Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS
- 5h 31m
- Goutam Chakraborty, Murali Pagolu, Satish Garla
- SAS Institute
- 2013
Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media.
However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS.
This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries.
Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis.
About the Authors
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.
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 currently holds six SAS certification credentials.
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 is a SAS Certified Advanced Programmer for SAS 9 and a Certified Predictive Modeler using SAS Enterprise Miner 6.1.
In this Book
-
Introduction to Text Analytics
-
Information Extraction Using SAS Crawler
-
Importing Textual Data into SAS Text Miner
-
Parsing and Extracting Features
-
Data Transformation
-
Clustering and Topic Extraction
-
Content Management
-
Sentiment Analysis
-
Case Studies
-
Text Mining SUGI/SAS Global Forum Paper Abstracts to Reveal Trends
-
Automatic Detection of Section Membership for SAS Conference Paper Abstract Submissions
-
Features-based Sentiment Analysis of Customer Reviews
-
Exploring Injury Data for Root Causal and Association Analysis
-
Enhancing Predictive Models Using Textual Data
-
Opinion Mining of Professional Drivers' Feedback
-
Information Organization and Access of Enron Emails to Help Investigation
-
Unleashing the Power of Unified Text Analytics to Categorize Call Center Data
-
Evaluating Health Provider Service Performance Using Textual Responses
