mining the social web data mining facebook twitter linkedin google github and more

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Mining The Social Web

Author : Matthew A. Russell
ISBN : 9781449368210
Genre : Computers
File Size : 84. 96 MB
Format : PDF, Docs
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How can you tap into the wealth of social web data to discover who’s making connections with whom, what they’re talking about, and where they’re located? With this expanded and thoroughly revised edition, you’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs. Employ the Natural Language Toolkit, NetworkX, and other scientific computing tools to mine popular social web sites Apply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language data Bootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projects Build interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkit Take advantage of more than two-dozen Twitter recipes, presented in O’Reilly’s popular "problem/solution/discussion" cookbook format The example code for this unique data science book is maintained in a public GitHub repository. It’s designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.

Mining The Social Web

Author : Matthew A. Russell
ISBN : 9781449388348
Genre : Computers
File Size : 82. 39 MB
Format : PDF
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Provides information on data analysis from a vareity of social networking sites, including Facebook, Twitter, and LinkedIn.

Web Data Mining And The Development Of Knowledge Based Decision Support Systems

Author : Sreedhar, G.
ISBN : 9781522518785
Genre : Computers
File Size : 63. 48 MB
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Websites are a central part of today’s business world; however, with the vast amount of information that constantly changes and the frequency of required updates, this can come at a high cost to modern businesses. Web Data Mining and the Development of Knowledge-Based Decision Support Systems is a key reference source on decision support systems in view of end user accessibility and identifies methods for extraction and analysis of useful information from web documents. Featuring extensive coverage across a range of relevant perspectives and topics, such as semantic web, machine learning, and expert systems, this book is ideally designed for web developers, internet users, online application developers, researchers, and faculty.

Social Media Mining With R

Author : Nathan Danneman
ISBN : 9781783281787
Genre : Computers
File Size : 65. 83 MB
Format : PDF, ePub
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A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

Web And Network Data Science

Author : Thomas W. Miller
ISBN : 9780133887648
Genre : Computers
File Size : 24. 12 MB
Format : PDF, ePub
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Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Modeling Techniques In Predictive Analytics With Python And R

Author : Thomas W. Miller
ISBN : 9780133892147
Genre : Computers
File Size : 23. 91 MB
Format : PDF, ePub, Docs
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Qualitative Research In Digital Environments

Author : Alessandro Caliandro
ISBN : 9781317282181
Genre : Business & Economics
File Size : 55. 23 MB
Format : PDF, Mobi
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This book offers a toolkit of methods and technologies to undertake qualitative research on digital spaces. This "Digital Ethnography" is unlike commonly used traditional methodological strategies, which are "retrofitted" to digital spaces. Instead, this book offers researchers a set of "digitally native" tools that are designed for virtual communities. Thanks to a broad range of cases including Louis Vuitton, YouTube and the concept of hipsterism, this text illustrates the practical applications of techniques and tools over the most popular social media environments. This book will be a valuable guide to qualitative research for marketing students, researchers and practitioners, as well as a central reference point for tutors in the growing field of Digital Sociology.

Introduction To Electronic Commerce And Social Commerce

Author : Efraim Turban
ISBN : 9783319500911
Genre : Business & Economics
File Size : 89. 43 MB
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This is a complete update of the best-selling undergraduate textbook on Electronic Commerce (EC). New to this 4th Edition is the addition of material on Social Commerce (two chapters); a new tutorial on the major EC support technologies, including cloud computing, RFID, and EDI; ten new learning outcomes; and video exercises added to most chapters. Wherever appropriate, material on Social Commerce has been added to existing chapters. Supplementary material includes an Instructor’s Manual; Test Bank questions for each chapter; Powerpoint Lecture Notes; and a Companion Website that includes EC support technologies as well as online files. The book is organized into 12 chapters grouped into 6 parts. Part 1 is an Introduction to E-Commerce and E-Marketplaces. Part 2 focuses on EC Applications, while Part 3 looks at Emerging EC Platforms, with two new chapters on Social Commerce and Enterprise Social Networks. Part 4 examines EC Support Services, and Part 5 looks at E-Commerce Strategy and Implementation. Part 6 is a collection of online tutorials on Launching Online Businesses and EC Projects, with tutorials focusing on e-CRM; EC Technology; Business Intelligence, including Data-, Text-, and Web Mining; E-Collaboration; and Competition in Cyberspace. the following="" tutorials="" are="" not="" related="" to="" any="" specific="" chapter.="" they="" cover="" the="" essentials="" ec="" technologies="" and="" provide="" a="" guide="" relevant="" resources.="" p

Social Commerce

Author : Efraim Turban
ISBN : 9783319170282
Genre : Business & Economics
File Size : 83. 23 MB
Format : PDF, ePub, Docs
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This is a multidisciplinary textbook on social commerce by leading authors of e-commerce and e-marketing textbooks, with contributions by several industry experts. It is effectively the first true textbook on this topic and can be used in one of the following ways: Textbook for a standalone elective course at the undergraduate or graduate levels (including MBA and executive MBA programs) Supplementary text in marketing, management or Information Systems disciplines Training courses in industry Support resources for researchers and practitioners in the fields of marketing, management and information management The book examines the latest trends in e-commerce, including social businesses, social networking, social collaboration, innovations and mobility. Individual chapters cover tools and platforms for social commerce; supporting theories and concepts; marketing communications; customer engagement and metrics; social shopping; social customer service and CRM contents; the social enterprise; innovative applications; strategy and performance management; and implementing social commerce systems. Each chapter also includes a real-world example as an opening case; application cases and examples; exhibits; a chapter summary; review questions and end-of-chapter exercises. The book also includes a glossary and key terms, as well as supplementary materials that include PowerPoint lecture notes, an Instructor’s Manual, a test bank and five online tutorials.

Mastering Social Media Mining With R

Author : Sharan Kumar Ravindran
ISBN : 9781784399672
Genre : Computers
File Size : 34. 38 MB
Format : PDF, Kindle
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Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.

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