Practical Text Analytics

Interpreting Text and Unstructured Data for Business Intelligence

Author: Steven Struhl

Publisher: Kogan Page Publishers

ISBN: 0749474025

Category: Business & Economics

Page: 272

View: 8891

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In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.
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Predictive Analytics for Marketers

Using Data Mining for Business Advantage

Author: Barry Leventhal

Publisher: Kogan Page Publishers

ISBN: 0749479949

Category: Business & Economics

Page: 272

View: 3530

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Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics has been used to successfully achieve a range of business purposes.
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Text Mining and Analysis

Practical Methods, Examples, and Case Studies Using SAS

Author: Dr. Goutam Chakraborty,Murali Pagolu,Satish Garla

Publisher: SAS Institute

ISBN: 1612907873

Category: Computers

Page: 340

View: 6814

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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. This book is part of the SAS Press program.
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Practical Predictive Analytics

Author: Ralph Winters

Publisher: Packt Publishing Ltd

ISBN: 1785880462

Category: Computers

Page: 576

View: 3690

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Make sense of your data and predict the unpredictable About This Book A unique book that centers around develop six key practical skills needed to develop and implement predictive analytics Apply the principles and techniques of predictive analytics to effectively interpret big data Solve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domains Who This Book Is For This book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues. Basic familiarity with a programming language of R is expected. What You Will Learn Master the core predictive analytics algorithm which are used today in business Learn to implement the six steps for a successful analytics project Classify the right algorithm for your requirements Use and apply predictive analytics to research problems in healthcare Implement predictive analytics to retain and acquire your customers Use text mining to understand unstructured data Develop models on your own PC or in Spark/Hadoop environments Implement predictive analytics products for customers In Detail This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data. Style and Approach This book takes a practical hands-on approach wherein the algorithms will be explained with the help of real-world use cases. It is written in a well-researched academic style which is a great mix of theoretical and practical information. Code examples are supplied for both theoretical concepts as well as for the case studies. Key references and summaries will be provided at the end of each chapter so that you can explore those topics on their own.
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The Intelligent Enterprise in the Era of Big Data

Author: Venkat Srinivasan

Publisher: John Wiley & Sons

ISBN: 1118834623

Category: Mathematics

Page: 216

View: 2798

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“ … the enterprise of today has changed … wherever you sit in this new corporation … Srinivasan gives us a practical and provocative guide for rethinking our business process … calling us all to action around rapid development of our old, hierarchical structures into flexible customer centric competitive force …. A must read for today’s business leader.” Mark Nunnelly, Executive Director, MassIT, Commonwealth of Massachusetts and Managing Director, Bain Capital “’Efficiency,’ ‘agile,’ and ‘analytics’ used to be the rage. Venkat Srinivasan explains in this provocative book why organizations can no longer afford to stop there. They need to move beyond – to be ‘intelligent.’ It isn’t just theory. He’s done it.” Bharat Anand, Henry R. Byers Professor of Business Administration, Harvard Business School In the era of big data and automation, the book presents a cutting-edge approach to how enterprises should organize and function. Striking a practical balance between theory and practice, The Intelligent Enterprise in the Era of Big Data presents the enterprise architecture that identifies the power of the emerging technology environment. Beginning with an introduction to the key challenges that enterprises face, the book systematically outlines modern enterprise architecture through a detailed discussion of the inseparable elements of such architecture: efficiency, flexibility, and intelligence. This architecture enables rapid responses to market needs by sensing important developments in internal and external environments in real time. Illustrating all of these elements in an integrated fashion, The Intelligent Enterprise in the Era of Big Data also features: • A detailed discussion on issues of time-to-market and flexibility with respect to enterprise application technology • Novel analyses illustrated through extensive real-world case studies to help readers better understand the applicability of the architecture and concepts • Various applications of natural language processing to real-world business transactions • Practical approaches for designing and building intelligent enterprises The Intelligent Enterprise in the Era of Big Data is an appropriate reference for business executives, information technology professionals, data scientists, and management consultants. The book is also an excellent supplementary textbook for upper-undergraduate and graduate-level courses in business intelligence, data mining, big data, and business process automation. “a compelling vision of the next generation of organization—the intelligent enterprise—which will leverage not just big data but also unstructured text and artificial intelligence to optimize internal processes in real time … a must-read book for CEOs and CTOs in all industries.” Ravi Ramamurti, D”Amore-McKim Distinguished Professor of International Business and Strategy, and Director, Center for Emerging Markets, Northeastern University “It is about the brave new world that narrows the gap between technology and business …. The book has practical advice from a thoughtful practitioner. Intelligent automation will be a competitive strength in the future. Will your company be ready?” Victor J. Menezes, Retired Senior Vice Chairman, Citigroup Venkat Srinivasan, PhD, is Chairman and Chief Executive Officer of RAGE Frameworks, Inc., which supports the creation of intelligent business process automation solutions and cognitive intelligence solutions for global corporations. He is an entrepreneur and holds several patents in the area of knowledge-based technology architectures. He is the author of two edited volumes and over 30 peer-reviewed publications. He has served as an associate professor in the College of Business Administration at Northeastern University.
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Information Technology Applications in Industry, Computer Engineering and Materials Science

Author: S.Z. Cai,Q.F. Zhang

Publisher: Trans Tech Publications Ltd

ISBN: 3038261513

Category: Technology & Engineering

Page: 4840

View: 3716

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Collection of selected, peer reviewed papers from the 2013 3rd International Conference on Materials Science and Information Technology (MSIT 2013), September 14-15, 2013, Nanjing, Jiangsu, China. The 958 papers are grouped as follows: Chapter 1: Materials Science and Engineering; Chapter 2: Mechatronics, Control, Testing, Measurement, Instrumentation, Detection and Monitoring Technologies; Chapter 3: Communication, Computer Engineering and Information Technologies; Chapter 4: Data Processing and Applied Computational Methods and Algorithms; Chapter 5: Power Systems and Electronics, Microelectronics and Embedded, Integrated Systems, Electric Applications; Chapter 6: Manufacturing, Industry Development and Automation.
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