Practical Text Mining with Perl

A reliable, cost-effective approach to extracting priceless business information from all sources of text Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You’ll learn how to: Identify actionable social media posts to improve customer service Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more Most companies’ data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents—until now.

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.” This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.

After providing an introduction to the Perl programming language, this helpful guide teaches computer networking using Perl. Topics discussed include ethernet network analysis, programming standard Internet protocols, and exploring mobile agent programming. * Each chapter provides a general discussion of the technologies under consideration, the support for programming the technologies as provided by Perl, and implementations of working examples * Covers Mobile Agent Technology, which is set to become one of the «next big things» on the Internet * Further information is supplied, including a listing of Web and print resources, programming exercises, and tips to expand the reader's understanding of the material

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: • A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools • Illustrations of how to use the outlined concepts in real-world situations • Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials • Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.

Covers fundamental and advanced Java database programming techniques for beginning and experienced readers This book covers the practical considerations and applications in database programming using Java NetBeans IDE, JavaServer Pages, JavaServer Faces, and Java Beans, and comes complete with authentic examples and detailed explanations. Two data-action methods are developed and presented in this important resource. With Java Persistence API and plug-in Tools, readers are directed step by step through the entire database programming development process and will be able to design and build professional data-action projects with a few lines of code in mere minutes. The second method, runtime object, allows readers to design and build more sophisticated and practical Java database applications. Advanced and updated Java database programming techniques such as Java Enterprise Edition development kits, Enterprise Java Beans, JavaServer Pages, JavaServer Faces, Java RowSet Object, and Java Updatable ResultSet are also discussed and implemented with numerous example projects. Ideal for classroom and professional training use, this text also features: A detailed introduction to NetBeans Integrated Development Environment Java web-based database programming techniques (web applications and web services) More than thirty detailed, real-life sample projects analyzed via line-by-line illustrations Problems and solutions for each chapter A wealth of supplemental material available for download from the book's ftp site, including PowerPoint slides, solution manual, JSP pages, sample image files, and sample databases Coverage of two popular database systems: SQL Server 2008 and Oracle This book provides undergraduate and graduate students as well as database programmers and software engineers with the necessary tools to handle the database programming issues in the Java NetBeans environment. To obtain instructor materials please send an email to: pressbooks@ieee.org

First title to ever present soft computing approaches and their application in data mining, along with the traditional hard-computing approaches Addresses the principles of multimedia data compression techniques (for image, video, text) and their role in data mining Discusses principles and classical algorithms on string matching and their role in data mining

The most up-to-date Visual Basic.NET programming textbook—covering both fundamentals and advanced-level programming techniques—complete with examples and solutions Visual Basic.NET (VB.NET) is an object-oriented computer programming language that can be viewed as an evolution of the classic Visual Basic (VB), which is implemented on the .NET Framework. Microsoft currently supplies two major implementations of Visual Basic: Microsoft Visual Studio (which is commercial software) and Microsoft Visual Studio Express (which is free of charge). Forgoing the large amounts of programming codes found in most database programming books, Practical Database Programming with Visual Basic.NET shows students and professionals both how to develop professional and practical database programs in a Visual Basic.NET environment by using Visual Studio.NET Data Tools and Wizards related to ADO.NET 4.0, and how to apply codes that are auto-generated by solely using Wizards. The fully updated Second Edition: Covers both fundamentals and advanced database programming techniques Introduces three popular database systems with practical examples including MS Access, SQL Server 2008, and Oracle Features more than fifty sample projects with detailed illustrations and explanations to help students understand key techniques and programming technologies Includes downloadable programming codes and exercise questions This book provides undergraduate and graduate students as well as database programmers and software engineers with the necessary tools to handle the database programming issues in the Visual Studio.NET environment.

This book covers both the fundamentals, as well as practical techniques in designing and building microcontrollers in industrial and commercial applications. Examples included in this book have been compiled, built, and tested Includes Both ARM® assembly and C codes Direct Register Access (DRA) model and the Software Driver (SD) model programming techniques and discussed If you are an instructor and adopted this book for your course, please email ieeeproposals@wiley.com to get access to the instructor files for this book.

Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies. It begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge. The information provided puts new capabilities at the hands of technology managers. Using the material present, these managers can identify and access the most valuable technology information resources (publications, patents, etc.); search, retrieve, and clean the information on topics of interest; and lower the costs and enhance the benefits of competitive technological intelligence operations.