Text Mining and Analysis | Practical Methods, Examples, and Case Studies Using SAS

Dr. Goutam Chakraborty has a B. Tech (Honors) in mechanical engineering from the Indian Institute of Technology, Kharagpur; a PGCGM from the Indian Institute of Management, Calcutta; and an MS in statistics and a PhD in marketing from the University of Iowa. He has held managerial positions with a subsidiary of Union Carbide, USA, and with a subsidiary of British American Tobacco, UK. He is a professor of marketing at Oklahoma State University, where he has taught business analytics, marketing analytics, data mining, advanced data mining, database marketing, new product development, advanced marketing research, web-business strategy, interactive marketing, and product management for more than 20 years. Goutam has presented numerous programs and workshops to executives, educators, and research professionals in the US, Europe, Asia, and the Middle East. He has won many teaching awards, including the SAS Distinguished Professor Award from SAS Institute, and he teaches the popular SAS Business Knowledge Series course, "Text Analytics and Sentiment Mining Using SAS." Goutam's research has been published in many scholarly journals, such as the Journal of Interactive Marketing, Journal of Advertising Research, Journal of Advertising, Journal of Business Research, and Industrial Marketing Management. He coauthored the book Contemporary Database Marketing. In addition, Goutam has served on the editorial review board of the Journal of Business Research and Journal of Academy of Marketing Science. He serves as a member of the SAS Customer Analytics Advisory Board and the JMP Discovery Summit Steering Committee. Goutam has also consulted extensively on issues related to developing digital business strategy, building and managing customer relationships, product development, and management and creation of e-business models with companies such as Aetna, Mercruiser, Thrifty Rent-A-Car, Berendsen Fluid Power, Globe Life Insurance, Vanguard Realtors, Hilti, and Love's Travel Stops. He is the founder of the SAS and OSU Data Mining Certificate program as well as the SAS and OSU Business Analytics Certificate program at Oklahoma State University.

Murali Pagolu is a Business Analytics Consultant at SAS and has four years of experience using SAS software in both academic research and business applications. His focus areas include database marketing, marketing research, data mining and customer relationship management (CRM) applications, customer segmentation, and text analytics. Murali is responsible for implementing analytical solutions and developing proofs of concept for SAS customers. He has presented innovative applications of text analytics, such as mining text comments from YouTube videos and patent portfolio analysis, at past SAS Analytics conferences. He holds six SAS certification credentials covering a variety of SAS technical skills, including Data Integration, Business Intelligence, Platform Administration, and Base and Advanced Programming techniques using SAS 9. He is also a certified predictive modeler using SAS Enterprise Miner 6. Murali holds a master's degree in Management Information Systems from the Spears School of Business at Oklahoma State University, Stillwater. He was presented the "Outstanding Student" award by the Spears School of Business for his academic excellence in the SAS and OSU Data Mining Certificate program. He is a member of the Marketing Analytics and Data Mining Individual Advisory Board at Oklahoma State University. He is also a member of Beta Gamma Sigma, the international honor society for collegiate schools of business. During his time at OSU, Murali won several scholarships to attend SAS Analytics, SAS Global Forum, and SAS regional conferences. His "Eliminating Response Style Segments in Survey Data via Double Standardization before Clustering" poster was selected as one of the top-5 posters in the student poster competition at the M2010 Data Mining Conference. He is part of a team that won an honorable mention award at the M2010 Data mining Conference and second place at the A2011 Analytics Conference for annual data mining shootout competitions. He received the SAS Student Ambassador award at SAS Global Forum 2011 for his paper on standardization techniques to eliminate response style segments in survey data. Prior to his graduate studies in the United States, he worked as an IT consultant at a software services firm in India for more than four years. During that time, he was responsible for maintaining and supporting straight-through processing (STP) applications for a global financial services firm. He also holds a bachelor's degree in Electrical and Electronics Engineering from Osmania University, India.

Satish Garla is an Analytical Consultant in Risk Practice at SAS. He has extensive experience in risk modeling for healthcare, predictive modeling, text analytics, and SAS programming. He has a distinguished academic background in analytics, databases, and business administration. Satish holds a master's degree in Management Information Systems at Oklahoma State University and has completed the SAS and OSU Data Mining Certificate program. He has three years of professional experience as an Oracle CRM Consultant, and he is a SAS Certified Advanced Programmer for SAS 9 and a Certified Predictive Modeler using SAS Enterprise Miner 6.1. Satish's research in health risk analytics, text analytics, market segmentation, and social media analytics has been presented at SAS Global Forum, JMP Discovery Summit, and other SAS regional conferences. He has developed a SAS macro to collect and analyze customized tweets from Twitter for which he was awarded SAS Student Ambassador for 2011 by SAS. He is also part of a team that won second place in the Data Mining shootout competition 2011 organized by SAS and Central Michigan University. Satish's paper on "Analyzing Sentiments Expressed about Wal-Mart and Sam's Club in Tweets" won first prize in the Walmart analytics competition and SAS Global Forum 2012 social media section. He also contributed a case study on sentiment analysis for the book, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications.