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Author: Reza Zafarani Publisher: Cambridge University Press ISBN: 1107018854 Category : Computers Languages : en Pages : 337
Book Description
Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.
Author: Reza Zafarani Publisher: Cambridge University Press ISBN: 1107018854 Category : Computers Languages : en Pages : 337
Book Description
Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.
Author: Lam Thuy Vo Publisher: No Starch Press ISBN: 1593279167 Category : Computers Languages : en Pages : 210
Book Description
BuzzFeed News Senior Reporter Lam Thuy Vo explains how to mine, process, and analyze data from the social web in meaningful ways with the Python programming language. Did fake Twitter accounts help sway a presidential election? What can Facebook and Reddit archives tell us about human behavior? In Mining Social Media, senior BuzzFeed reporter Lam Thuy Vo shows you how to use Python and key data analysis tools to find the stories buried in social media. Whether you're a professional journalist, an academic researcher, or a citizen investigator, you'll learn how to use technical tools to collect and analyze data from social media sources to build compelling, data-driven stories. Learn how to: Write Python scripts and use APIs to gather data from the social web Download data archives and dig through them for insights Inspect HTML downloaded from websites for useful content Format, aggregate, sort, and filter your collected data using Google Sheets Create data visualizations to illustrate your discoveries Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library Apply what you've learned to research topics on your own Social media is filled with thousands of hidden stories just waiting to be told. Learn to use the data-sleuthing tools that professionals use to write your own data-driven stories.
Author: Xu, Guandong Publisher: IGI Global ISBN: 1466628073 Category : Computers Languages : en Pages : 272
Book Description
Social Media Mining and Social Network Analysis: Emerging Research highlights the advancements made in social network analysis and social web mining and its influence in the fields of computer science, information systems, sociology, organization science discipline and much more. This collection of perspectives on developmental practice is useful for industrial practitioners as well as researchers and scholars.
Author: Marco Bonzanini Publisher: Packt Publishing Ltd ISBN: 1783552026 Category : Computers Languages : en Pages : 333
Book Description
Acquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data This is your one-stop solution to fetching, storing, analyzing, and visualizing social media data Who This Book Is For This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data. What You Will Learn Interact with a social media platform via their public API with Python Store social data in a convenient format for data analysis Slice and dice social data using Python tools for data science Apply text analytics techniques to understand what people are talking about on social media Apply advanced statistical and analytical techniques to produce useful insights from data Build beautiful visualizations with web technologies to explore data and present data products In Detail Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data. Style and approach This practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks.
Author: Matthew Russell Publisher: "O'Reilly Media, Inc." ISBN: 1449388345 Category : Computers Languages : en Pages : 356
Book Description
Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed. Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools. Get a straightforward synopsis of the social web landscape Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn Learn how to employ easy-to-use Python tools to slice and dice the data you collect Explore social connections in microformats with the XHTML Friends Network Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits "Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." --Jeff Hammerbacher, Chief Scientist, Cloudera "A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google
Author: Gabor Szabo Publisher: John Wiley & Sons ISBN: 1118824857 Category : Computers Languages : en Pages : 352
Book Description
Harness the power of social media to predict customer behavior and improve sales Social media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Social Media Data Mining and Analytics shows analysts how to use sophisticated techniques to mine social media data, obtaining the information they need to generate amazing results for their businesses. Social Media Data Mining and Analytics isn't just another book on the business case for social media. Rather, this book provides hands-on examples for applying state-of-the-art tools and technologies to mine social media - examples include Twitter, Wikipedia, Stack Exchange, LiveJournal, movie reviews, and other rich data sources. In it, you will learn: The four key characteristics of online services-users, social networks, actions, and content The full data discovery lifecycle-data extraction, storage, analysis, and visualization How to work with code and extract data to create solutions How to use Big Data to make accurate customer predictions How to personalize the social media experience using machine learning Using the techniques the authors detail will provide organizations the competitive advantage they need to harness the rich data available from social media platforms.
Author: Sharan Kumar Ravindran Publisher: Packt Publishing Ltd ISBN: 1784399671 Category : Computers Languages : en Pages : 248
Book Description
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.
Author: Helen Kennedy Publisher: Springer ISBN: 1137353988 Category : Social Science Languages : en Pages : 270
Book Description
In this book, Helen Kennedy argues that as social media data mining becomes more and more ordinary, as we post, mine and repeat, new data relations emerge. These new data relations are characterised by a widespread desire for numbers and the troubling consequences of this desire, and also by the possibility of doing good with data and resisting data power, by new and old concerns, and by instability and contradiction. Drawing on action research with public sector organisations, interviews with commercial social insights companies and their clients, focus groups with social media users and other research, Kennedy provides a fascinating and detailed account of living with social media data mining inside the organisations that make up the fabric of everyday life.
Author: María N. Moreno García Publisher: MDPI ISBN: 3036502467 Category : Technology & Engineering Languages : en Pages : 144
Book Description
This book presents diverse contributions related to some of the latest advances in the field of personalization and recommender systems, as well as social media and sentiment analysis. The work comprises several articles that address different problems in these areas by means of recent techniques such as deep learning, methods to analyze the structure and the dynamics of social networks, and modern language processing approaches for sentiment analysis, among others. The proposals included in the book are representative of some highly topical research directions and cover different application domains where they have been validated. These go from the recommendation of hotels, movies, music, documents, or pharmacy cross-selling to sentiment analysis in the field of telemedicine and opinion mining on news, also including the study of social capital on social media and dynamics aspects of the Twitter social network.
Author: Fattane Zarrinkalam Publisher: ISBN: 9781680837384 Category : Languages : en Pages :
Book Description
Mining user interests from user behavioral data is critical for many applications. Based on user interests, service providers like advertisers can significantly reduce service delivery costs by offering the most relevant products to their customers. The challenge of accurately and efficiently identifying user interests has been the subject of increasing attention for several years. With the emergence and growing popularity of social media, many users are extensively engaged in social media applications to express their feelings and views about a wide variety of social events/topics as they happen in real time. The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine, and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services, and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this monograph, the authors cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions, and opportunities for further work. The monograph is a valuable resource for those who have familiarity with social media mining and the basics of information retrieval (IR) techniques.
Author: Reza Zafarani
Author: Reza Zafarani
Author: Lam Thuy Vo
Author: Xu, Guandong
Author: Marco Bonzanini
Author: Matthew Russell
Author: Gabor Szabo
Author: Sharan Kumar Ravindran
Author: Helen Kennedy
Author: María N. Moreno García
Author: Fattane Zarrinkalam