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

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

1st Edition - January 11, 2012
This is the Latest Edition
  • Authors: Gary Miner, John Elder, Andrew Fast, Thomas Hill, Robert Nisbet, Dursun Delen
  • eBook ISBN: 9780123870117

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Description

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.

Key Features

  • 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

Readership

In one comprehensive resource, this book provides complete coverage of statistical analysis and text mining applications to aid professionals, practitioners, researchers and upper level undergraduate and graduate students for those who need to learn how to rapidly do text mining to incorporate into information distillation and thus good decision making.

Table of Contents

  • Dedication

    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 I: 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 II: Introduction to the Tutorial and Case Study Section of This Book

    Introduction

    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 Auctions 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. 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 III: 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

    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 Trial Available)

    IV STATISTICA Text Miner Software (30-day free trial on the DVD that accompanies this book)

Product details

  • No. of pages: 1000
  • Language: English
  • Copyright: © Academic Press 2012
  • Published: January 11, 2012
  • Imprint: Academic Press
  • eBook ISBN: 9780123870117
  • About the Authors

    Gary Miner

    Gary Miner
    Dr. Gary Miner received a B.S. from Hamline University, St. Paul, MN, with biology, chemistry, and education majors; an M.S. in zoology and population genetics from the University of Wyoming; and a Ph.D. in biochemical genetics from the University of Kansas as the recipient of a NASA pre-doctoral fellowship. He pursued additional National Institutes of Health postdoctoral studies at the U of Minnesota and U of Iowa eventually becoming immersed in the study of affective disorders and Alzheimer's disease. In 1985, he and his wife, Dr. Linda Winters-Miner, founded the Familial Alzheimer's Disease Research Foundation, which became a leading force in organizing both local and international scientific meetings, bringing together all the leaders in the field of genetics of Alzheimer's from several countries, resulting in the first major book on the genetics of Alzheimer’s disease. In the mid-1990s, Dr. Miner turned his data analysis interests to the business world, joining the team at StatSoft and deciding to specialize in data mining. He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs. Robert A. Nisbet and John Elder), which received the 2009 American Publishers Award for Professional and Scholarly Excellence (PROSE). Their follow-up collaboration, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, also received a PROSE award in February of 2013. Gary was also co-author of “Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Overall, Dr. Miner’s career has focused on medicine and health issues, and the use of data analytics (statistics and predictive analytics) in analyzing medical data to decipher fact from fiction. Gary has also served as Merit Reviewer for PCORI (Patient Centered Outcomes Research Institute) that awards grants for predictive analytics research into the comparative effectiveness and heterogeneous treatment effects of medical interventions including drugs among different genetic groups of patients; additionally he teaches on-line classes in ‘Introduction to Predictive Analytics’, ‘Text Analytics’, ‘Risk Analytics’, and ‘Healthcare Predictive Analytics’ for the University of California-Irvine. Recently, until ‘official retirement’ 18 months ago, he spent most of his time in his primary role as Senior Analyst-Healthcare Applications Specialist for Dell | Information Management Group, Dell Software (through Dell’s acquisition of StatSoft (www.StatSoft.com) in April 2014). Currently Gary is working on two new short popular books on ‘Healthcare Solutions for the USA’ and ‘Patient-Doctor Genomics Stories’.

    Affiliations and Expertise

    Retired, currently Board Member for and teaching with the University of California, Irvine Predictive Analytics Certificate Program, USA

    John Elder

    John Elder
    Dr. John Elder heads the United States’ leading data mining consulting team, with offices in Charlottesville, Virginia; Washington, D.C.; and Baltimore, Maryland (www.datamininglab.com). Founded in 1995, Elder Research, Inc. focuses on investment, commercial, and security applications of advanced analytics, including text mining, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, market sector timing, and fraud detection. John obtained a B.S. and an M.E.E. in electrical engineering from Rice University and a Ph.D. in systems engineering from the University of Virginia, where he’s an adjunct professor teaching Optimization or Data Mining. Prior to 16 years at ERI, he spent five years in aerospace defense consulting, four years heading research at an investment management firm, and two years in Rice's Computational & Applied Mathematics Department.

