; Preface; About the Authors; Introduction; BUILDING THE WORKSHOP MANUAL; COMMUNICATION; THE STRUCTURE OF THIS BOOK; PART I: BASIC TEXT MINING PRINCIPLES; PART II: TUTORIALS; PART III: ADVANCED TOPICS; TUTORIALS; WHY DID WE WRITE THIS BOOK?; WHAT ARE THE BENEFITS OF TEXT MINING?; BLAST OFF!; References. 
• List of Tutorials by Guest AuthorsPart 1 - Basic Text Mining Principles; Chapter 1 - The History of Text Mining; PREAMBLE; THE ROOTS OF TEXT MINING: INFORMATION RETRIEVAL, EXTRACTION, AND SUMMARIZATION; INFORMATION EXTRACTION AND MODERN TEXT MINING; MAJOR INNOVATIONS IN TEXT MINING SINCE 2000; THE DEVELOPMENT OF ENABLING TECHNOLOGY IN TEXT MINING; EMERGING APPLICATIONS IN TEXT MINING; SENTIMENT ANALYSIS AND OPINION MINING; IBM'S WATSON: AN "INTELLIGENT" TEXT MINING MACHINE?; WHAT'S NEXT?; POSTSCRIPT; References; Chapter 2 - The Seven Practice Areas of Text Analytics; PREAMBLE. 
• WHAT IS TEXT MINING?THE SEVEN PRACTICE AREAS OF TEXT ANALYTICS; FIVE QUESTIONS FOR FINDING THE RIGHT PRACTICE AREA; THE SEVEN PRACTICE AREAS IN DEPTH; INTERACTIONS BETWEEN THE PRACTICE AREAS; SCOPE OF THIS BOOK; SUMMARY; POSTSCRIPT; References; Chapter 3 - Conceptual Foundations of Text Mining and Preprocessing Steps; PREAMBLE; INTRODUCTION; SYNTAX VERSUS SEMANTICS; THE GENERALIZED VECTOR-SPACE MODEL; PREPROCESSING TEXT; CREATING VECTORS FROM PROCESSED TEXT; SUMMARY; POSTSCRIPT; Reference; Chapter 4 - Applications and Use Cases for Text Mining; PREAMBLE; WHY IS TEXT MINING USEFUL?. 
• EXTRACTING "MEANING" FROM UNSTRUCTURED TEXTSUMMARIZING TEXT; COMMON APPROACHES TO EXTRACTING MEANING; EXTRACTING INFORMATION THROUGH STATISTICAL NATURAL LANGUAGE PROCESSING; STATISTICAL ANALYSIS OF DIMENSIONS OF MEANING; BEYOND STATISTICAL ANALYSIS OF WORD FREQUENCIES: PARSING AND ANALYZING SYNTAX; REVIEW; IMPROVING ACCURACY IN PREDICTIVE MODELING; USING STATISTICAL NATURAL LANGUAGE PROCESSING TO IMPROVE LIFT; USING DICTIONARIES TO IMPROVE PREDICTION; IDENTIFYING SIMILARITY AND RELEVANCE BY SEARCHING; PART OF SPEECH TAGGING AND ENTITY EXTRACTION; SUMMARY; POSTSCRIPT; References. 
• Chapter 5 - Text Mining MethodologyPREAMBLE; TEXT MINING APPLICATIONS; CROSS-INDUSTRY STANDARD PROCESS FOR DATA MINING (CRISP-DM); EXAMPLE 1: AN EXPLORATORY LITERATURE SURVEY USING TEXT MINING; POSTSCRIPT; References; Chapter 6 - Three Common Text Mining Software Tools; PREAMBLE; INTRODUCTION; IBM SPSS MODELER PREMIUM; SAS TEXT MINER; ABOUT THE SCENARIOS IN THIS SAS SECTION; TIPS FOR TEXT MINING; STATISTICA TEXT MINER; SUMMARY: STATISTICA TEXT MINER; POSTSCRIPT; Part 2 - Introduction to the Tutorial and Case Study Section of This Book; Reference. 
• Tutorial AA - CASE STUDY: Using the Social Share of Voice to Predict Events That Are about to Happen. 

• The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase d

Ämnesord

Data mining. 

Genre

Electronic books.  (LCSH)

Publikations- och innehållstyp

QA76.9.D343 P73 2012 (LCC)

006.3/12 (DDC)

Pud (kssb/8 (machine generated))