Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

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 presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. 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 dramatically. Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com Glossary of text mining terms provided in the appendix Front Cover Practical Text Mining and Statistical Analysis for Non-structured Text 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 Authors Part 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 TEXT SUMMARIZING 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 Methodology PREAMBLE 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 ANALYSIS SUMMARY Tutorial BB - Mining Twitter for Airline Consumer Sentiment INTRODUCTION WHAT IS R? LOADING DATA INTO R THE TWITTER PACKAGE EXTRACTING TEXT FROM TWEETS THE PLYR PACKAGE ESTIMATING SENTIMENT LOADING THE OPINION LEXICON IMPLEMENTING OUR SENTIMENT SCORING ALGORITHM ALGORITHM SANITY CHECK DATA.FRAMES HOLD TABULAR DATA SCORING THE TWEETS REPEAT FOR EACH AIRLINE COMPARE THE SCORE DISTRIBUTIONS IGNORE THE MIDDLE COMPARE WITH ACSI’S CUSTOMER SATISFACTION INDEX SCRAPE THE ACSI WEBSITE COMPARE TWITTER RESULTS WITH ACSI SCORES GRAPH THE RESULTS NOTES AND ACKNOWLEDGMENTS References Tutorial A - Using STATISTICA Text Miner to Monitor and Predict Success of Marketing Campaigns Based on Social Media Data INTRODUCTION THE KEY ISSUE STEP 1: COLLECTING DATA STEP 2: MONITORING THE SITUATION STEP 3: CREATING PREDICTIVE MODELS STEP 4: PERFORMING A “WHAT-IF” ANALYSIS OF THE MARKETING CAMPAIGNS STEP 5: PERFORMING SENTIMENT ANALYSIS SUMMARY Tutorial B - Text Mining Improves Model Performance in Predicting Airplane Flight Accident Outcome INTRODUCTION THE DATA TEXT MINING THE DATA TEXT MINING RESULTS DATA PREPARATION USING TEXT MINING RESULTS TO BUILD PREDICTIVE MODELS TUTORIAL C - Insurance Industry: Text Analytics Adds “Lift” to Predictive Models with STATISTICA Text and Data Miner INTRODUCTION DATA DESCRIPTION PART A: COMPARING THE LIFT OF PREDICTIVE MODELS WITH AND WITHOUT TEXT MINING BOOSTED TREES (WITHOUT TEXT MATERIAL) BOOSTED TREES ADDING THE TEXT MINING VARIABLES HOW TO MERGE GRAPHS PART B: ENTERPRISE DEPLOYMENT SUMMARY Tutorial D - Analysis of Survey Data for Establishing the “Best Medical Survey Instrument” Using Text Mining INTRODUCTION THE ANALYSIS SUMMARY Tutorial E - Analysis of Survey Data for Establishing “Best Medical Survey Instrument” Using Text Mining: Central Asian (Russian Language) Study Tutorial 2: Potential for Constructing Instruments That Have Increased Validity* INTRODUCTION THE ANALYSIS SUMMARY Tutorial F - Using eBay Text for Predicting ATLAS Instrumental Learning INTRODUCTION EXAMINING THE DATA BY TYPES SUMMARY Reference Tutorial G - Text Mining for Patterns in Children’s Sleep Disorders Using STATISTICA Text Miner SETTING UP THE ANALYSIS REVIEWING RESULTS SUMMARY Tutorial H - Extracting Knowledge from Published Literature Using RapidMiner INTRODUCTION MOTIVATION A BRIEF INTRODUCTION TO RAPIDMINER TEXT ANALYTICS IN RAPIDMINER STARTING A NEW PROCESS SUMMARY Reference Tutorial I - Text Mining Speech Samples: Can the Speech of