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.
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"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
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