Transform Raw Social Media Data into Real Competitive Advantage There’s real competitive advantage buried in today’s deluge of social media data. If you know how to analyze it, you can increase your relevance to customers, establishing yourself as a trusted supplier in a cutthroat environment where consumers rely more than ever on “public opinion” about your products, services, and experiences. Social Media Analytics is the complete insider’s guide for all executives and marketing analysts who want to answer mission-critical questions and maximize the business value of their social media data. Two leaders of IBM’s pioneering Social Media Analysis Initiative offer thorough and practical coverage of the entire process: identifying the right unstructured data, analyzing it, and interpreting and acting on the knowledge you gain. Their expert guidance, practical tools, and detailed examples will help you learn more from all your social media conversations, and avoid pitfalls that can lead to costly mistakes. You’ll learn how to: Focus on the questions that social media data can realistically answer Determine which information is actually useful to you—and which isn’t Cleanse data to find and remove inaccuracies Create data models that accurately represent your data and lead to more useful answers Use historical data to validate hypotheses faster, so you don’t waste time Identify trends and use them to improve predictions Drive value “on-the-fly” from real-time/ near-real-time and ad hoc analyses Analyze text, a.k.a. “data at rest” Recognize subtle interrelationships that impact business performance Improve the accuracy of your sentiment analyses Determine eminence, and distinguish “talkers” from true influencers Optimize decisions about marketing and advertising spend Whether you’re a marketer, analyst, manager, or technologist, you’ll learn how to use social media data to compete more effectively, respond more rapidly, predict more successfully…grow profits, and keep them growing.
By focusing on the relationship between social media and other technology models, such as wisdom of crowds, healthcare, fintech and blockchain, machine learning methods, and 5G, this book is able to provide applications used to understand ...
How much does it matter if we are using a certain social network or all of them? ... With training comes the selection of good data and the use of enough data so that machines can reach the highest levels of accuracy in what they do.
... interactions separate the respective roles from others. These interactions are recorded by the pairwise feature αp composed of two components as shown below. Proxemic Interaction Feature αProx.p: The proxemic interaction of two people ...
Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more Analyze and extract ...
Tap into the realm of social media and unleash the power of analytics for data-driven insights using RAbout This Book* A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and ...
Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and ...
By the end of this book, you will be well prepared for your organization’s next social data analytics project.
By the end of this book, the readers will have mastered the theories, concepts, strategies, techniques, and tools necessary to extract business value from big social media that help increase brand loyalty, generate leads, drive traffic, and ...
This edited book provides techniques which address various aspects of big data collection and analysis from social media platforms and beyond.
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.