Individuals Diagnosed with Schizophrenia Differentiate Them from Unaffected Controls? INTRODUCTION OBJECTIVES CASE STUDY: THE STEPS USED TO PREPARE THE DATA RESULTS AND ANALYSIS SUMMARY References Tutorial J - Text Mining Using STMTM, CART®, and TreeNet® from Salford Systems: Analysis of 16,000 iPod Auction on eBay INSTALLING THE SALFORD TEXT MINER COMMENTS ON THE CHALLENGE Tutorial K - Predicting Micro Lending Loan Defaults Using SAS® Text Miner INTRODUCTION ABOUT SAS® TEXT MINER PROJECT OVERVIEW PREPARING THE DATA AND SETTING UP THE DIAGRAM CREATING A NEW PROJECT REGISTERING THE TABLE CREATING A NEW DIAGRAM TEXT FILTER NODE TEXT TOPIC NODE CREATING THE TEXT MINING FLOW INSERTING THE DATA UNDERSTANDING TEXT PARSING SYNONYMS AND MULTITERM WORDS DEFINING TOPICS OTHER USES OF THE INTERACTIVE TOPIC VIEWER MAKING THE PREDICTIVE MODEL FINAL RESULTS VIEWING THE REPORTS TEXT ONLY DECISION TREE ALL VARIABLE TEXT AND RELATIONAL CONCLUSION Tutorial l - Opera Lyrics: Text Analytics Compared by the Composer and the Century of Composition—Wagner versus Puccini Tutorial M - Tutorial M - CASE STUDY: Sentiment-Based Text Analytics to Better Predict Customer Satisfaction and Net Promoter® Score Using IBM®SPSS® Modeler INTRODUCTION BUSINESS OBJECTIVES CASE STUDY CREATING NEW CATEGORIES AND ADDING MISSING DESCRIPTORS RESULTS AND ANALYSIS SUMMARY References TUTORIAL N - CASE STUDY: Detecting Deception in Text with Freely Available Text and Data Mining Tools INTRODUCTION GENERAL ARCHITECTURE FOR TEST ENGINEERING LINGUISTIC INQUIRY AND WORD COUNT WORKING WITH GENERAL ARCHITECTURE FOR TEST ENGINEERING AND LINGUISTIC INQUIRY AND WORD COUNT OUTPUT SUMMARY References Tutorial O - Predicting Box Office Success of Motion Pictures with Text Mining INTRODUCTION ANALYSIS SUMMARY References Tutorial P - A Hands-On Tutorial of Text Mining in PASW: Clustering and Sentiment Analysis Using Tweets from Twitter INTRODUCTION OBJECTIVE CASE STUDY CATEGORIZATION CLUSTER ANALYSIS ANALYZING TEXT LINKS ADDITIONAL SETTINGS SUMMARY Tutorial Q - A Hands-On Tutorial on Text Mining in SAS®: Analysis of Customer Comments for Clustering and Predictive Modeling INTRODUCTION OBJECTIVE CASE STUDY SUMMARY References Tutorial R Scoring Retention and Success of Incoming College Freshmen Using Text Analytics Introduction Part I. Predictive Modeling Using Only the Numeric Variables Part II. Text Mining and Text Variables’ Word Frequencies and Concepts Tutorial S - Searching for Relationships in Product Recall Data from the Consumer Product Safety Commission with STATISTICA Text Miner SPECIFYING THE ANALYSIS REVIEWING THE RESULTS Tutorial T - Potential Problems That Can Arise in Text Mining: Example Using NALL Aviation Data INTRODUCTION SPELLING ERRORS EXAMPLE: FINDING SPELLING ERRORS IN TEXT MINER COMBINE WORDS MISSPELLINGS AS SYNONYMS UNEXPECTED TERMS EXAMPLE: FINDING UNEXPECTED TERMS DIFFERENT FILE TYPES SUMMARY Tutorial U - Exploring the Unabomber Manifesto Using Text Miner INTRODUCTION SUMMARIZING THE TEXT SEARCHING FOR TRENDS WITH PRONOUNS References Tutorial V - Text Mining PubMed: Extracting Publications on Genes and Genetic Markers Associated with Migraine Headaches from PubMed Abstracts