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 STM™, 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.