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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.
- Sales Rank: #690072 in Books
- Brand: Brand: SAS Institute
- Published on: 2013-10-25
- Original language: English
- Number of items: 1
- Dimensions: 11.00" h x .77" w x 8.25" l, 1.69 pounds
- Binding: Perfect Paperback
- 340 pages
- Used Book in Good Condition
Review
"Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS is much more than a guide to real-world application of SAS Text Miner. The authors present a case-driven approach to explain the broad field of text analytics, the techniques and mathematics behind the curtain, and the advanced capabilities of the SAS toolset.
"When many people think of text analytics, they immediately think of search technologies. Search is only the beginning of the story in text analytics. Natural language processing, predictive modeling and classification, and sentiment analysis are a few examples of other techniques in text analytics that can drive tremendous business value. The authors provide a cogent, example-rich, and understandable introduction to text analytics, including one of the central techniques for analyzing high-dimensional data: the singular value decomposition.
"I would recommend this book as a foundation to anyone interested in applying text mining to real-world problems; the authors' deft presentation of theory and case-based practical application will arm you with the knowledge you need to derive real insight and value from your textual data." --Mark Pitts, MS, MAcc, Senior Vice President, Analytics, SourceHOV
"Text Mining and Analysis is a must read for anyone working in analyzing textual information. It provides a comprehensive, yet concise overview of text analytics with a focus on the business application." --Prof. Dr. Bart Baesens, Programme Coordinator, Master of Information Management,Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven
About the Author
Dr. Goutam Chakraborty 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. He has rich experience in implementing analytical solutions involving text analytics, predictive modeling, customer segmentation, database marketing, and various data mining techniques for customer relationship management applications. 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 background in analytics, databases, and business administration.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Very nice book
By Andrew Karl
This book gives a great introduction to text mining, even for those who are not using SAS. I think it's helpful to have a discussion of the high-level details of the implementation of text mining methods along with descriptions of the methods themselves. The book does a nice job highlighting the important tasks along the way from text collection to the various forms of possible analysis using the document-term matrix.
0 of 0 people found the following review helpful.
Excellent book on Text Mining and Analysis - Enjoyed every page!
By Bart Baesens
I have known professor Chakraborty for various years now and have attended various of his talks which have been very inspiring to me. One of the things which always strikes me about him, is how he manages to bridge the gap between analytics as a technical discipline, and analytics as a practitioner-focused, value adding business activity.
Throughout the many master projects which he supervised in collaboration with various Fortune 500 companies, prof. Chakraborty managed to acquire a unique experience and skill set. The book is the fruitful result of this. It reports extensively on the many case studies prof. Chakraborty conducted in the past few years.
What is unique about this book, is that it gives plenty of practical insights, examples, tips and tricks of how to use text mining and analysis in a practical setting. Prior SAS experience is useful but not strictly necessary to read the book. It is very easy to read with a good flow and structure. Whether you are business practitioner, (MBA) student, PhD researcher, or professor I would highly recommend the book to understand the full potential of text analytics.
Bart Baesens, Professor of Big Data and Analytics, KU Leuven (Belgium), www.dataminingapps.com
0 of 0 people found the following review helpful.
Solid book - lots of walk-through on "how to use" ...
By Amazon Customer
Solid book - lots of walk-through on "how to use" and could have benefitted from results. What was the pay-off? What does one get for all that effort in terms of actionable decisions, next steps, etc.
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