Tutorial W - CASE STUDY: The Problem with the Use of Medical Abbreviations by Physicians and Health Care Providers THE PRESENT PROBLEM IN THE USE OF MEDICAL ABBREVIATIONS BY PHYSICIANS AND HEALTH CARE PROVIDERS TJC (JCAHO) “DO NOT USE” ABBREVIATIONS ADDITIONAL ABBREVIATIONS, ACRONYMS, AND SYMBOLS USING THE “TEXT MINING PROJECT” FORMAT OF STATISTICA TEXT MINER USING TEXTMINER3.DBS CONCLUSION INTERVENTION TRAINING NEEDED References Tutorial X - Classifying Documents with Respect to “Earnings” and Then Making a Predictive Model for the Target Variable Using Decision Trees, MARSplines, Naïve Bayes Classifier, and K-Nearest Neighbors with STATISTICA Text Miner INTRODUCTION: AUTOMATIC TEXT CLASSIFICATION DATA FILE WITH FILE REFERENCES SPECIFYING THE ANALYSIS PROCESSING THE DATA ANALYSIS SAVING THE EXTRACTED WORD FREQUENCIES TO THE INPUT FILE INITIAL FEATURE SELECTION GENERAL CLASSIFICATION AND REGRESSION TREES K-NEAREST NEIGHBORS MODELING CONCLUSION Reference Tutorial Y - CASE STUDY: Predicting Exposure of Social Messages: The Bin Laden Live Tweeter INTRODUCTION ANALYSIS SUMMARY Tutorial Z - The InFLUence Model: Web Crawling, Text Mining, and Predictive Analysis with 2010–2011 Influenza Guidelines— CDC, IDSA, WHO, and FMC ABSTRACT WEB CRAWLING AND TEXT MINING OF CDC DOCUMENTS ON FLU FEATURE SELECTION MARSPLINES INTERACTIVE MODULE MODELING BOOSTED TREES NAÏVE BAYES MODELING K-NEAREST NEIGHBORS Part 3 - Advanced Topics Chapter 7 - Text Classification and Categorization PREAMBLE INTRODUCTION DEFINING A CLASSIFICATION PROBLEM FEATURE CREATION TEXT CLASSIFICATION ALGORITHMS COMBINING EVIDENCE EVALUATING TEXT CLASSIFIERS HIERARCHICAL TEXT CLASSIFICATION TEXT CLASSIFICATION APPLICATIONS SUMMARY POSTSCRIPT References Chapter 8 - Prediction in Text Mining: The Data Mining Algorithms of Predictive Analytics PREAMBLE INTRODUCTION THE POWER OF SIMPLE DESCRIPTIVE STATISTICS, GRAPHICS, AND VISUAL TEXT MINING VISUAL DATA MINING PREDICTIVE MODELING (SUPERVISED LEARNING) STATISTICAL MODELS VERSUS GENERAL PREDICTIVE MODELING CLUSTERING (UNSUPERVISED LEARNING) SINGULAR VALUE DECOMPOSITION, PRINCIPAL COMPONENTS ANALYSIS, AND DIMENSION REDUCTION ASSOCIATION AND LINK ANALYSIS SUMMARY POSTSCRIPT References Chapter 9 - Entity Extraction PREAMBLE INTRODUCTION TEXT FEATURES FOR ENTITY EXTRACTION STRATEGIES FOR ENTITY EXTRACTION CHOOSING AN ENTITY EXTRACTION APPROACH EVALUATING ENTITY EXTRACTION SUMMARY POSTSCRIPT References Chapter 10 - Feature Selection and Dimensionality Reduction PREAMBLE INTRODUCTION FEATURE SELECTION FEATURE SELECTION APPROACHES DIMENSIONALITY REDUCTION LINEAR DIMENSIONALITY REDUCTION APPROACHES POSTSCRIPT References Chapter 11 - Singular Value Decomposition in Text Mining PREAMBLE INTRODUCTION REDUNDANCY IN TEXT DIMENSIONS OF MEANING: LATENT SEMANTIC INDEXING THE MATH OF SINGULAR VALUE DECOMPOSITION GRAPHICAL REPRESENTATIONS AND SIMPLE EXAMPLES SINGULAR VALUE DECOMPOSITION IN EQUATION FORM SINGULAR VALUE DECOMPOSITION AND PRINCIPAL COMPONENTS ANALYSIS EIGENVALUES SOME PRACTICAL CONSIDERATIONS EXTRACTING DIMENSIONS SUBJECTIVE METHODS: REVIEWING GRAPHS ANALYTICAL METHODS: BUILDING MODELS FOR DIMENSIONS USEFUL ANALYSES BASED ON SINGULAR VALUE