    Affiliations and Expertise

    Elder Research, Inc. and the University of Virginia, Charlottesville, USA

    Andrew Fast

    Andrew Fast
    Dr. Andrew Fast leads research in text mining and social network analysis at Elder Research. Dr. Fast graduated magna cum laude from Bethel University and earned an M.S. and a Ph.D. in computer science from the University of Massachusetts Amherst. There, his research focused on causal data mining and mining complex relational data such as social networks. At ERI, Andrew leads the development of new tools and algorithms for data and text mining for applications of capabilities assessment, fraud detection, and national security. Dr. Fast has published on an array of applications, including detecting securities fraud using the social network among brokers and understanding the structure of criminal and violent groups. Other publications cover modeling peer-to-peer music file sharing networks, understanding how collective classification works, and predicting playoff success of NFL head coaches (work featured on ESPN.com).

    Thomas Hill

    Thomas Hill
    Thomas Hill received his Vordiplom in psychology from Kiel University in Germany and earned an M.S. in industrial psychology and a Ph.D. in psychology and quantitative methods from the University of Kansas. He was associate professor (and then research professor) at the University of Tulsa from 1984 to 2009, where he taught data analysis and data mining courses. He also has been vice president for Research and Development and then Analytic Solutions at StatSoft Inc., where he has been involved for over 20 years in the development of data analysis, data and text mining algorithms, and the delivery of analytic solutions. Dr. Hill joined Dell through Dell’s acquisition of StatSoft in April 2014, and he is currently the Executive Director for Analytics at Dell’s Information Management Group.

    Dr. Hill has received numerous academic grants and awards from the National Science Foundation, the National Institute of Health, the Center for Innovation Management, the Electric Power Research Institute, and other institutions. He has completed diverse consulting projects with companies from practically all industries and has worked with the leading financial services, insurance, manufacturing, pharmaceutical, retailing, and other companies in the United States and internationally on identifying and refining effective data mining and predictive modeling solutions for diverse applications. Dr. Hill has published widely on innovative applications for data mining and predictive analytics. He is the author (with Paul Lewicki, 2005) of Statistics: Methods and Applications, the Electronic Statistics Textbook (a popular on-line resource on statistics and data mining), a co-author of Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications (2012); he is also a contributing author to the popular Handbook of Statistical Analysis and Data Mining Applications (2009).

    Affiliations and Expertise

    StatSoft, Inc., Tulsa, OK, USA

    Robert Nisbet

    Robert Nisbet
    Dr. Robert Nisbet was trained initially in Ecology and Ecosystems Analysis. He has over 30 years of experience in complex systems analysis and modeling, most recently as a Researcher (University of California, Santa Barbara). In business, he pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications, Insurance, Banking, and Credit industries. In addition to data mining, he has expertise in data warehousing technology for Extract, Transform, and Load (ETL) operations, Business Intelligence reporting, and data quality analyses. He is lead author of the “Handbook of Statistical Analysis & Data Mining Applications” (Academic Press, 2009), and a co-author of "Practical Text Mining" (Academic Press, 2012), and co-author of “Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Currently, he serves as an Instructor in the University of California, Irvine Predictive Analytics Certificate Program, teaching online and on-campus courses in Effective Data preparation, and Applications of Predictive Analytics. Additionally Bob is in the last stages of writing another book on ‘Data Preparation for Predictive Analytic Modeling.

    Affiliations and Expertise

    Researcher, University of California, Irvine Predictive Analytics Certification Program, University of California, Santa Barbara, USA

    Dursun Delen

    Dursun Delen
    Dr. Dursun Delen is the William S. Spears Chair in Business Administration and Associate Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). He received his Ph.D. in industrial engineering and management from OSU in 1997. Prior to his appointment as an assistant professor at OSU in 2001, he worked for a privately owned research and consultancy company, Knowledge Based Systems Inc., in College Station, Texas, as a research scientist for five years, during which he led a number of decision support and other information systems-related research projects funded by federal agencies, including DoD, NASA, NIST and DOE.

    Latest reviews

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    • ChristopherSarrico Wed Dec 05 2018

      Great book

      As a masters student at Villanova, I found this book to be very insightful.