DECOMPOSITION SCORES CLUSTER ANALYSIS PREDICTIVE MODELING WHEN SVD IS NOT USEFUL SUMMARY POSTSCRIPT References Chapter 12 - Web Analytics and Web Mining PREAMBLE WEB ANALYTICS THE VALUE OF WEB ANALYTICS THE FUTURE OF WEB ANALYTICS AND WEB MINING POSTSCRIPT References Chapter 13 - Clustering Words and Documents PREAMBLE INTRODUCTION CLUSTERING ALGORITHMS CLUSTERING DOCUMENTS CLUSTERING WORDS CLUSTER VISUALIZATION SUMMARY POSTSCRIPT References Chapter 14 - Leveraging Text Mining in Property and Casualty Insurance PREAMBLE INTRODUCTION PROPERTY AND CASUALTY INSURANCE AS A BUSINESS ANALYTICS OPPORTUNITIES IN THE INSURANCE LIFE CYCLE DRIVING BUSINESS VALUE USING TEXT MINING SUMMARY POSTSCRIPT References Chapter 15 - Focused Web Crawling PREAMBLE INTRODUCTION THE FOCUSED CRAWLING PROCESS THE OPPORTUNITIES AND CHALLENGES OF MINING THE WEB TOPIC HIERARCHIES FOR FOCUSED CRAWLING TRAINING THE DOCUMENT CLASSIFIER CAPTURING USER FEEDBACK SUMMARY POSTSCRIPT References Chapter 16 - The Future of Text and Web Analytics TEXT ANALYTICS AND TEXT MINING THE PROS AND CONS OF COMMERCIAL SOFTWARE VERSUS OPEN SOURCE SOFTWARE THE FUTURE OF TEXT MINING THE FUTURE OF WEB ANALYTICS MULTISESSION PATHING INTEGRATION OF WEB ANALYTICS WITH STANDARD BI TOOLS ATTRIBUTION ACROSS MULTIPLE SESSIONS THE FUTURE: WHAT DOES IT HOLD? NEW AREAS THAT MAY USE TEXT ANALYTICS IN THE FUTURE IBM WATSON SUMMARY References IBM-Watson References Chapter 17 - Summary WHY ARE YOU READING THIS CHAPTER? OUR PERSPECTIVE FOR APPLYING TEXT MINING TECHNOLOGY PART I: BACKGROUND AND THEORY WHAT IS TEXT MINING? WHAT TOOLS CAN I USE? PART II: THE TEXT MINING LABORATORY—28 TUTORIALS PART III: ADVANCED TOPICS OUTLINES OF CHAPTER 7–15 Glossary References Index How to Use the Data Sets and the Text Mining Software on the DVD or on Links for Practical Text Mining I. DATA SETS FOR THE TUTORIALS IN PRACTICAL TEXT MINING II. SAS TEXT MINER SOFTWARE III. SALFORD SYSTEMS SOFTWARE, INCLUDING A NEW TEXT MINER MODULE MADE FOR THIS BOOK (30-DAY FREE T ... IV. STATISTICA TEXT MINER SOFTWARE (30-DAY FREE TRIAL ON THE DVD THAT ACCOMPANIES THIS BOOK)

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 presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities.

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 dramatically.

-Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible

-Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com

-Glossary of text mining terms provided in the appendix

-CD included

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 dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. -Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible -Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com -Glossary of text mining terms provided in the appendix "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 presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. 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 dramatically."-- Fourni par l'éditeur "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 dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities"-- Provided by publisher Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, (2012) 10pp. 978-0-12-386979-1